Kernel Planet

Matthew Garrett: Cordoomceps - replacing an Amiga's brain with Doom

Kernel Planet
3 hét óta
There's a lovely device called a pistorm, an adapter board that glues a Raspberry Pi GPIO bus to a Motorola 68000 bus. The intended use case is that you plug it into a 68000 device and then run an emulator that reads instructions from hardware (ROM or RAM) and emulates them. You're still limited by the ~7MHz bus that the hardware is running at, but you can run the instructions as fast as you want.

These days you're supposed to run a custom built OS on the Pi that just does 68000 emulation, but initially it ran Linux on the Pi and a userland 68000 emulator process. And, well, that got me thinking. The emulator takes 68000 instructions, emulates them, and then talks to the hardware to implement the effects of those instructions. What if we, well, just don't? What if we just run all of our code in Linux on an ARM core and then talk to the Amiga hardware?

We're going to ignore x86 here, because it's weird - but most hardware that wants software to be able to communicate with it maps itself into the same address space that RAM is in. You can write to a byte of RAM, or you can write to a piece of hardware that's effectively pretending to be RAM[1]. The Amiga wasn't unusual in this respect in the 80s, and to talk to the graphics hardware you speak to a special address range that gets sent to that hardware instead of to RAM. The CPU knows nothing about this. It just indicates it wants to write to an address, and then sends the data.

So, if we are the CPU, we can just indicate that we want to write to an address, and provide the data. And those addresses can correspond to the hardware. So, we can write to the RAM that belongs to the Amiga, and we can write to the hardware that isn't RAM but pretends to be. And that means we can run whatever we want on the Pi and then access Amiga hardware.

And, obviously, the thing we want to run is Doom, because that's what everyone runs in fucked up hardware situations.

Doom was Amiga kryptonite. Its entire graphical model was based on memory directly representing the contents of your display, and being able to modify that by just moving pixels around. This worked because at the time VGA displays supported having a memory layout where each pixel on your screen was represented by a byte in memory containing an 8 bit value that corresponded to a lookup table containing the RGB value for that pixel.

The Amiga was, well, not good at this. Back in the 80s, when the Amiga hardware was developed, memory was expensive. Dedicating that much RAM to the video hardware was unthinkable - the Amiga 1000 initially shipped with only 256K of RAM, and you could fill all of that with a sufficiently colourful picture. So instead of having the idea of each pixel being associated with a specific area of memory, the Amiga used bitmaps. A bitmap is an area of memory that represents the screen, but only represents one bit of the colour depth. If you have a black and white display, you only need one bitmap. If you want to display four colours, you need two. More colours, more bitmaps. And each bitmap is stored in an independent area of RAM. You never use more memory than you need to display the number of colours you want to.

But that means that each bitplane contains packed information - every byte of data in a bitplane contains the bit value for 8 different pixels, because each bitplane contains one bit of information per pixel. To update one pixel on screen, you need to read from every bitmap, update one bit, and write it back, and that's a lot of additional memory accesses. Doom, but on the Amiga, was slow not just because the CPU was slow, but because there was a lot of manipulation of data to turn it into the format the Amiga wanted and then push that over a fairly slow memory bus to have it displayed.

The CDTV was an aesthetically pleasing piece of hardware that absolutely sucked. It was an Amiga 500 in a hi-fi box with a caddy-loading CD drive, and it ran software that was just awful. There's no path to remediation here. No compelling apps were ever released. It's a terrible device. I love it. I bought one in 1996 because a local computer store had one and I pointed out that the company selling it had gone bankrupt some years earlier and literally nobody in my farming town was ever going to have any interest in buying a CD player that made a whirring noise when you turned it on because it had a fan and eventually they just sold it to me for not much money, and ever since then I wanted to have a CD player that ran Linux and well spoiler 30 years later I'm nearly there. That CDTV is going to be our test subject. We're going to try to get Doom running on it without executing any 68000 instructions.

We're facing two main problems here. The first is that all Amigas have a firmware ROM called Kickstart that runs at powerup. No matter how little you care about using any OS functionality, you can't start running your code until Kickstart has run. This means even documentation describing bare metal Amiga programming assumes that the hardware is already in the state that Kickstart left it in. This will become important later. The second is that we're going to need to actually write the code to use the Amiga hardware.

First, let's talk about Amiga graphics. We've already covered bitmaps, but for anyone used to modern hardware that's not the weirdest thing about what we're dealing with here. The CDTV's chipset supports a maximum of 64 colours in a mode called "Extra Half-Brite", or EHB, where you have 32 colours arbitrarily chosen from a palette and then 32 more colours that are identical but with half the intensity. For 64 colours we need 6 bitplanes, each of which can be located arbitrarily in the region of RAM accessible to the chipset ("chip RAM", distinguished from "fast ram" that's only accessible to the CPU). We tell the chipset where our bitplanes are and it displays them. Or, well, it does for a frame - after that the registers that pointed at our bitplanes no longer do, because when the hardware was DMAing through the bitplanes to display them it was incrementing those registers to point at the next address to DMA from. Which means that every frame we need to set those registers back.

Making sure you have code that's called every frame just to make your graphics work sounds intensely irritating, so Commodore gave us a way to avoid doing that. The chipset includes a coprocessor called "copper". Copper doesn't have a large set of features - in fact, it only has three. The first is that it can program chipset registers. The second is that it can wait for a specific point in screen scanout. The third (which we don't care about here) is that it can optionally skip an instruction if a certain point in screen scanout has already been reached. We can write a program (a "copper list") for the copper that tells it to program the chipset registers with the locations of our bitplanes and then wait until the end of the frame, at which point it will repeat the process. Now our bitplane pointers are always valid at the start of a frame.

Ok! We know how to display stuff. Now we just need to deal with not having 256 colours, and the whole "Doom expects pixels" thing. For the first of these, I stole code from ADoom, the only Amiga doom port I could easily find source for. This looks at the 256 colour palette loaded by Doom and calculates the closest approximation it can within the constraints of EHB. ADoom also includes a bunch of CPU-specific assembly optimisation for converting the "chunky" Doom graphic buffer into the "planar" Amiga bitplanes, none of which I used because (a) it's all for 68000 series CPUs and we're running on ARM, and (b) I have a quad core CPU running at 1.4GHz and I'm going to be pushing all the graphics over a 7.14MHz bus, the graphics mode conversion is not going to be the bottleneck here. Instead I just wrote a series of nested for loops that iterate through each pixel and update each bitplane and called it a day. The set of bitplanes I'm operating on here is allocated on the Linux side so I can read and write to them without being restricted by the speed of the Amiga bus (remember, each byte in each bitplane is going to be updated 8 times per frame, because it holds bits associated with 8 pixels), and then copied over to the Amiga's RAM once the frame is complete.

And, kind of astonishingly, this works! Once I'd figured out where I was going wrong with RGB ordering and which order the bitplanes go in, I had a recognisable copy of Doom running. Unfortunately there were weird graphical glitches - sometimes blocks would be entirely the wrong colour. It took me a while to figure out what was going on and then I felt stupid. Recording the screen and watching in slow motion revealed that the glitches often showed parts of two frames displaying at once. The Amiga hardware is taking responsibility for scanning out the frames, and the code on the Linux side isn't synchronised with it at all. That means I could update the bitplanes while the Amiga was scanning them out, resulting in a mashup of planes from two different Doom frames being used as one Amiga frame. One approach to avoid this would be to tie the Doom event loop to the Amiga, blocking my writes until the end of scanout. The other is to use double-buffering - have two sets of bitplanes, one being displayed and the other being written to. This consumes more RAM but since I'm not using the Amiga RAM for anything else that's not a problem. With this approach I have two copper lists, one for each set of bitplanes, and switch between them on each frame. This improved things a lot but not entirely, and there's still glitches when the palette is being updated (because there's only one set of colour registers), something Doom does rather a lot, so I'm going to need to implement proper synchronisation.

Except. This was only working if I ran a 68K emulator first in order to run Kickstart. If I tried accessing the hardware without doing that, things were in a weird state. I could update the colour registers, but accessing RAM didn't work - I could read stuff out, but anything I wrote vanished. Some more digging cleared that up. When you turn on a CPU it needs to start executing code from somewhere. On modern x86 systems it starts from a hardcoded address of 0xFFFFFFF0, which was traditionally a long way any RAM. The 68000 family instead reads its start address from address 0x00000004, which overlaps with where the Amiga chip RAM is. We can't write anything to RAM until we're executing code, and we can't execute code until we tell the CPU where the code is, which seems like a problem. This is solved on the Amiga by powering up in a state where the Kickstart ROM is "overlayed" onto address 0. The CPU reads the start address from the ROM, which causes it to jump into the ROM and start executing code there. Early on, the code tells the hardware to stop overlaying the ROM onto the low addresses, and now the RAM is available. This is poorly documented because it's not something you need to care if you execute Kickstart which every actual Amiga does and I'm only in this position because I've made poor life choices, but ok that explained things. To turn off the overlay you write to a register in one of the Complex Interface Adaptor (CIA) chips, and things start working like you'd expect.

Except, they don't. Writing to that register did nothing for me. I assumed that there was some other register I needed to write to first, and went to the extent of tracing every register access that occurred when running the emulator and replaying those in my code. Nope, still broken. What I finally discovered is that you need to pulse the reset line on the board before some of the hardware starts working - powering it up doesn't put you in a well defined state, but resetting it does.

So, I now have a slightly graphically glitchy copy of Doom running without any sound, displaying on an Amiga whose brain has been replaced with a parasitic Linux. Further updates will likely make things even worse. Code is, of course, available.

[1] This is why we had trouble with late era 32 bit systems and 4GB of RAM - a bunch of your hardware wanted to be in the same address space and so you couldn't put RAM there so you ended up with less than 4GB of RAM

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Brendan Gregg: When to Hire a Computer Performance Engineering Team (2025) part 1 of 2

Kernel Planet
3 hét 1 nap óta

As a leader in computer performance I've been asked by companies about how (and why) to form a performance engineering team, and as this is broadly useful I'll share my advice here.

Large tech companies in the US hire performance engineers (under that or other titles) to ensure that infrastructure costs and service latency don't grow too high, and that their service is reliable under peak load. A new performance team can likely find enough optimizations to halve infrastructure spend in their first couple of years, even for companies that have been using commercial performance or observability tools. Performance engineers do much more than those tools, working with development teams and vendors to build, test, debug, tune, and adopt new performance solutions, and to find deep optimizations that those tools can miss.

I previously worked on the performance engineering team for Netflix, a large tech consumer running on hundreds of thousands of AWS instances. I'm now doing similar work at Intel (a large tech vendor) for Intel and their customers. As a leader in this space I've also interacted with other performance teams and staff doing performance work at many companies. In this post I'll explain what these teams do and when you should consider forming one. In part 2 I'll provide sample job descriptions, specialties, advice, pitfalls, comments on AI, and what to do if you can't hire a performance team.

It's easy for hardware vendors like Intel to justify hiring performance engineers, as the number one factor in sales is beating a competitor's performance. However, my focus in this post is on non-vendor tech-heavy companies who hire these staff to drive down costs and latency outliers (e.g., banks, telecomms, defense, AI, tech-based companies, and anyone else who is spending more than $1M/year on back-end compute and AI).

What is the ROI of performance engineering?

The main ROIs are infrastructure cost savings, latency reductions, improved scalability and reliability, and faster engineering. The cost savings alone can justify a performance team and can help calculate its size, and I'll explore that in depth, but the other ROIs are worth considering and may be more important to a company depending on its stage of growth.

Infrastructure Cost Savings and Margin Improvements

An appropriately-sized performance team should be targeting 5-10% cost savings per year through tuning and product adoptions. (I'll explain appropriate sizes in the When To Hire section.) For many large companies a 5% result would be considered "good" and a 10% would be "great." Achieving this in practice can mean finding large wins (15-80%) on parts of the infrastructure, which become 5-10% overall. Wins are cumulative, so a team hitting 5% savings each year will multiply to become 28% after their 5th year (like compound interest). Even a modest 2% per year will become significant over time. While these compounded numbers can become large, a team needs to continue finding new cost savings each year to justify long-term retention, and should always focused on the next 5-10%.

Companies may invest in this work for more than just the cost savings: It can be about developing a competitive advantage in their area by providing a better cost/performance ratio, especially for companies with similar tech-based services that pass costs on to customers.

For sites that haven't employed performance engineers before, there can be enough low-hanging fruit that the team can halve infrastructure costs in their first couple of years (50%). It all depends on the number of staff, their level of expertise, how much perf work other staff are already doing (senior developers, SREs), how much custom code is running, and how complex and volatile the stack is.

It would great if we could publicly share specific results, which would look something like this:

    "This year we helped reduce our company's infrastructure spend by 5%, from $60M/year to $57M/year, however, since our user base also grew by 3%, we actually reduced cost-per-user by 8%, saving $5M/year in this and all future years."

However, these numbers are usually considered financially sensitive as they can reveal company growth, financial health, confidential infrastructure discounts, etc. As a performance engineer I can talk publicly about percent wins on a back-end service, but I usually can't map it to dollar signs. That doesn't help other companies to understand the value of performance engineering. It's not so much of a problem in Silicon Valley, since staff change companies all the time and word spreads about the latest practices in tech. But in far away countries performance engineering doesn't really exist yet, even though there are companies with sufficiently large infrastructure spend.

Continuing the above example, a typical 8% win could be composed of:

  • 2%: direct optimizations. We looked across all the infrastructure and found on average 5% wins on 40% of the infrastructure (on the rest we found nothing, this year).
  • 3%: developer/SRE enablement. We developed and adopted custom observability tools and helped developers use them, who in turn found some 15% wins across 20% of our infrastructure.
  • 3%: vendor adoptions. We supported a major adoption project that replaced 10% of our infrastructure for a 30% win.

With developer/SRE enablement and vendor adoptions, the performance team isn't finding the wins directly but is enabling other teams and vendors to do so. For example, when I worked at Netflix we built and maintained the flame graph "self-service" application, which developers used daily to find wins, and we worked on multiple product adoptions every year. This all needs to be considered as part of the performance team’s ROI.

Latency Reductions

Reducing the response time or latency of a service is a large part of performance engineering. This involves analyzing average latency, 99th percentile latency, and outlier latency; ensuring latency SLA/SLOs are met; and ensuring acceptable latency during perturbations or peak usage.

Many of the cost optimizations described earlier will also reduce average latency, but latency variance or outliers can remain. For example, a once-every-5-minute system task may have negligible cost and CPU footprint, but it may briefly perturb the application and cause latency outliers. These are debugged differently, often using monitoring, logs, distributed tracing, system-level tracing, packet logs, and custom ad-hoc tools. Sometimes the high latency is caused by the request type itself (the system is fine, but the end-user has requested a slow thing) or is an expected consequence of load (queueing theory, tail latency). Other times it can be from complex interactions across multiple layers of the software stack or from interactions across multiple network endpoints.

    As a related aside: One performance anti-pattern is when a company, to debug one performance problem, installs a monitoring tool that periodically does work and causes application latency outliers. Now the company has two problems. Tip: try turning off all monitoring agents and see if the problem goes away.

While latency is the main consideration to improve end user experience, others include throughput and parallelism.

Improved Scalability and Reliability

Systems under load can respond with exponential latency or a cascading failure, causing disruptions or a service outage. Performance engineers can test resource scalability with custom load generators and benchmarks, and use analysis tools to study all parts of the system to find and solve bottlenecks. A performance engineer will not just measure scalability limits, but should also explain what the limiting factors are and how to address them to scale further.

A stable and performant service will also earn trust in your company, and can help you grow customers more quickly. It may be a requirement for satisfying enterprise SLA/SLOs.

    I'll share a scalability story from my time at Sun Microsystems (a vendor). My goal was to achieve the number one throughput in the industry for a storage system, which would require exceeding 1M IOPS. The expected bottleneck was the rotational disks. I developed my own load generators and analysis tools and concluded that the real bottleneck was, surprisingly, the CPU interconnect. The interconnect was AMD HyperTransport 1, so AMD sent me a new systemboard with HT3 and faster CPUs. I installed it and…performance was identical. I was upset with myself for getting it wrong, until I discovered that AMD had sent me a HT1 board by mistake. They then sent me a real HT3 board and the performance increased by up to 75%! The CPU interconnect (when present) is just one of many components that companies typically don't check, and commercial observability tools don't check either.
Faster Engineering

Performance engineers can take care of components outside of a developer's code base so the developers can stay focused, and also provide them with a greater performance budget so that they can adopt expensive features earlier. For some early stage companies this ROI may be their most important (and is sometimes called engineering velocity). In detail:

  • Eliminate outside performance distractions: A development team can be coding at one hundred miles an hour in their codebase, but then run into a performance problem in a library, kernel, hypervisor, or hardware component, and need to slow down to learn some new thing and its analysis tools. Or, it could be some new performance technology that someone saw on hackernews and the development team wonder if they should stop to evaluate it. These performance activities can be offloaded to the perf team, so the developers stay focused on their code and at full speed. This is the same as how OS, deployment, and reliability issues can be offloaded to SRE and DevOps teams.
  • Bypass expensive project failures: Good performance engineers know the internals of the software and hardware stack (including the Linux kernel). Many developers don't and most of the time they don't need to. But this can lead to developers proposing ideas based on incorrect assumptions. (This actually happens a lot.) Having a one hour meeting with a performance engineering team can save months of engineering effort: A developer talks about their ideas A and B, and the perf team says "A won't work and this is why, but B sounds good and we can help." Months saved in one hour.
      At one large tech company many years ago I analyzed an engineering proposal to adopt a new event-based coding framework on the belief it would improve performance by ten fold. My analysis showed the real gain would have been less than 10%. The project was abandoned. This saved having all the engineers rewrite all the company's code, something we were expecting would take a year. This is one of the biggest performance wins of my career, not measured as a percentage but rather as engineering hours saved.
  • Develop accelerator tools: As mentioned earlier, performance teams can develop custom observability tools that developers use, finding issues sooner and accelerating development. For example, I've been publishing here about AI flame graphs, which involved kernel and hardware internals to build.
  • Faster feature adoption: Reducing costs and latency can enable developers to do more with a limited infrastructure and latency budget, offering more capabilities for their service or allowing more processing to be crammed in per request. Some companies have cost limits for adding features, so optimization work can mean a feature is now approved. All this work can mean a company can outpace their competitors with feature adoption, while maintaining a reliable, low-latency, cost/performance competitive service.
What do performance engineers do?

For non-vendor tech companies, in summary:

A. Test, debug, and tune new software and hardware products to find performance improvements, and drives company-wide adoption.

    Examples: New cloud instance types, language runtimes, JVM versions, JVM subsystems (new GC algorithms or compilers: Graal vs c2), system libraries (glibc vs tcmalloc etc.), kernels (Linux vs BSD) and versions, compilers (gcc, llvm, icc), processor features (AVX, QAT, etc.), hardware accelerators, and so on. It can take months to debug, fix, and patch everything so the latest thing delivers its performance claim.

B. Develop in-house performance solutions, such as custom analysis tools, that other teams use to find performance wins.

    Examples: Custom monitoring using Prometheus and Grafana, one-click flame graphs, and analysis tools using eBPF: All of this is open-source based, but someone has to get it working locally, integrate them with existing local tools, teach other teams how to use them, and maintain them.

C. Does deep-dive analysis to identify and reduce workload bottleneck(s) and latency outliers.

    Examples: Using code profilers (CPU flame graphs), distributed tracers (OpenTelemetry and products), application logs, system counters (Linux: sysstat), system tracers (Linux: eBPF, Ftrace, perf), static and dynamic instrumentation (Linux: kprobes, uprobes), debuggers (gdb, etc.), hardware counters (Linux: perf), and on rare occasions hardware instruction tracing. A lot of hands-on live debugging over an SSH session, following methodologies to efficiently find the root-cause(s), which can require the development of custom tools (mini load generators, observability tools, etc.).

D. Optimize software and hardware via tunable parameters and configuration choices.

    Examples: System tunables (Linux: sysctls), network tunables (socket options, qdiscs), device tunables, runtime tunables (Java -XX:*), library settings, environment variables, etc. As with (C), the team needs SSH access to do this and likely superuser privileges.

E. Work with development teams (internal and external) to catch non-scalable solutions early in development, and to suggest or test later performance improvements.

    Examples: Identifying communication layer will flood network links when horizontally scaled; A developer has a good optimization idea but can't get it to work and needs some help; There's a performance-related pull request on some software the company uses but the request is two years old and needs someone to fix code conflicts, test it, and advocate for merging it.

F. Develop proof-of-concept demonstrations of new performance technologies.

    Examples: Linux eBPF and io_uring can provide significant performance improvements when developed into hot-path kernel-based accelerators, but someone needs to at least build a POC to show it would work for the company. These are typically too esoteric for developers to try on their own.

G. Develop performance improvements directly for internal and external code.

    Examples: Performance engineers get a lot done by asking the right people, but sometimes no one has the time to code that Linux/runtime/database performance fix the company needs, so a perf engineer takes it on. We aren't as quick as full-time developers since we are hopping between different languages all the time, and as a new code base committer will typically come under extra (and time-consuming) scrutiny.

H. Capacity planning activities: purchase guidance, choosing metrics to monitor, and bottleneck forecasting.

    Examples: Modeling and performance characterization for hardware purchases, resource utilization monitoring to forecast capacity issues (nowadays often done by developers and SREs using monitoring tools); propose the best metrics to be watched in those monitoring tools for alert generation and auto-scaling rules; work with business side of the company to help define practical SLA/SLOs.

I. Perform knowledge sharing to uplift engineering.

    Examples: Performance education to help developers produce more efficient software; act as a conduit to share performance learnings between teams (that may otherwise be siloed) to avoid rework and rediscovery.

J. Provide in-house expertise to guide purchasing performance solutions.

    Examples: Providing in-house expertise for performance topics like observability, telemetry, and eBPF can help the company choose better commercial products by evaluating their capabilities and overhead costs, and can recognize which are just Prometheus and Grafana, or my open source eBPF tools, in a suit. Without expertise you're vulnerable to being ripped off, or may adopt a tool that increases infrastructure costs more than the gains it provides (I've seen some that have overhead exceeding 10%).

To elaborate on (A), the testing of new products: Other staff will try a technology by configuring it based on the README, run a load test, and then share the result with management. Some companies hire dedicated staff for this called "performance testers." Performance engineers get more out of the same technology by running analyzers during the test to understand its limiter ("active benchmarking"), and will tune the technology to get an extra 5%, 50%, or more performance. They may also discover that the limiter is an unintended target (e.g., accidentally testing a caching layer instead). Any performance test should be accompanied by an explanation of the limiting factor, since no explanation will reveal the test wasn't analyzed and the result may be bogus. You can simply ask "why is the result not double?".

    As an aside: "CPU bound" isn't an explanation. Do you mean (a) clockspeed, (b) thread pool size, (c) core count, (d) memory bus (which kernels misleadingly include in %CPU counters), or something else (like power, thermal, CPU subsystem bottleneck)? Each of those leads to a different actionable item for the company (E.g.: (a) faster processors; (b) more threads; (c) more cores; (d) faster memory, bigger caches, less NUMA, or software techniques like zero copy). That's just the generic stuff. The code behind any CPU bound workload will also be analyzed to look for inefficiencies, and sometimes their instructions as well.

Day to day, a performance engineer can spend a lot of time fixing broken builds and configuring workloads, because you're the first person testing new patches and bleeding-edge software versions.

What I've described here is for companies that consume tech. For vendors that sell it, performance engineering includes design modeling, analysis of prototype and in-development software and hardware, competitive benchmarking, non-regression testing of new product releases, and pre- and post-sales performance analysis support. (I explain this in more detail in Systems Performance 2nd edition, chapter 1.)

When to Hire a Performance Team and How Many

Most companies I've encountered are already doing some kind of performance work scattered across projects and individuals, but they don't yet have a central performance engineering team looking deeply at everything. This leaves their attention spotty, ok in some areas, poor to absent in others. A central performance team looks at everything and prioritizes work based on the potential ROI.

Here are a few rough rules to determine when you should start forming a company-wide performance engineering team and how to size it (see caveats at the end):

(A) One engineer at $1M/year infrastructure spend, then one per $10M to $20M/year

That first engineer finds some of the low-hanging fruit, and should be cost effective as your company grows past $1M/year. I'd then consider another performance engineer for every $10M to $20M, and maintain a 3:1 junior:senior ratio. The values you use depend on your performance engineer's skill and the complexity of your environment, and how aggressively you wish to improve performance. At a $20M spend, 5% yearly wins means $1M in savings per staff member (minus their cost); whereas for a $10M spend you'd need to hit 10% wins yearly for $1M in savings.

Consider that as your spend keeps growing you will keep adding more staff, which makes their job harder as there is less low-hanging fruit to find. However, your site will also be growing in scale and complexity, and developing new performance issues for the growing team to solve. Also, smaller percent wins become more impactful at large scale, so I expect such a growing perf team to remain cost effective. (To a point: the largest teams I've seen stop at around 150 staff.)

(B) Staff spend should equal or exceed observability monitoring spend

If you're spending $1M/year on an observability product, you can spend $1M/year on a performance engineering team: e.g., 3 to 4 good staff. If you're only spending $50k/year on an observability product, you can't hire a performance engineer at that price, but you can bring in a consultant or pay for performance training and conference attendance. As I'd expect staff to halve infrastructure costs over time, just the savings on monitoring alone (which typically scale with instance/server count) will pay for the new staff. Because these new engineers are actively reducing infrastructure spend, the total savings are much greater.

(C) When latency or reliability is prohibitive to growth

I’ve heard some small companies and startups say they spend more money on coffee than they do back-end compute, and don't want to waste limited developer time on negligible cost reductions. However, when a wave of new customers arrive they may hit scalability issues and start losing customers because latency is too high or reliability is too inconsistent. That's usually a good time for small companies to start investing in performance engineering.

Caveats for A-C
  • You likely already have some perf engineers under different titles, such as senior developers or SREs who are focusing on perf work, leaving less work for the central performance team. Account for these focused staff when you are determining how many performance engineers to hire.
  • There will be a backpressure effect: If a new team halves your infrastructure, do you then halve the team? Up to you. But first think about what a perf engineer does: They have to work on anything, any operating system, language, or hardware the company needs. So you have these extra staff that can go deep on anything. Just some personal examples: There was an 18 month stretch when Netflix needed more core SREs on rotation, so myself and other perf engineers were temporarily assigned to do SRE work; Netflix would also have me do kernel crash dump analysis and application core dump analysis, since I was already using debuggers at that level.
  • You ideally have more performance engineers than technologies so staff members can specialize. E.g.: If your stack is AWS, Intel, Linux kernel, Ubuntu OS, containers, lambda, gRPC, Java, golang, Python, Cassandra, and TensorFlow, that's 12 performance engineers. Plus at least one for locally developed code. Want to go multi-cloud? Add another engineer for each CSP.
  • Remember that performance wins are cumulative for future years. A novice team could struggle to get up to speed with both performance engineering and your complex environment, and could also discover that you already had senior staff find all the low-hanging fruit. They might therefore only deliver 2% cost savings in each of their first three years. But that still combines to be 6% going forward.
Companies and Global Staff

Here are some example articles about performance engineering work at non-vendor companies:

  • Netflix: Cloud Performance Root Cause Analysis [me, 2018]
  • Meta: Strobelight: A profiling service built on open source technology [Jordan Rome, 2025]
  • Pinterest: Debugging the One-in-a-Million Failure: Migrating Pinterest’s Search Infrastructure to Kubernetes [Samson Hu, et al. 2025]
  • LinkedIn: Who moved my 99th percentile latency? [Richard Hsu, Cuong Tran, 2015]
  • eBay: Speed by a Thousand Cuts [Senthil Padmanabhan, 2020]
  • Twitter: #ExpandTheEdge: Making Twitter Faster [Todd Segal, Anthony Roberts, 2019]
  • Salesforce: How Salesforce makes performance engineering work at Enterprise Scale [Archana Sethuraman]
  • Uber: PerfInsights: Detecting Performance Optimization Opportunities in Go Code using Generative AI [Lavanya Verma, Ryan Hang, Sung Whang, Joseph Wang]
  • Twitch: Go’s march to low-latency GC [Rhys Hiltner, 2016]
  • Airbnb: Creating Airbnb’s Page Performance Score [Andrew Scheuermann, 2021]
  • Stripe: Using ML to detect and respond to performance degradations in slices of Stripe payments [Lakshmi Narayan, Lakshmi Narayan, 2025]
  • DoorDash: How DoorDash Standardized and Improved Microservices Caching [Lev Neiman, Jason Fan, 2023]
  • Roblox: How We Redesigned a Highly Scaled Data Store to Lower 99th Percentile Latency 100x [Andrew Korytko, 2020]
  • Capital One: Driving cloud value through cost optimization & efficiency [Jerzy Grzywinski, Brent Segner, 2024]

I would like to add Bank of America, Wells Fargo, JPMorgan Chase, and CitiGroup to this list since they have many staff with the title "performance engineer" (as you can find on LinkedIn) but it's hard to find public articles about their work. I'd also like a canonical list of central performance engineering teams, but such org chart data can also be hard to find online, and staff don't always call themselves "performance engineers." Other keywords to look out for are: insights, monitoring, and observability; some are just called "support engineers".

Note that there is also a lot of performance engineering done at hardware, software, and cloud vendors (Intel, AMD, NVIDIA, Apple, Microsoft, Google, Amazon, etc.) not listed here, as well as at performance solution companies. In this post I just wanted to focus on non-vendor companies.

Global Staff

I've never seen concrete data on how many people are employed worldwide in performance engineering. Here are my guesses:

  • Staff identifying as performance engineers at non-vendor companies: <1,000.
  • Staff identifying as performance engineers at SW/HW vendors: >10,000.
  • Staff who have become focused on performance engineering work under other titles (developer, SRE, support): >100,000.
  • Staff who occasionally do performance engineering work: I'd guess most developers.

It's possible LinkedIn can provide better estimates if you have enterprise access.

Conclusion

There are many reasons to hire a performance engineering team, such as infrastructure cost savings, latency reductions, improved scalability and reliability, and faster engineering. Cost savings alone can justify hiring a team, because a team should be targeting a cost reduction of 5-10% every year, which over the years adds up to become significantly larger: 28%-61% savings after 5 years.

In this post I explained what performance engineers do and provided some suggested rules on hiring:

    A) One engineer at >$1M infrastructure spend, then another for every $10-20M.
    B) Performance staff spend should equal or exceed observability monitoring spend.

Note that you likely already have some senior developers or SREs who are focusing on perf work, reducing the number of new performance engineers you need.

I've met people who would like to work as performance engineers but their employer has no such roles (other than performance testing: not the same thing) despite spending millions per year on infrastructure. I hope this blog post helps companies understand the value of performance engineering and understand when and how many staff to hire.

Hiring good performance engineers isn't easy as it's a specialized area with a limited talent pool. In part 2 I'll discuss how to hire or train a performance engineering team and provide sample job descriptions and tips, and what to do if you can't hire a performance team.

Thanks

Thanks for the feedback and suggestions: Vadim Filanovsky (OpenAI), Jason Koch (Netflix), Ambud Sharma (Pinterest), Harshad Sane (Netflix), Ed Hunter, Deirdre Straughan.

Matthew Garrett: Secure boot certificate rollover is real but probably won't hurt you

Kernel Planet
3 hét 4 nap óta
LWN wrote an article which opens with the assertion "Linux users who have Secure Boot enabled on their systems knowingly or unknowingly rely on a key from Microsoft that is set to expire in September". This is, depending on interpretation, either misleading or just plain wrong, but also there's not a good source of truth here, so.

First, how does secure boot signing work? Every system that supports UEFI secure boot ships with a set of trusted certificates in a database called "db". Any binary signed with a chain of certificates that chains to a root in db is trusted, unless either the binary (via hash) or an intermediate certificate is added to "dbx", a separate database of things whose trust has been revoked[1]. But, in general, the firmware doesn't care about the intermediate or the number of intermediates or whatever - as long as there's a valid chain back to a certificate that's in db, it's going to be happy.

That's the conceptual version. What about the real world one? Most x86 systems that implement UEFI secure boot have at least two root certificates in db - one called "Microsoft Windows Production PCA 2011", and one called "Microsoft Corporation UEFI CA 2011". The former is the root of a chain used to sign the Windows bootloader, and the latter is the root used to sign, well, everything else.

What is "everything else"? For people in the Linux ecosystem, the most obvious thing is the Shim bootloader that's used to bridge between the Microsoft root of trust and a given Linux distribution's root of trust[2]. But that's not the only third party code executed in the UEFI environment. Graphics cards, network cards, RAID and iSCSI cards and so on all tend to have their own unique initialisation process, and need board-specific drivers. Even if you added support for everything on the market to your system firmware, a system built last year wouldn't know how to drive a graphics card released this year. Cards need to provide their own drivers, and these drivers are stored in flash on the card so they can be updated. But since UEFI doesn't have any sandboxing environment, those drivers could do pretty much anything they wanted to. Someone could compromise the UEFI secure boot chain by just plugging in a card with a malicious driver on it, and have that hotpatch the bootloader and introduce a backdoor into your kernel.

This is avoided by enforcing secure boot for these drivers as well. Every plug-in card that carries its own driver has it signed by Microsoft, and up until now that's been a certificate chain going back to the same "Microsoft Corporation UEFI CA 2011" certificate used in signing Shim. This is important for reasons we'll get to.

The "Microsoft Windows Production PCA 2011" certificate expires in October 2026, and the "Microsoft Corporation UEFI CA 2011" one in June 2026. These dates are not that far in the future! Most of you have probably at some point tried to visit a website and got an error message telling you that the site's certificate had expired and that it's no longer trusted, and so it's natural to assume that the outcome of time's arrow marching past those expiry dates would be that systems will stop booting. Thankfully, that's not what's going to happen.

First up: if you grab a copy of the Shim currently shipped in Fedora and extract the certificates from it, you'll learn it's not directly signed with the "Microsoft Corporation UEFI CA 2011" certificate. Instead, it's signed with a "Microsoft Windows UEFI Driver Publisher" certificate that chains to the "Microsoft Corporation UEFI CA 2011" certificate. That's not unusual, intermediates are commonly used and rotated. But if we look more closely at that certificate, we learn that it was issued in 2023 and expired in 2024. Older versions of Shim were signed with older intermediates. A very large number of Linux systems are already booting certificates that have expired, and yet things keep working. Why?

Let's talk about time. In the ways we care about in this discussion, time is a social construct rather than a meaningful reality. There's no way for a computer to observe the state of the universe and know what time it is - it needs to be told. It has no idea whether that time is accurate or an elaborate fiction, and so it can't with any degree of certainty declare that a certificate is valid from an external frame of reference. The failure modes of getting this wrong are also extremely bad! If a system has a GPU that relies on an option ROM, and if you stop trusting the option ROM because either its certificate has genuinely expired or because your clock is wrong, you can't display any graphical output[3] and the user can't fix the clock and, well, crap.

The upshot is that nobody actually enforces these expiry dates - here's the reference code that disables it. In a year's time we'll have gone past the expiration date for "Microsoft Windows UEFI Driver Publisher" and everything will still be working, and a few months later "Microsoft Windows Production PCA 2011" will also expire and systems will keep booting Windows despite being signed with a now-expired certificate. This isn't a Y2K scenario where everything keeps working because people have done a huge amount of work - it's a situation where everything keeps working even if nobody does any work.

So, uh, what's the story here? Why is there any engineering effort going on at all? What's all this talk of new certificates? Why are there sensationalist pieces about how Linux is going to stop working on old computers or new computers or maybe all computers?

Microsoft will shortly start signing things with a new certificate that chains to a new root, and most systems don't trust that new root. System vendors are supplying updates[4] to their systems to add the new root to the set of trusted keys, and Microsoft has supplied a fallback that can be applied to all systems even without vendor support[5]. If something is signed purely with the new certificate then it won't boot on something that only trusts the old certificate (which shouldn't be a realistic scenario due to the above), but if something is signed purely with the old certificate then it won't boot on something that only trusts the new certificate.

How meaningful a risk is this? We don't have an explicit statement from Microsoft as yet as to what's going to happen here, but we expect that there'll be at least a period of time where Microsoft signs binaries with both the old and the new certificate, and in that case those objects should work just fine on both old and new computers. The problem arises if Microsoft stops signing things with the old certificate, at which point new releases will stop booting on systems that don't trust the new key (which, again, shouldn't happen). But even if that does turn out to be a problem, nothing is going to force Linux distributions to stop using existing Shims signed with the old certificate, and having a Shim signed with an old certificate does nothing to stop distributions signing new versions of grub and kernels. In an ideal world we have no reason to ever update Shim[6] and so we just keep on shipping one signed with two certs.

If there's a point in the future where Microsoft only signs with the new key, and if we were to somehow end up in a world where systems only trust the old key and not the new key[7], then those systems wouldn't boot with new graphics cards, wouldn't be able to run new versions of Windows, wouldn't be able to run any Linux distros that ship with a Shim signed only with the new certificate. That would be bad, but we have a mechanism to avoid it. On the other hand, systems that only trust the new certificate and not the old one would refuse to boot older Linux, wouldn't support old graphics cards, and also wouldn't boot old versions of Windows. Nobody wants that, and for the foreseeable future we're going to see new systems continue trusting the old certificate and old systems have updates that add the new certificate, and everything will just continue working exactly as it does now.

Conclusion: Outside some corner cases, the worst case is you might need to boot an old Linux to update your trusted keys to be able to install a new Linux, and no computer currently running Linux will break in any way whatsoever.

[1] (there's also a separate revocation mechanism called SBAT which I wrote about here, but it's not relevant in this scenario)

[2] Microsoft won't sign GPLed code for reasons I think are unreasonable, so having them sign grub was a non-starter, but also the point of Shim was to allow distributions to have something that doesn't change often and be able to sign their own bootloaders and kernels and so on without having to have Microsoft involved, which means grub and the kernel can be updated without having to ask Microsoft to sign anything and updates can be pushed without any additional delays

[3] It's been a long time since graphics cards booted directly into a state that provided any well-defined programming interface. Even back in 90s, cards didn't present VGA-compatible registers until card-specific code had been executed (hence DEC Alphas having an x86 emulator in their firmware to run the driver on the card). No driver? No video output.

[4] There's a UEFI-defined mechanism for updating the keys that doesn't require a full firmware update, and it'll work on all devices that use the same keys rather than being per-device

[5] Using the generic update without a vendor-specific update means it wouldn't be possible to issue further updates for the next key rollover, or any additional revocation updates, but I'm hoping to be retired by then and I hope all these computers will also be retired by then

[6] I said this in 2012 and it turned out to be wrong then so it's probably wrong now sorry, but at least SBAT means we can revoke vulnerable grubs without having to revoke Shim

[7] Which shouldn't happen! There's an update to add the new key that should work on all PCs, but there's always the chance of firmware bugs

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Linux Plumbers Conference: All Microconferences have been Accepted!

Kernel Planet
1 hónap óta

Good news! All Microconferences have been accepted and are now accepting submissions. The accepted Microconferences are:

  • Android
  • Build Systems
  • Confidential Computing
  • Containers and checkpoint/restore
  • Device and Specific Purpose Memory
  • Devicetree
  • Embedded & Internet of Things
  • Gaming on Linux
  • Kernel Memory Management
  • Kernel Testing & Dependability
  • Linux System Monitoring and Observability
  • Live Update
  • Power and Thermal management
  • RISC-V
  • Rust
  • Safe Systems with Linux
  • sched_ext: The BPF extensible scheduler class
  • Scheduler and Real-Time
  • System Boot and Security
  • Toolchains
  • VFIO/IOMMU/PCI
  • x86

You can start submitting topics to these Microconferences. Remember to read the Blog on what makes the ideal Microconference topic before submitting.

After that, submit your topic and make sure that you select the appropriate track that you are submitting for (they are all listed under LPC Microconference Proposals and end with MC).

Dave Airlie (blogspot): ramalama/mesa : benchmarks on my hardware and open source vs proprietary

Kernel Planet
1 hónap óta

One of my pet peeves around running local LLMs and inferencing is the sheer mountain of shit^W^W^W complexity of compute stacks needed to run any of this stuff in an mostly optimal way on a piece of hardware.

CUDA, ROCm, and Intel oneAPI all to my mind scream over-engineering on a massive scale at least for a single task like inferencing. The combination of closed source, over the wall open source, and open source that is insurmountable for anyone to support or fix outside the vendor, screams that there has to be a simpler way. Combine that with the pytorch ecosystem and insanity of deploying python and I get a bit unstuck.

What can be done about it?

llama.cpp to me seems like the best answer to the problem at present, (a rust version would be a personal preference, but can't have everything). I like how ramalama wraps llama.cpp to provide a sane container interface, but I'd like to eventually get to the point where container complexity for a GPU compute stack isn't really needed except for exceptional cases.

On the compute stack side, Vulkan exposes most features of GPU hardware in a possibly suboptimal way, but with extensions all can be forgiven. Jeff Bolz from NVIDIA's talk at Vulkanised 2025 started to give me hope that maybe the dream was possible.

The main issue I have is Jeff is writing driver code for the NVIDIA proprietary vulkan driver which reduces complexity but doesn't solve my open source problem.

Enter NVK, the open source driver for NVIDIA GPUs. Karol Herbst and myself are taking a look at closing the feature gap with the proprietary one. For mesa 25.2 the initial support for VK_KHR_cooperative_matrix was landed, along with some optimisations, but there is a bunch of work to get VK_NV_cooperative_matrix2 and a truckload of compiler optimisations to catch up with NVIDIA.

But since mesa 25.2 was coming soon I wanted to try and get some baseline figures out.

I benchmarked on two systems (because my AMD 7900XT wouldn't fit in the case). Both Ryzen CPUs. The first I used system I put in an RTX5080 then a RTX6000 Ada and then the Intel A770. The second I used for the RX7900XT. The Intel SYCL stack failed to launch unfortunately inside ramalama and I hacked llama.cpp to use the A770 MMA accelerators. 

ramalama bench  hf://unsloth/Qwen3-8B-GGUF:UD-Q4_K_XL 

I picked this model at random, and I've no idea if it was a good idea.
 


Some analysis:

The token generation workload is a lot less matmul heavy than prompt processing, it also does a lot more synchronising. Jeff has stated CUDA wins here mostly due to CUDA graphs and most of the work needed is operation fusion on the llama.cpp side. Prompt processing is a lot more matmul heavy, extensions like NV_coopmat2 will help with that (NVIDIA vulkan already uses it in the above), but there may be further work to help close the CUDA gap. On AMD radv (open source) Vulkan is already better at TG than ROCm, but behind in prompt processing. Again coopmat2 like extensions should help close the gap there.

NVK is starting from a fair way behind, we just pushed support for the most basic coopmat extension and we know there is a long way to go, but I think most of it is achievable as we move forward and I hope to update with new scores on a semi regular basis. We also know we can definitely close the gap on the NVIDIA proprietary Vulkan driver if we apply enough elbow grease and register allocation :-)

I think it might also be worth putting some effort into radv coopmat2 support, I think if radv could overtake ROCm for both of these it would remove a large piece of complexity from the basic users stack.

As for Intel I've no real idea, I hope to get their SYCL implementation up and running, and maybe I should try and get my hands on a B580 card as a better baseline. When I had SYCL running once before I kinda remember it being 2-4x the vulkan driver, but there's been development on both sides. 

(The graphs were generated by Gemini.)

Pete Zaitcev: Floating Point

Kernel Planet
1 hónap óta
I'm unemployed right now and I go to job interviews once in a while. One time, the company was doing another AI thing, having to do with verifying that training computations were doing something useful, and not just "dumping a stream of floating point numbers".

Until now I didn't think of it, but apparently AI is all in FP. And it reminded me how I worked in a CPU design place, where they had a group focused on FP. Those guys were doing FP since the days of transistor. They migrated their designs, generation by generation, through TTL, ECL, Bi-CMOS, CMOS. When I heard from them last, they were tinkering with "deep sub-micron".

One remarkable part about their thing was that because they started out in transistors, their FPU didn't have any microcode. It was all in hardware. Even divisions! Just a bunch of counters that sequenced whatever necessary.

For a long time during the reign of x86, the group was somewhat de-prioritized, because many microprocessors at the time treated FP performance as an afterthought. A number of desktop CPUs shipped with no hardware FP at all. But look how the tables have turned. I honestly hope that it was not too late and AI has become a boon for the successors of my past colleagues.

Pete Zaitcev: AI writing

Kernel Planet
1 hónap 2 hét óta
On the topic of AI writing code, I've read a Sci-Fi story some 30 years ago, probably from the 1950s or 1960s.

At the future Earth, fiction writers write using machines. The quality of writing is associated with the sophistication of writer's machine. Publishers reject stories written on a lower end machine. The hero of the story is a struggling writer, who has to make do with a cheap unit. As his machine writes poorly, he's paid little, so he cannot save up for an upgrade. He hatches a plan to sneak into the house of a successful writer, and use the better machine to write a break-out story. The oddly prescient punch-line is, he discovers that the successful writer's machine was non-functional. His better wrote his stories manually in secret.

I wonder if such scenario may even be possible, programming-wise, if you work in a company that does not micro-manage velocity, or work as a consultant, so that your sausage factory remains behind a curtain.

Harald Welte: Security Issues regarding GSMA eSIMs / eUICCs + Javacard

Kernel Planet
1 hónap 2 hét óta

The independent security researcher Adam Gowdiak has published an extensive report on flaws he found in some eUICCs (the chips used to store eSIM profiles within the GSMA eSIM architecture). While the specific demonstrable exploit was in a product of one specific CardOS Vendor (Kigen, formerly part of ARM), the fundamental underlying issue is actually an architectural one.

The Oracle Javacard [memory] safety architecture relies on a so-called bytecode verifier which is a program that you run after compiling an application, but before executing the code on the Javacard. The specifications allow for both on-card and off-card verification. However, the computational complexity of this verifier is generally assumed to exceed the resources available inside many microcontrollers used to implement java cards. Such microcontrollers often are ARM SC000 (Cortex-M0 based) or SC300 (Cortex-M3 based) based, with only tens of kilobytes of RAM and hundreds of kilobytes of flash.

Javacard was originally developed for use cases within the banking/payment industry. In that industry, the card-issuing bank is the sole entity that has the keys to load java applets onto a card. That entity is of course interested in the security of the card, and will hence always run an off-card bytecode verifier. In a world of physical SIM/USIM cards issued by a single mobile operator, the situation is the same: The card-issuing MNO/MVNO is the only entity with key materials to install additional java applets on the card.

This fundamental problem became already apparent by earlier findings by Adam Gowdiak in 2019, but at least in terms of public responses by Oracle and Gemalto back then, they mostly did hand-waving and/or made lame excuses.

However, when the industry represented in GSMA standardized the eSIM architecture, this changed. Suddenly we have various eSIM profiles of various different operators, each holding key material to install Java applets on the shared card. In such an environment, it is no longer safe to assume that every MNO/MVNO can be trusted to be non-adverserial and hence trusted to run that off-card bytecode verifier before loading applets onto the card.

If the Javacard runtime on the existing card/chip itself cannot autonomously perform those verification tasks, I don't see how the problem can ever be solved short of completely removing/disabling Javacard support in such eUICCs. Luckily it is an optional feature and not a mandatory requirement for an eUICC to be approved/accredited. Sadly many MNOs/MVNOS however will mandate Javacard support in their eSIM profiles and hence refuse to install into an eUICC without it :(

In my opinion, the solution to the problem can only be to either make the GSMA require full on-card bytecode verification on all eUICCs, or to remove Javacard support from the eUICC.

We have to keep in mind that there are hundreds if not thousands of MVNOs around the planet, and all of them are subject to whatever local jurisdiction they operate in, and also subject to whatever government pressure (e.g from intelligence agencies).

In hindsight, anyone familiar with the 2019 work by Gowdiak and an understanding of the fundamental change to multiple stakeholders in an eUICC (compared to classic SIM/USIM) should have arrived at the conclusion that there is a serious problem that needs addressing. I think the 2019 work had not been publicized and covered significantly enough to make sure that everyone in the industry was made aware of the problems. And that in turn is mostly a result of Oracle + Gemalto downplaying the 2019 findings back in the day, rather than raising awareness within all relevant groups and bodies of the industry.

Mitigation via TS.48 key diversification

The specific attack presented was using a GSMA TS.48 test profile to install the malicious java bytecode; those TS.48 profiles are standardized profiles used by the industry for cellular testing; until the attack they contained well-known static OTA key material. The mitigation to randomize/diversity those keys in TS.48v7 closes that particular vector, but the attack itself is not dependent on test profiles. Any MNO/NVNO (or rather, anyone with access to a commercial service of a SM-DP+ accredited by GSMA) obviously has the ability to load java applets into the eSIM profile that they create, using keys that they themselves specify.

What IMHO ought to be done

  • Oracle should get off their we only provide a reference implementation and vendors should invent their own prporietary verification mechanisms horse. This is just covering their own ass and not helping any of their downstream users/implementers. The reference implementation should show how proper verification can be done in the most resource-constrained environment of cards (it's JavaCard, after all!), and any advances of the verifier should happen once at Oracle, and then used by all the implementers (CardOS vendors). Anyone who really cares about security of a standardized platform (like Javacard) should never leave key aspects of it up to each and every implementer, but rather should solve the problem once, publicly, with validation and testing tools, independent 3rd party penetration testing and then ensure that every implementer uses that proven implementation.

  • GSMA should have security requirements (and mandatory penetration tests) specifically regarding the JVM/JRE of each card that gets SAS-UP accredited.

  • GSMA should require that Javacard support should be disabled on all existing eUICCs that cannot legitimately claim/demonstrate that they are performing full bytecode verification entirely on-card.

  • GSMA should refuse any future SAS-UP accreditation to any product that requires off-card bytecode verification

  • The entire industry should find a way to think beyond Javacard, or in fact any technology whose security requires verification of the executable program that is too complex to perform on-card on the targeted microcontrollers.

Dave Airlie (blogspot): nvk: blackwell support

Kernel Planet
1 hónap 3 hét óta

Blog posts are like buses sometimes...

I've spent time over the last month enabling Blackwell support on NVK, the Mesa vulkan driver for NVIDIA GPUs. Faith from Collabora, the NVK maintainer has cleaned up and merged all the major pieces of this work and landed them into mesa this week. Mesa 25.2 should ship with a functioning NVK on blackwell. The code currently in mesa main passes all tests in the Vulkan CTS.

Quick summary of the major fun points:

Ben @ NVIDIA had done the initial kernel bringup in to r570 firmware in the nouveau driver. I worked with Ben on solidifying that work and ironing out a bunch of memory leaks and regressions that snuck in.

Once the kernel was stable, there were a number of differences between Ada and Blackwell that needed to be resolved. Thanks to Faith, Mel and Mohamed for their help, and NVIDIA for providing headers and other info.

I did most of the work on a GB203 laptop and a desktop 5080.

1. Instruction encoding: a bunch of instructions changed how they were encoded. Mel helped sort out most of those early on.

2. Compute/QMD: the QMD which is used to launch compute shaders, has a new encoding. NVIDIA released the official QMD headers which made this easier in the end.

3. Texture headers: texture headers were encoded different from Hopper on, so we had to use new NVIDIA headers to encode those properly

4. Depth/Stencil: NVIDIA added support for separate d/s planes and this also has some knock on effects on surface layouts. 

5. Surface layout changes. NVIDIA attaches a memory kind to memory allocations, due to changes in Blackwell, they now use a generic kind for all allocations. You now longer know the internal bpp dependent layout of the surfaces. This means changes to the dma-copy engine to provide that info. This means we have some modifier changes to cook with NVIDIA over the next few weeks at least for 8/16 bpp surfaces. Mohamed helped get this work and host image copy support done.

6. One thing we haven't merged is bound texture support. Currently blackwell is using bindless textures which might be a little slower. Due to changes in the texture instruction encoding, you have to load texture handles to intermediate uniform registers before using them as bound handles. This causes a lot of fun with flow control and when you can spill uniform registers. I've written a few efforts at using bound textures, so we understand how to use them, just have some compiler issues to maybe get it across the line.

7. Proper instruction scheduling isn't landed yet. I have a spreadsheet with all the figures, and I started typing, so will try and get that into an MR before I take some holidays. 

 

Dave Airlie (blogspot): radv: VK_KHR_video_encode_av1 support

Kernel Planet
1 hónap 3 hét óta

 I should have mentioned this here a week ago. The Vulkan AV1 encode extension has been out for a while, and I'd done the initial work on enabling it with radv on AMD GPUs. I then left it in a branch, which Benjamin from AMD picked up and fixed a bunch of bugs, and then we both got distracted. I realised when doing VP9 that it hasn't landed, so did a bit of cleanup. Then David from AMD picked it up and carried it over the last mile and it got merged last week.

So radv on supported hw now supports all vulkan decode/encode formats currently available.  

Matthew Garrett: Why is there no consistent single signon API flow?

Kernel Planet
2 hónap óta
Single signon is a pretty vital part of modern enterprise security. You have users who need access to a bewildering array of services, and you want to be able to avoid the fallout of one of those services being compromised and your users having to change their passwords everywhere (because they're clearly going to be using the same password everywhere), or you want to be able to enforce some reasonable MFA policy without needing to configure it in 300 different places, or you want to be able to disable all user access in one place when someone leaves the company, or, well, all of the above. There's any number of providers for this, ranging from it being integrated with a more general app service platform (eg, Microsoft or Google) or a third party vendor (Okta, Ping, any number of bizarre companies). And, in general, they'll offer a straightforward mechanism to either issue OIDC tokens or manage SAML login flows, requiring users present whatever set of authentication mechanisms you've configured.

This is largely optimised for web authentication, which doesn't seem like a huge deal - if I'm logging into Workday then being bounced to another site for auth seems entirely reasonable. The problem is when you're trying to gate access to a non-web app, at which point consistency in login flow is usually achieved by spawning a browser and somehow managing submitting the result back to the remote server. And this makes some degree of sense - browsers are where webauthn token support tends to live, and it also ensures the user always has the same experience.

But it works poorly for CLI-based setups. There's basically two options - you can use the device code authorisation flow, where you perform authentication on what is nominally a separate machine to the one requesting it (but in this case is actually the same) and as a result end up with a straightforward mechanism to have your users socially engineered into giving Johnny Badman a valid auth token despite webauthn nominally being unphisable (as described years ago), or you reduce that risk somewhat by spawning a local server and POSTing the token back to it - which works locally but doesn't work well if you're dealing with trying to auth on a remote device. The user experience for both scenarios sucks, and it reduces a bunch of the worthwhile security properties that modern MFA supposedly gives us.

There's a third approach, which is in some ways the obviously good approach and in other ways is obviously a screaming nightmare. All the browser is doing is sending a bunch of requests to a remote service and handling the response locally. Why don't we just do the same? Okta, for instance, has an API for auth. We just need to submit the username and password to that and see what answer comes back. This is great until you enable any kind of MFA, at which point the additional authz step is something that's only supported via the browser. And basically everyone else is the same.

Of course, when we say "That's only supported via the browser", the browser is still just running some code of some form and we can figure out what it's doing and do the same. Which is how you end up scraping constants out of Javascript embedded in the API response in order to submit that data back in the appropriate way. This is all possible but it's incredibly annoying and fragile - the contract with the identity provider is that a browser is pointed at a URL, not that any of the internal implementation remains consistent.

I've done this. I've implemented code to scrape an identity provider's auth responses to extract the webauthn challenges and feed those to a local security token without using a browser. I've also written support for forwarding those challenges over the SSH agent protocol to make this work with remote systems that aren't running a GUI. This week I'm working on doing the same again, because every identity provider does all of this differently.

There's no fundamental reason all of this needs to be custom. It could be a straightforward "POST username and password, receive list of UUIDs describing MFA mechanisms, define how those MFA mechanisms work". That even gives space for custom auth factors (I'm looking at you, Okta Fastpass). But instead I'm left scraping JSON blobs out of Javascript and hoping nobody renames a field, even though I only care about extremely standard MFA mechanisms that shouldn't differ across different identity providers.

Someone, please, write a spec for this. Please don't make it be me.

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Matthew Garrett: My a11y journey

Kernel Planet
2 hónap óta
23 years ago I was in a bad place. I'd quit my first attempt at a PhD for various reasons that were, with hindsight, bad, and I was suddenly entirely aimless. I lucked into picking up a sysadmin role back at TCM where I'd spent a summer a year before, but that's not really what I wanted in my life. And then Hanna mentioned that her PhD supervisor was looking for someone familiar with Linux to work on making Dasher, one of the group's research projects, more usable on Linux. I jumped.

The timing was fortuitous. Sun were pumping money and developer effort into accessibility support, and the Inference Group had just received a grant from the Gatsy Foundation that involved working with the ACE Centre to provide additional accessibility support. And I was suddenly hacking on code that was largely ignored by most developers, supporting use cases that were irrelevant to most developers. Being in a relatively green field space sounds refreshing, until you realise that you're catering to actual humans who are potentially going to rely on your software to be able to communicate. That's somewhat focusing.

This was, uh, something of an on the job learning experience. I had to catch up with a lot of new technologies very quickly, but that wasn't the hard bit - what was difficult was realising I had to cater to people who were dealing with use cases that I had no experience of whatsoever. Dasher was extended to allow text entry into applications without needing to cut and paste. We added support for introspection of the current applications UI so menus could be exposed via the Dasher interface, allowing people to fly through menu hierarchies and pop open file dialogs. Text-to-speech was incorporated so people could rapidly enter sentences and have them spoke out loud.

But what sticks with me isn't the tech, or even the opportunities it gave me to meet other people working on the Linux desktop and forge friendships that still exist. It was the cases where I had the opportunity to work with people who could use Dasher as a tool to increase their ability to communicate with the outside world, whose lives were transformed for the better because of what we'd produced. Watching someone use your code and realising that you could write a three line patch that had a significant impact on the speed they could talk to other people is an incomparable experience. It's been decades and in many ways that was the most impact I've ever had as a developer.

I left after a year to work on fruitflies and get my PhD, and my career since then hasn't involved a lot of accessibility work. But it's stuck with me - every improvement in that space is something that has a direct impact on the quality of life of more people than you expect, but is also something that goes almost unrecognised. The people working on accessibility are heroes. They're making all the technology everyone else produces available to people who would otherwise be blocked from it. They deserve recognition, and they deserve a lot more support than they have.

But when we deal with technology, we deal with transitions. A lot of the Linux accessibility support depended on X11 behaviour that is now widely regarded as a set of misfeatures. It's not actually good to be able to inject arbitrary input into an arbitrary window, and it's not good to be able to arbitrarily scrape out its contents. X11 never had a model to permit this for accessibility tooling while blocking it for other code. Wayland does, but suffers from the surrounding infrastructure not being well developed yet. We're seeing that happen now, though - Gnome has been performing a great deal of work in this respect, and KDE is picking that up as well. There isn't a full correspondence between X11-based Linux accessibility support and Wayland, but for many users the Wayland accessibility infrastructure is already better than with X11.

That's going to continue improving, and it'll improve faster with broader support. We've somehow ended up with the bizarre politicisation of Wayland as being some sort of woke thing while X11 represents the Roman Empire or some such bullshit, but the reality is that there is no story for improving accessibility support under X11 and sticking to X11 is going to end up reducing the accessibility of a platform.

When you read anything about Linux accessibility, ask yourself whether you're reading something written by either a user of the accessibility features, or a developer of them. If they're neither, ask yourself why they actually care and what they're doing to make the future better.

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Matthew Garrett: Locally hosting an internet-connected server

Kernel Planet
2 hónap 1 hét óta
I'm lucky enough to have a weird niche ISP available to me, so I'm paying $35 a month for around 600MBit symmetric data. Unfortunately they don't offer static IP addresses to residential customers, and nor do they allow multiple IP addresses per connection, and I'm the sort of person who'd like to run a bunch of stuff myself, so I've been looking for ways to manage this.

What I've ended up doing is renting a cheap VPS from a vendor that lets me add multiple IP addresses for minimal extra cost. The precise nature of the VPS isn't relevant - you just want a machine (it doesn't need much CPU, RAM, or storage) that has multiple world routeable IPv4 addresses associated with it and has no port blocks on incoming traffic. Ideally it's geographically local and peers with your ISP in order to reduce additional latency, but that's a nice to have rather than a requirement.

By setting that up you now have multiple real-world IP addresses that people can get to. How do we get them to the machine in your house you want to be accessible? First we need a connection between that machine and your VPS, and the easiest approach here is Wireguard. We only need a point-to-point link, nothing routable, and none of the IP addresses involved need to have anything to do with any of the rest of your network. So, on your local machine you want something like:

[Interface]
PrivateKey = privkeyhere
ListenPort = 51820
Address = localaddr/32

[Peer]
Endpoint = VPS:51820
PublicKey = pubkeyhere
AllowedIPs = VPS/0

And on your VPS, something like:

[Interface]
Address = vpswgaddr/32
SaveConfig = true
ListenPort = 51820
PrivateKey = privkeyhere

[Peer]
PublicKey = pubkeyhere
AllowedIPs = localaddr/32

The addresses here are (other than the VPS address) arbitrary - but they do need to be consistent, otherwise Wireguard is going to be unhappy and your packets will not have a fun time. Bring that interface up with wg-quick and make sure the devices can ping each other. Hurrah! That's the easy bit.

Now you want packets from the outside world to get to your internal machine. Let's say the external IP address you're going to use for that machine is 321.985.520.309 and the wireguard address of your local system is 867.420.696.005. On the VPS, you're going to want to do:

iptables -t nat -A PREROUTING -p tcp -d 321.985.520.309 -j DNAT --to-destination 867.420.696.005

Now, all incoming packets for 321.985.520.309 will be rewritten to head towards 867.420.696.005 instead (make sure you've set net.ipv4.ip_forward to 1 via sysctl!). Victory! Or is it? Well, no.

What we're doing here is rewriting the destination address of the packets so instead of heading to an address associated with the VPS, they're now going to head to your internal system over the Wireguard link. Which is then going to ignore them, because the AllowedIPs statement in the config only allows packets coming from your VPS, and these packets still have their original source IP. We could rewrite the source IP to match the VPS IP, but then you'd have no idea where any of these packets were coming from, and that sucks. Let's do something better. On the local machine, in the peer, let's update AllowedIps to 0.0.0.0/0 to permit packets form any source to appear over our Wireguard link. But if we bring the interface up now, it'll try to route all traffic over the Wireguard link, which isn't what we want. So we'll add table = off to the interface stanza of the config to disable that, and now we can bring the interface up without breaking everything but still allowing packets to reach us. However, we do still need to tell the kernel how to reach the remote VPN endpoint, which we can do with ip route add vpswgaddr dev wg0. Add this to the interface stanza as:

PostUp = ip route add vpswgaddr dev wg0
PreDown = ip route del vpswgaddr dev wg0

That's half the battle. The problem is that they're going to show up there with the source address still set to the original source IP, and your internal system is (because Linux) going to notice it has the ability to just send replies to the outside world via your ISP rather than via Wireguard and nothing is going to work. Thanks, Linux. Thinux.

But there's a way to solve this - policy routing. Linux allows you to have multiple separate routing tables, and define policy that controls which routing table will be used for a given packet. First, let's define a new table reference. On the local machine, edit /etc/iproute2/rt_tables and add a new entry that's something like:

1 wireguard

where "1" is just a standin for a number not otherwise used there. Now edit your wireguard config and replace table=off with table=wireguard - Wireguard will now update the wireguard routing table rather than the global one. Now all we need to do is to tell the kernel to push packets into the appropriate routing table - we can do that with ip rule add from localaddr lookup wireguard, which tells the kernel to take any packet coming from our Wireguard address and push it via the Wireguard routing table. Add that to your Wireguard interface config as:

PostUp = ip rule add from localaddr lookup wireguard
PreDown = ip rule del from localaddr lookup wireguard
and now your local system is effectively on the internet.

You can do this for multiple systems - just configure additional Wireguard interfaces on the VPS and make sure they're all listening on different ports. If your local IP changes then your local machines will end up reconnecting to the VPS, but to the outside world their accessible IP address will remain the same. It's like having a real IP without the pain of convincing your ISP to give it to you.

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Dave Airlie (blogspot): radv: vulkan VP9 video decode

Kernel Planet
2 hónap 2 hét óta

The Vulkan WG has released VK_KHR_video_decode_vp9. I did initial work on a Mesa extensions for this a good while back, and I've updated the radv code with help from AMD and Igalia to the final specification.

There is an open MR[1] for radv to add support for vp9 decoding on navi10+ with the latest firmware images in linux-firmware. It is currently passing all VK-GL-CTS tests for VP9 decode.

Adding this decode extension is a big milestone for me as I think it now covers all the reasons I originally got involved in Vulkan Video as signed off, there is still lots to do and I'll stay involved, but it's been great to see the contributions from others and how there is a bit of Vulkan Video community upstream in Mesa.

 [1] https://gitlab.freedesktop.org/mesa/mesa/-/merge_requests/35398

 

Matthew Garrett: Twitter's new encrypted DMs aren't better than the old ones

Kernel Planet
2 hónap 2 hét óta
(Edit: Twitter could improve this significantly with very few changes - I wrote about that here. It's unclear why they'd launch without doing that, since it entirely defeats the point of using HSMs)

When Twitter[1] launched encrypted DMs a couple
of years ago, it was the worst kind of end-to-end
encrypted - technically e2ee, but in a way that made it relatively easy for Twitter to inject new encryption keys and get everyone's messages anyway. It was also lacking a whole bunch of features such as "sending pictures", so the entire thing was largely a waste of time. But a couple of days ago, Elon announced the arrival of "XChat", a new encrypted message platform built on Rust with (Bitcoin style) encryption, whole new architecture. Maybe this time they've got it right?

tl;dr - no. Use Signal. Twitter can probably obtain your private keys, and admit that they can MITM you and have full access to your metadata.

The new approach is pretty similar to the old one in that it's based on pretty straightforward and well tested cryptographic primitives, but merely using good cryptography doesn't mean you end up with a good solution. This time they've pivoted away from using the underlying cryptographic primitives directly and into higher level abstractions, which is probably a good thing. They're using Libsodium's boxes for message encryption, which is, well, fine? It doesn't offer forward secrecy (if someone's private key is leaked then all existing messages can be decrypted) so it's a long way from the state of the art for a messaging client (Signal's had forward secrecy for over a decade!), but it's not inherently broken or anything. It is, however, written in C, not Rust[2].

That's about the extent of the good news. Twitter's old implementation involved clients generating keypairs and pushing the public key to Twitter. Each client (a physical device or a browser instance) had its own private key, and messages were simply encrypted to every public key associated with an account. This meant that new devices couldn't decrypt old messages, and also meant there was a maximum number of supported devices and terrible scaling issues and it was pretty bad. The new approach generates a keypair and then stores the private key using the Juicebox protocol. Other devices can then retrieve the private key.

Doesn't this mean Twitter has the private key? Well, no. There's a PIN involved, and the PIN is used to generate an encryption key. The stored copy of the private key is encrypted with that key, so if you don't know the PIN you can't decrypt the key. So we brute force the PIN, right? Juicebox actually protects against that - before the backend will hand over the encrypted key, you have to prove knowledge of the PIN to it (this is done in a clever way that doesn't directly reveal the PIN to the backend). If you ask for the key too many times while providing the wrong PIN, access is locked down.

But this is true only if the Juicebox backend is trustworthy. If the backend is controlled by someone untrustworthy[3] then they're going to be able to obtain the encrypted key material (even if it's in an HSM, they can simply watch what comes out of the HSM when the user authenticates if there's no validation of the HSM's keys). And now all they need is the PIN. Turning the PIN into an encryption key is done using the Argon2id key derivation function, using 32 iterations and a memory cost of 16MB (the Juicebox white paper says 16KB, but (a) that's laughably small and (b) the code says 16 * 1024 in an argument that takes kilobytes), which makes it computationally and moderately memory expensive to generate the encryption key used to decrypt the private key. How expensive? Well, on my (not very fast) laptop, that takes less than 0.2 seconds. How many attempts to I need to crack the PIN? Twitter's chosen to fix that to 4 digits, so a maximum of 10,000. You aren't going to need many machines running in parallel to bring this down to a very small amount of time, at which point private keys can, to a first approximation, be extracted at will.

Juicebox attempts to defend against this by supporting sharding your key over multiple backends, and only requiring a subset of those to recover the original. I can't find any evidence that Twitter's does seem to be making use of this,Twitter uses three backends and requires data from at least two, but all the backends used are under x.com so are presumably under Twitter's direct control. Trusting the keystore without needing to trust whoever's hosting it requires a trustworthy communications mechanism between the client and the keystore. If the device you're talking to can prove that it's an HSM that implements the attempt limiting protocol and has no other mechanism to export the data, this can be made to work. Signal makes use of something along these lines using Intel SGX for contact list and settings storage and recovery, and Google and Apple also have documentation about how they handle this in ways that make it difficult for them to obtain backed up key material. Twitter has no documentation of this, and as far as I can tell does nothing to prove that the backend is in any way trustworthy. (Edit to add: The Juicebox API does support authenticated communication between the client and the HSM, but that relies on you having some way to prove that the public key you're presented with corresponds to a private key that only exists in the HSM. Twitter gives you the public key whenever you communicate with them, so even if they've implemented this properly you can't prove they haven't made up a new key and MITMed you the next time you retrieve your key)

On the plus side, Juicebox is written in Rust, so Elon's not 100% wrong. Just mostly wrong.

But ok, at least you've got viable end-to-end encryption even if someone can put in some (not all that much, really) effort to obtain your private key and render it all pointless? Actually no, since you're still relying on the Twitter server to give you the public key of the other party and there's no out of band mechanism to do that or verify the authenticity of that public key at present. Twitter can simply give you a public key where they control the private key, decrypt the message, and then reencrypt it with the intended recipient's key and pass it on. The support page makes it clear that this is a known shortcoming and that it'll be fixed at some point, but they said that about the original encrypted DM support and it never was, so that's probably dependent on whether Elon gets distracted by something else again. And the server knows who and when you're messaging even if they haven't bothered to break your private key, so there's a lot of metadata leakage.

Signal doesn't have these shortcomings. Use Signal.

[1] I'll respect their name change once Elon respects his daughter

[2] There are implementations written in Rust, but Twitter's using the C one with these JNI bindings

[3] Or someone nominally trustworthy but who's been compelled to act against your interests - even if Elon were absolutely committed to protecting all his users, his overarching goals for Twitter require him to have legal presence in multiple jurisdictions that are not necessarily above placing employees in physical danger if there's a perception that they could obtain someone's encryption keys

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Matthew Garrett: How Twitter could (somewhat) fix their encrypted DMs

Kernel Planet
2 hónap 2 hét óta
As I wrote in my last post, Twitter's new encrypted DM infrastructure is pretty awful. But the amount of work required to make it somewhat better isn't large.

When Juicebox is used with HSMs, it supports encrypting the communication between the client and the backend. This is handled by generating a unique keypair for each HSM. The public key is provided to the client, while the private key remains within the HSM. Even if you can see the traffic sent to the HSM, it's encrypted using the Noise protocol and so the user's encrypted secret data can't be retrieved.

But this is only useful if you know that the public key corresponds to a private key in the HSM! Right now there's no way to know this, but there's worse - the client doesn't have the public key built into it, it's supplied as a response to an API request made to Twitter's servers. Even if the current keys are associated with the HSMs, Twitter could swap them out with ones that aren't, terminate the encrypted connection at their endpoint, and then fake your query to the HSM and get the encrypted data that way. Worse, this could be done for specific targeted users, without any indication to the user that this has happened, making it almost impossible to detect in general.

This is at least partially fixable. Twitter could prove to a third party that their Juicebox keys were generated in an HSM, and the key material could be moved into clients. This makes attacking individual users more difficult (the backdoor code would need to be shipped in the public client), but can't easily help with the website version[1] even if a framework exists to analyse the clients and verify that the correct public keys are in use.

It's still worse than Signal. Use Signal.

[1] Since they could still just serve backdoored Javascript to specific users. This is, unfortunately, kind of an inherent problem when it comes to web-based clients - we don't have good frameworks to detect whether the site itself is malicious.

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Brendan Gregg: 3 Years of Extremely Remote Work

Kernel Planet
3 hónap óta

In the last 3 years I've attended 77 meetings that began between 1am and 6am, roughly once every two weeks, followed by my usual 7am start, Monday to Saturday. I'm working remotely from Australia for a US firm (Intel) who does not have a local office here. I'm not complaining. I work weird hours, but I don't think I work too many. I'm writing this post because there are some misconceptions and assumptions about the lives of remote workers, and I thought I'd share my own details as an anecdote, along with some tips for others in a similar situation (US job from Asia).

Most early meetings were 1 hour, but some were longer, for a total of 102 hours awake. As a histogram:

I've never been a morning person, but I can manage a 7am start. What about a 2am meeting? Also doable. They sound ok in isolation, but consider that every 2-3am is followed by a 7am start, which means a 6:30am alarm after going back to sleep at 3:30am, if you're lucky. It's not easy working this timezone difference, and I'd guess others have it even worse than I do (more meetings).

Like other remote staff I work with, I have a dedicated office at home where I can close the door and work uninterrupted for hours (it's changed quite a bit since it was filmed in the eBPF documentary):

That's a Samson Meteor mic on a desktop suspension boom, and I have another suspension boom clamped onto a bookcase holding a Logitech BRIO camera (when I had it sitting on my monitor it'd shake as I typed). It's important to have the best sound and video when that's how you're interacting with people all day.

Miscellaneous notes and tips about remote work:

Count out-of-hours meetings. Sometimes people hesitate to book me at 2am, when there's no other time that would work, and it takes a few emails to convince them that it's ok. I've found it saves time to log these meetings, count them, and share stats: "I've had 76 meetings between 1am and 6am, if you need to add another, that's ok, it'll be my 77th."

Never complain about the hours. There may be someone in management who doesn't support remote work, who could use such complains to argue against it. Sometimes, when I'm searching to find the right words to say at 4-something-am, I've confessed that I'm a bit tired, but I really try to never mention it.

Staying motivated. I found keeping a daily log of what I accomplished works best (I've done this for over a decade). If one day my entry looks soft, I try to do more on the next. I summarize each week for my manager.

People assume it's a problem when it isn't. Brendan didn't accept the meeting, oh, it's because it's 2am for him and he'll be asleep. Wrong! It's because I have a clash with another 2am meeting. I try to communicate this with the meeting host, but there can be others in the meeting that don't get the message, and they never consider this possibility. If I were back in California I'd still miss the meeting, but people would assume it's due to a clash. Here, they assume I'm asleep and not making the effort, when in fact I am. It means people are assuming that remote work is a problem when it isn't.

Some meetings get cancelled or silently recorded. The 77 count I mentioned earlier doesn't include the several meetings that were cancelled at the last minute, but I woke up for anyway, or those that weren't supposed to be recorded but I woke up to find they were. If you have remote staff, please try to cancel meetings before they go to bed, and be clear on which meetings are recorded for later viewing.

Upset stomaches. One early meeting every two weeks doesn't sound too bad. The worst problem is that it can leave me with an upset stomach that can last for days. I'm still working fine, it's just uncomfortable. I don't know if other people suffer this or why it happens. Maybe it's just the extra coffee.

Fewer sick days. I don't normally take many sick days, so I can't say my data is very significant. FWIW: In my last in-person job I averaged 1.5 sick days/year (9 in 6 years), and now it's 0.33 (1 in 3 years).

Northen/Southern hemisphere times get weird. Daylight savings moves in different directions and on different dates, so from Sydney depending on the time of year I have 3, 4, or 5 hours of overlap with 9-5pm on the US west coast. To make it easy for people I setup my calendar with my work hours highlighted.

Saturday? Saturday morning is Friday afternoon in the US. For a while I tried working Tuesday to Saturday, but it's better for family time if I finish early on Saturday (at US 5pm) and work Monday to make up the difference.


Phone alarms

Career limiting I've experienced it to be career limiting, and I've heard the saying "out of sight, out of mind." Opportunities can be given to local workers despite the remote worker being more qualified, or even number one in the world in that area. The remote worker is then expected to teach the local worker in weekly or monthly meetings. It's not enough time and there's a power imbalance: the local worker is in charge and doesn't have to listen. This reduces company competitiveness. It's also fixable: try giving remote workers the chance they are best qualified to do.

Compared to office work. It depends on the job and meeting load. In my last years as an in-person office worker I was on a team where we typically worked solo on different projects, with headphones on, and primarily communicated over chatrooms with few in-person meetings. The only regular time we had human interaction was lunch. Remote work has not been a big difference to that kind of job: similar work, similar chatrooms. (The thing I miss the most about the office is playing cricket with other staff after work.) It wouldn't make a big difference to my current job either, as the people I work with are scattered. The one thing it would solve is "out of sight" problem, but that's not the only way to solve it.

Remote work success stories. Linux development is a great example of engineers around the world working together. Note that Linux engineers do try to meet at least once a year at one of the Linux conferences, something remote office workers can do as well at company get-togethers. My books are another example: they are out-of-hours projects where I worked with the reviewers online, some of whom I still haven't met. And the world's first AI Flame Graphs is the work of a fully-remote team.

I was in a video meeting recently with US staff where I mentioned the "77 meetings between 1 and 6am" statistic, and I could see the look of shock in their faces. Did they assume I was just working 9-5 and not making an effort to accommodate other timezones? I know some companies are discussing ending remote-work: are the deciders also making such assumptions about the lives of remote workers? It may not do much, but I can at least share my own details here as an example anecdote.

Update: Some comments about this post have pointed out that these hours can be unhealthy and shouldn't be encouraged, and I don't disagree. I hope to have fewer early meetings in the years ahead, myself. This post is just to show that remote workers can be more accomodating than people may assume, and I hope that's taken into consideration when changing remote work policies.

When some people hear I'm working from Australia, they may think of beaches and surfboards and sunny vacations, but my reality is a full time job across weird hours, lots of coffee, and being overlooked for opportunities. That said, every job has its pros and cons, and I'm still grateful that some companies make fully remote work an option.

Linux Plumbers Conference: Submission time for Linux Plumbers 2025

Kernel Planet
3 hónap 1 hét óta

Submissions for the Refereed Track and Microconferences are now open. Linux Plumbers will be held this year in Tokyo from December 11th – 13th (Note, the 13th is on a Saturday).

The Refereed presentations are 45 minutes in length and should focus on a specific aspect of the “plumbing” in a Linux ecosystem. Examples of Linux plumbing include core kernel subsystems, init systems, core libraries, toolchains, windowing systems, management tools, device support, media creation/playback, testing, and so on. The best presentations are not about finished work, but rather problem statements, proposals, or proof-of-concept solutions that require face-to-face discussions and debate.

The Microconferences are 3 and a half hours of technical discussion, broken up into 15 to 30 minute subtopics. The only presentations allowed are those that are needed to bring the audience up to speed and should not last more than half the allotted time for the subtopic. To submit a Microconference, provide a topic, some examples of subtopics to be discussed and a list of key people that should be present to have meaningful discussions. For Microconferences that have been to Linux Plumbers in the past, they should provide a list of accomplishments that were a direct result of the discussions from their previous sessions (with links to patches and such).

Presentations and Microconference subtopic leads should ideally be physically present at the conference. Remote presentations may be available but are strongly discouraged.

The Refereed submissions end at 11:59PM UTC on Wednesday, September 10, 2025.
The Microconference submissions end at 11:59PM UTC on Sunday, June 29, 2025.

Go ahead and submit your Refereed track presentation or Microconference topic. We are looking forward to seeing the great content that is submitted that makes Linux Plumbers the best technical conference there is.

Brendan Gregg: Doom GPU Flame Graphs

Kernel Planet
3 hónap 3 hét óta

AI Flame Graphs are now open source and include Intel Battlemage GPU support, which means it can also generate full-stack GPU flame graphs for providing new insights into gaming performance, especially when coupled with FlameScope (an older open source project of mine). Here's an example of GZDoom, and I'll start with flame scopes for both CPU and GPU utilization, with details annotated:

(Here are the raw CPU and GPU versions.) FlameScope shows a subsecond-offset heatmap of profile samples, where each column is one second (in this example, made up of 50 x 20ms blocks) and the color depth represents the number of samples, revealing variance and perturbation that you can select to generate a flame graph just for that time range. Update: the row size can be ajusted (it is limited by the sample rate captured in the profile), e.g., you could generate 60 rows to match 60fps games.

Putting these CPU and GPU flame scopes side by side has enabled your eyes to do pattern matching to solve what would otherwise be a time-consuming task of performance correlation. The gaps in the GPU flame scope on the right – where the GPU was not doing much work – match the heavier periods of CPU work on the left.

CPU Analysis

FlameScope lets us click on the interesting periods. By selecting one of the CPU shader compilation stripes we get the flame graph just for that range:

This is brilliant, and we can see exactly why the CPUs were busy for about 180 ms (the vertical length of the red stripe): it's doing compilation of GPU shaders and some NIR preprocessing (optimizations to the NIR intermediate representation that Mesa uses internally). If you are new to flame graphs, you look for the widest towers and optimize them first. Here is the interactive SVG.

CPU flame graphs and CPU flame scope aren't new (from 2011 and 2018, both open source). What is new is full-stack GPU flame graphs and GPU flame scope.

GPU Analysis

Interesting details can also be selected in the GPU FlameScope for generating GPU flame graphs. This example selects the "room 3" range, which is a room in the Doom map that contains hundreds of enemies. The green frames are the actual instructions running on the GPU, aqua shows the source for these functions, and red (C) and yellow (C++) show the CPU code paths that initiated the GPU programs. The gray "-" frames just help highlight the boundary between CPU and GPU code. (This is similar to what I described in the AI flame graphs post, which included extra frames for kernel code.) The x-axis is proportional to cost, so you look for the widest things and find ways to reduce them.

I've included the interactive SVG version of this flame graph so you can mouse-over elements and click to zoom. (PNG version.)

The GPU flame graph is split between stalls coming from rendering walls (41.4%), postprocessing effects (35.7%), stenciling (17.2%), and sprites (4.95%). The CPU stacks are further differentiated by the individual shaders that are causing stalls, along with the reasons for those stalls.

GZDoom

We picked GZDoom to try since it's an open source version of a well known game that runs on Linux (our profiler does not support Windows yet). Intel Battlemage makes light work of GZDoom, however, and since the GPU profile is stall-based we weren't getting many samples. We could have switched to a more modern and GPU-demanding game, but didn't have any great open source ideas, so I figured we'd just make GZDoom more demanding. We built GPU demanding maps for GZDoom (I can't believe I have found a work-related reason to be using Slade), and also set some Battlemage tunables to limit resources, magnifying the utilization of remaining resources.

Our GZDoom test map has three rooms: room 1 is empty, room 2 is filled with torches, and room 3 is open with a large skybox and filled with enemies, including spawnpoints for Sergeants. This gave us a few different workloads to examine by walking between the rooms.

Using iaprof: Intel's open source accelerator profiler

The AI Flame Graph project is pioneering work, and has needed various changes to graphics compilers, libraries, and kernel drivers, not just the code but also how they are built. Since Intel has its own public cloud (the Intel® Tiber™ AI Cloud) we can fix the software stack in advance so that for customers it "just works." Check the available releases. It currently supports the Intel Max Series GPU.

If you aren't on the Intel cloud, or you wish to try this with Intel Battlemage, then it can require a lot of work to get the system ready to be profiled. Requirements include:

  • A Linux system with superuser (root) access, so that eBPF and Intel eustalls can be used.
  • A newer Linux kernel with the latest Intel GPU drivers. For Intel Battlemage this means Linux 6.15+ with the Xe driver; For the Intel Max Series GPU it's Linux 5.15 with the i915 driver.
  • The Linux kernel built with Intel driver-specific eustall and eudebug interfaces (see the github docs for details). Some of these modifications are upstreamed in the latest versions of Linux and others are currently in progress. (These interfaces are made available by default on the Intel® Tiber™ AI Cloud.)
  • All system libraries or programs that are being profiled need to include frame pointers so that the full stacks are visible, including Intel's oneAPI and graphics libraries. For this example, GZDoom itself needed to be compiled with frame pointers and also all libraries used by GZDoom (glibc, etc.). This is getting easier in the lastest versions of Fedora and Ubuntu (e.g., Ubuntu 24.04 LTS) which are shipping system libraries with frame pointers by default. But I'd expect there will be applications and dependencies that don't have frame pointers yet, and need recompilation. If your flame graph has areas that are very short, one or two frames deep, this is why.

If you are new to custom kernel builds and library tinkering, then getting this all working may feel like Nightmare! difficulty. Over time things will improve and gradually get easier: check the github docs. Intel can also develop a much easier version of this tool as part of a broader product offering and get it working on more than just Linux and Battlemage (either watch this space or, if you have an Intel rep, ask them to make it a priority).

Once you have it all working, you can run the iaprof command to profile the GPU. E.g.:

git clone --recursive https://github.com/intel/iaprof cd iaprof make deps make sudo iaprof record > profile.txt cat profile.txt | iaprof flame > flame.svg

iaprof is modeled on the Linux perf command. (Maybe one day it'll become included in perf directly.) Thanks to Gabriel Muñoz for getting the work done to get this open sourced.

FAQ and Future Work

From the launch of AI flame graphs last year, I can guess what FAQ #1 will be: “What about NVIDIA?”. They do have flame graphs in Nsight Graphics for GPU workloads, although their flame graphs are currently shallow as it is GPU code only, and onerous to use as I believe it requires an interposer; on the plus side they have click-to-source. The new GPU profiling method we've been developing allows for easy, everything, anytime profiling, like you expect from CPU profilers.

Future work will include github releases, more hardware support, and overhead reduction. We're the first to use eustalls in this way, and we need to add more optimization to reach our target of <5% overhead, especially with the i915 driver.

Conclusion

We've open sourced AI flame graphs and tested it on new hardware, Intel Battlemage, and a non-AI workload: GZDoom (gaming). It's great to see a view of both CPU and GPU resources down to millisecond resolution, where we can see visual patterns in the flame scope heat maps that can be selected to produce flame graphs to show the code. We applied these new tools to GZDoom and explained GPU pauses by selecting the corresponding CPU burst and reading the flame graph, as well as GPU code use for arbitrary time windows.

While we have open sourced this, getting it all running requires Intel hardware and Linux kernel and library tinkering – which can be a lot of work. (Actually playing Doom on Nightmare! difficulty may be easier.) This will get better over time. We look forward to seeing if anyone can fight their way through this work in the meantime and what new performance issues they can solve.

Authors: Brendan Gregg, Ben Olson, Brandon Kammerdiener, Gabriel Muñoz.

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