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English version follows. Aujourd’hui, Khronos Group a sorti la spécification 1.4 de l’API graphique standard Vulkan. Le projet Asahi Linux est fier d’annoncer le premier pilote Vulkan 1.4 pour le matériel d’Apple. En effet, notre pilote graphique Honeykrisp est reconnu par Khronos comme conforme à cette nouvelle version dès aujourd’hui. Ce pilote est déjà disponible dans nos dépôts officiels. Après avoir installé Fedora Asahi Remix, executez dnf upgrade --refresh pour obtenir la dernière version du pilote. Vulkan 1.4 standardise plusieurs fonctionnalités importantes, y compris les horodatages et la lecture locale avec le rendu dynamique. L’industrie suppose que ces fonctionnalités devront être plus courantes, et nous y sommes préparés. Sortir un pilote conforme reflète notre engagement en faveur des standards graphiques et du logiciel libre. Asahi Linux est aussi compatible avec OpenGL 4.6, OpenGL ES 3.2, et OpenCL 3.0, tous conformes aux spécifications pertinentes. D’ailleurs, les notres sont les seules pilotes conformes pour le materiel d’Apple de n’importe quel standard graphique. Même si le pilote est sorti, il faut encore compiler une version expérimentale de Vulkan-Loader pour accéder à la nouvelle version de Vulkan. Toutes les nouvelles fonctionnalités sont néanmoins disponsibles comme extensions à notre pilote Vulkan 1.3 pour en profiter tout de suite. Pour plus d’informations, consultez l’article de blog de Khronos. Today, the Khronos Group released the 1.4 specification of Vulkan, the standard graphics API. The Asahi Linux project is proud to announce the first Vulkan 1.4 driver for Apple hardware. Our Honeykrisp driver is Khronos-recognized as conformant to the new version since day one. That driver is already available in our official repositories. After installing Fedora Asahi Remix, run dnf upgrade --refresh to get the latest drivers. Vulkan 1.4 standardizes several important features, including timestamps and dynamic rendering local read. The industry expects that these features will become more common, and we are prepared. Releasing a conformant driver reflects our commitment to graphics standards and software freedom. Asahi Linux is also compatible with OpenGL 4.6, OpenGL ES 3.2, and OpenCL 3.0, all conformant to the relevant specifications. For that matter, ours are the only conformant drivers on Apple hardware for any graphics standard graphics. Although the driver is released, you still need to build an experimental version of Vulkan-Loader to access the new Vulkan version. Nevertheless, you can immediately use all the new features as extensions in Vulkan 1.3 driver. For more information, see the Khronos blog post.
Gaming on Linux on M1 is here! We’re thrilled to release our Asahi game playing toolkit, which integrates our Vulkan 1.3 drivers with x86 emulation and Windows compatibility. Plus a bonus: conformant OpenCL 3.0. Asahi Linux now ships the only conformant OpenGL®, OpenCL™, and Vulkan® drivers for this hardware. As for gaming… while today’s release is an alpha, Control runs well! Installation First, install Fedora Asahi Remix. Once installed, get the latest drivers with dnf upgrade --refresh && reboot. Then just dnf install steam and play. While all M1/M2-series systems work, most games require 16GB of memory due to emulation overhead. The stack Games are typically x86 Windows binaries rendering with DirectX, while our target is Arm Linux with Vulkan. We need to handle each difference: FEX emulates x86 on Arm. Wine translates Windows to Linux. DXVK and vkd3d-proton translate DirectX to Vulkan. There’s one curveball: page size. Operating systems allocate memory in fixed size “pages”. If an application expects smaller pages than the system uses, they will break due to insufficient alignment of allocations. That’s a problem: x86 expects 4K pages but Apple systems use 16K pages. While Linux can’t mix page sizes between processes, it can virtualize another Arm Linux kernel with a different page size. So we run games inside a tiny virtual machine using muvm, passing through devices like the GPU and game controllers. The hardware is happy because the system is 16K, the game is happy because the virtual machine is 4K, and you’re happy because you can play Fallout 4. Vulkan The final piece is an adult-level Vulkan driver, since translating DirectX requires Vulkan 1.3 with many extensions. Back in April, I wrote Honeykrisp, the only Vulkan 1.3 driver for Apple hardware. I’ve since added DXVK support. Let’s look at some new features. Tessellation Tessellation enables games like The Witcher 3 to generate geometry. The M1 has hardware tessellation, but it is too limited for DirectX, Vulkan, or OpenGL. We must instead tessellate with arcane compute shaders, as detailed in today’s talk at XDC2024. Geometry shaders Geometry shaders are an older, cruder method to generate geometry. Like tessellation, the M1 lacks geometry shader hardware so we emulate with compute. Is that fast? No, but geometry shaders are slow even on desktop GPUs. They don’t need to be fast – just fast enough for games like Ghostrunner. Enhanced robustness “Robustness” permits an application’s shaders to access buffers out-of-bounds without crashing the hardware. In OpenGL and Vulkan, out-of-bounds loads may return arbitrary elements, and out-of-bounds stores may corrupt the buffer. Our OpenGL driver exploits this definition for efficient robustness on the M1. Some games require stronger guarantees. In DirectX, out-of-bounds loads return zero, and out-of-bounds stores are ignored. DXVK therefore requires VK_EXT_robustness2, a Vulkan extension strengthening robustness. Like before, we implement robustness with compare-and-select instructions. A naïve implementation would compare a loaded index with the buffer size and select a zero result if out-of-bounds. However, our GPU loads are vector while arithmetic is scalar. Even if we disabled page faults, we would need up to four compare-and-selects per load. load R, buffer, index * 16 ulesel R[0], index, size, R[0], 0 ulesel R[1], index, size, R[1], 0 ulesel R[2], index, size, R[2], 0 ulesel R[3], index, size, R[3], 0 There’s a trick: reserve 64 gigabytes of zeroes using virtual memory voodoo. Since every 32-bit index multiplied by 16 fits in 64 gigabytes, any index into this region loads zeroes. For out-of-bounds loads, we simply replace the buffer address with the reserved address while preserving the index. Replacing a 64-bit address costs just two 32-bit compare-and-selects. ulesel buffer.lo, index, size, buffer.lo, RESERVED.lo ulesel buffer.hi, index, size, buffer.hi, RESERVED.hi load R, buffer, index * 16 Two instructions, not four. Next steps Sparse texturing is next for Honeykrisp, which will unlock more DX12 games. The alpha already runs DX12 games that don’t require sparse, like Cyberpunk 2077. While many games are playable, newer AAA titles don’t hit 60fps yet. Correctness comes first. Performance improves next. Indie games like Hollow Knight do run full speed. Beyond gaming, we’re adding general purpose x86 emulation based on this stack. For more information, see the FAQ. Today’s alpha is a taste of what’s to come. Not the final form, but enough to enjoy Portal 2 while we work towards “1.0”. Acknowledgements This work has been years in the making with major contributions from… Alyssa Rosenzweig Asahi Lina chaos_princess Davide Cavalca Dougall Johnson Ella Stanforth Faith Ekstrand Janne Grunau Karol Herbst marcan Mary Guillemard Neal Gompa Sergio López TellowKrinkle Teoh Han Hui Rob Clark Ryan Houdek … Plus hundreds of developers whose work we build upon, spanning the Linux, Mesa, Wine, and FEX projects. Today’s release is thanks to the magic of open source. We hope you enjoy the magic. Happy gaming.
For years, the M1 has only supported OpenGL 4.1. That changes today – with our release of full OpenGL® 4.6 and OpenGL® ES 3.2! Install Fedora for the latest M1/M2-series drivers. Already installed? Just dnf –refresh upgrade. Unlike the vendor’s non-conformant 4.1 drivers, our open source Linux drivers are conformant to the latest OpenGL versions, finally promising broad compatibility with modern OpenGL workloads, like Blender, Ryujinx, and Citra. Conformant 4.6/3.2 drivers must pass over 100,000 tests to ensure correctness. The official list of conformant drivers now includes our OpenGL 4.6 and ES 3.2. While the vendor doesn’t yet support graphics standards like modern OpenGL, we do. For this Valentine’s Day, we want to profess our love for interoperable open standards. We want to free users and developers from lock-in, enabling applications to run anywhere the heart wants without special ports. For that, we need standards conformance. Six months ago, we became the first conformant driver for any standard graphics API for the M1 with the release of OpenGL ES 3.1 drivers. Today, we’ve finished OpenGL with the full 4.6… and we’re well on the road to Vulkan. Compared to 4.1, OpenGL 4.6 adds dozens of required features, including: Robustness SPIR-V Clip control Cull distance Compute shaders Upgraded transform feedback Regrettably, the M1 doesn’t map well to any graphics standard newer than OpenGL ES 3.1. While Vulkan makes some of these features optional, the missing features are required to layer DirectX and OpenGL on top. No existing solution on M1 gets past the OpenGL 4.1 feature set. How do we break the 4.1 barrier? Without hardware support, new features need new tricks. Geometry shaders, tessellation, and transform feedback become compute shaders. Cull distance becomes a transformed interpolated value. Clip control becomes a vertex shader epilogue. The list goes on. For a taste of the challenges we overcame, let’s look at robustness. Built for gaming, GPUs traditionally prioritize raw performance over safety. Invalid application code, like a shader that reads a buffer out-of-bounds, can trigger undefined behaviour. Drivers exploit that to maximize performance. For applications like web browsers, that trade-off is undesirable. Browsers handle untrusted shaders, which they must sanitize to ensure stability and security. Clicking a malicious link should not crash the browser. While some sanitization is necessary as graphics APIs are not security barriers, reducing undefined behaviour in the API can assist “defence in depth”. “Robustness” features can help. Without robustness, out-of-bounds buffer access in a shader can crash. With robustness, the application can opt for defined out-of-bounds behaviour, trading some performance for less attack surface. All modern cross-vendor APIs include robustness. Many games even (accidentally?) rely on robustness. Strangely, the vendor’s proprietary API omits buffer robustness. We must do better for conformance, correctness, and compatibility. Let’s first define the problem. Different APIs have different definitions of what an out-of-bounds load returns when robustness is enabled: Zero (Direct3D, Vulkan with robustBufferAccess2) Either zero or some data in the buffer (OpenGL, Vulkan with robustBufferAccess) Arbitrary values, but can’t crash (OpenGL ES) OpenGL uses the second definition: return zero or data from the buffer. One approach is to return the last element of the buffer for out-of-bounds access. Given the buffer size, we can calculate the last index. Now consider the minimum of the index being accessed and the last index. That equals the index being accessed if it is valid, and some other valid index otherwise. Loading the minimum index is safe and gives a spec-compliant result. As an example, a uniform buffer load without robustness might look like: load.i32 result, buffer, index Robustness adds a single unsigned minimum (umin) instruction: umin idx, index, last load.i32 result, buffer, idx Is the robust version slower? It can be. The difference should be small percentage-wise, as arithmetic is faster than memory. With thousands of threads running in parallel, the arithmetic cost may even be hidden by the load’s latency. There’s another trick that speeds up robust uniform buffers. Like other GPUs, the M1 supports “preambles”. The idea is simple: instead of calculating the same value in every thread, it’s faster to calculate once and reuse the result. The compiler identifies eligible calculations and moves them to a preamble executed before the main shader. These redundancies are common, so preambles provide a nice speed-up. We usually move uniform buffer loads to the preamble when every thread loads the same index. Since the size of a uniform buffer is fixed, extra robustness arithmetic is also moved to the preamble. The robustness is “free” for the main shader. For robust storage buffers, the clamping might move to the preamble even if the load or store cannot. Armed with robust uniform and storage buffers, let’s consider robust “vertex buffers”. In graphics APIs, the application can set vertex buffers with a base GPU address and a chosen layout of “attributes” within each buffer. Each attribute has an offset and a format, and the buffer has a “stride” indicating the number of bytes per vertex. The vertex shader can then read attributes, implicitly indexing by the vertex. To do so, the shader loads the address: Some hardware implements robust vertex fetch natively. Other hardware has bounds-checked buffers to accelerate robust software vertex fetch. Unfortunately, the M1 has neither. We need to implement vertex fetch with raw memory loads. One instruction set feature helps. In addition to a 64-bit base address, the M1 GPU’s memory loads also take an offset in elements. The hardware shifts the offset and adds to the 64-bit base to determine the address to fetch. Additionally, the M1 has a combined integer multiply-add instruction imad. Together, these features let us implement vertex loads in two instructions. For example, a 32-bit attribute load looks like: imad idx, stride/4, vertex, offset/4 load.i32 result, base, idx The hardware load can perform an additional small shift. Suppose our attribute is a vector of 4 32-bit values, densely packed into a buffer with no offset. We can load that attribute in one instruction: load.v4i32 result, base, vertex << 2 …with the hardware calculating the address: What about robustness? We want to implement robustness with a clamp, like we did for uniform buffers. The problem is that the vertex buffer size is given in bytes, while our optimized load takes an index in “vertices”. A single vertex buffer can contain multiple attributes with different formats and offsets, so we can’t convert the size in bytes to a size in “vertices”. Let’s handle the latter problem. We can rewrite the addressing equation as: That is: one buffer with many attributes at different offsets is equivalent to many buffers with one attribute and no offset. This gives an alternate perspective on the same data layout. Is this an improvement? It avoids an addition in the shader, at the cost of passing more data – addresses are 64-bit while attribute offsets are 16-bit. More importantly, it lets us translate the vertex buffer size in bytes into a size in “vertices” for each vertex attribute. Instead of clamping the offset, we clamp the vertex index. We still make full use of the hardware addressing modes, now with robustness: umin idx, vertex, last valid load.v4i32 result, base, idx << 2 We need to calculate the last valid vertex index ahead-of-time for each attribute. Each attribute has a format with a particular size. Manipulating the addressing equation, we can calculate the last byte accessed in the buffer (plus 1) relative to the base: The load is valid when that value is bounded by the buffer size in bytes. We solve the integer inequality as: The driver calculates the right-hand side and passes it into the shader. One last problem: what if a buffer is too small to load anything? Clamping won’t save us – the code would clamp to a negative index. In that case, the attribute is entirely invalid, so we swap the application’s buffer for a small buffer of zeroes. Since we gave each attribute its own base address, this determination is per-attribute. Then clamping the index to zero correctly loads zeroes. Putting it together, a little driver math gives us robust buffers at the cost of one umin instruction. In addition to buffer robustness, we need image robustness. Like its buffer counterpart, image robustness requires that out-of-bounds image loads return zero. That formalizes a guarantee that reasonable hardware already makes. …But it would be no fun if our hardware was reasonable. Running the conformance tests for image robustness, there is a single test failure affecting “mipmapping”. For background, mipmapped images contain multiple “levels of detail”. The base level is the original image; each successive level is the previous level downscaled. When rendering, the hardware selects the level closest to matching the on-screen size, improving efficiency and visual quality. With robustness, the specifications all agree that image loads return… Zero if the X- or Y-coordinate is out-of-bounds Zero if the level is out-of-bounds Meanwhile, image loads on the M1 GPU return… Zero if the X- or Y-coordinate is out-of-bounds Values from the last level if the level is out-of-bounds Uh-oh. Rather than returning zero for out-of-bounds levels, the hardware clamps the level and returns nonzero values. It’s a mystery why. The vendor does not document their hardware publicly, forcing us to rely on reverse engineering to build drivers. Without documentation, we don’t know if this behaviour is intentional or a hardware bug. Either way, we need a workaround to pass conformance. The obvious workaround is to never load from an invalid level: if (level <= levels) { return imageLoad(x, y, level); } else { return 0; } That involves branching, which is inefficient. Loading an out-of-bounds level doesn’t crash, so we can speculatively load and then use a compare-and-select operation instead of branching: vec4 data = imageLoad(x, y, level); return (level <= levels) ? data : 0; This workaround is okay, but it could be improved. While the M1 GPU has combined compare-and-select instructions, the instruction set is scalar. Each thread processes one value at a time, not a vector of multiple values. However, image loads return a vector of four components (red, green, blue, alpha). While the pseudo-code looks efficient, the resulting assembly is not: image_load R, x, y, level ulesel R[0], level, levels, R[0], 0 ulesel R[1], level, levels, R[1], 0 ulesel R[2], level, levels, R[2], 0 ulesel R[3], level, levels, R[3], 0 Fortunately, the vendor driver has a trick. We know the hardware returns zero if either X or Y is out-of-bounds, so we can force a zero output by setting X or Y out-of-bounds. As the maximum image size is 16384 pixels wide, any X greater than 16384 is out-of-bounds. That justifies an alternate workaround: bool valid = (level <= levels); int x_ = valid ? x : 20000; return imageLoad(x_, y, level); Why is this better? We only change a single scalar, not a whole vector, compiling to compact scalar assembly: ulesel x_, level, levels, x, #20000 image_load R, x_, y, level If we preload the constant to a uniform register, the workaround is a single instruction. That’s optimal – and it passes conformance. Blender “Wanderer” demo by Daniel Bystedt, licensed CC BY-SA.
Conformant OpenGL® ES 3.1 drivers are now available for M1- and M2-family GPUs. That means the drivers are compatible with any OpenGL ES 3.1 application. Interested? Just install Linux! For existing Asahi Linux users, upgrade your system with dnf upgrade (Fedora) or pacman -Syu (Arch) for the latest drivers. Our reverse-engineered, free and open source graphics drivers are the world’s only conformant OpenGL ES 3.1 implementation for M1- and M2-family graphics hardware. That means our driver passed tens of thousands of tests to demonstrate correctness and is now recognized by the industry. To become conformant, an “implementation” must pass the official conformance test suite, designed to verify every feature in the specification. The test results are submitted to Khronos, the standards body. After a 30-day review period, if no issues are found, the implementation becomes conformant. The Khronos website lists all conformant implementations, including our drivers for the M1, M1 Pro/Max/Ultra, M2, and M2 Pro/Max. Today’s milestone isn’t just about OpenGL ES. We’re releasing the first conformant implementation of any graphics standard for the M1. And we don’t plan to stop here ;-) Unlike ours, the manufacturer’s M1 drivers are unfortunately not conformant for any standard graphics API, whether Vulkan or OpenGL or OpenGL ES. That means that there is no guarantee that applications using the standards will work on your M1/M2 (if you’re not running Linux). This isn’t just a theoretical issue. Consider Vulkan. The third-party MoltenVK layers a subset of Vulkan on top of the proprietary drivers. However, those drivers lack key functionality, breaking valid Vulkan applications. That hinders developers and users alike, if they haven’t yet switched their M1/M2 computers to Linux. Why did we pursue standards conformance when the manufacturer did not? Above all, our commitment to quality. We want our users to know that they can depend on our Linux drivers. We want standard software to run without M1-specific hacks or porting. We want to set the right example for the ecosystem: the way forward is implementing open standards, conformant to the specifications, without compromises for “portability”. We are not satisfied with proprietary drivers, proprietary APIs, and refusal to implement standards. The rest of the industry knows that progress comes from cross-vendor collaboration. We know it, too. Achieving conformance is a win for our community, for open source, and for open graphics. Of course, Asahi Lina and I are two individuals with minimal funding. It’s a little awkward that we beat the big corporation… It’s not too late though. They should follow our lead! OpenGL ES 3.1 updates the experimental OpenGL ES 3.0 and OpenGL 3.1 we shipped in June. Notably, ES 3.1 adds compute shaders, typically used to accelerate general computations within graphics applications. For example, a 3D game could run its physics simulations in a compute shader. The simulation results can then be used for rendering, eliminating stalls that would otherwise be required to synchronize the GPU with a CPU physics simulation. That lets the game run faster. Let’s zoom in on one new feature: atomics on images. Older versions of OpenGL ES allowed an application to read an image in order to display it on screen. ES 3.1 allows the application to write to the image, typically from a compute shader. This new feature enables flexible image processing algorithms, which previously needed to fit into the fixed-function 3D pipeline. However, GPUs are massively parallel, running thousands of threads at the same time. If two threads write to the same location, there is a conflict: depending which thread runs first, the result will be different. We have a race condition. “Atomic” access to memory provides a solution to race conditions. With atomics, special hardware in the memory subsystem guarantees consistent, well-defined results for select operations, regardless of the order of the threads. Modern graphics hardware supports various atomic operations, like addition, serving as building blocks to complex parallel algorithms. Can we put these two features together to write to an image atomically? Yes. A ubiquitous OpenGL ES extension, required for ES 3.2, adds atomics operating on pixels in an image. For example, a compute shader could atomically increment the value at pixel (10, 20). Other GPUs have dedicated instructions to perform atomics on an images, making the driver implementation straightforward. For us, the story is more complicated. The M1 lacks hardware instructions for image atomics, even though it has non-image atomics and non-atomic images. We need to reframe the problem. The idea is simple: to perform an atomic on a pixel, we instead calculate the address of the pixel in memory and perform a regular atomic on that address. Since the hardware supports regular atomics, our task is “just” calculating the pixel’s address. If the image were laid out linearly in memory, this would be straightforward: multiply the Y-coordinate by the number of bytes per row (“stride”), multiply the X-coordinate by the number of bytes per pixel, and add. That gives the pixel’s offset in bytes relative to the first pixel of the image. To get the final address, we add that offset to the address of the first pixel. Alas, images are rarely linear in memory. To improve cache efficiency, modern graphics hardware interleaves the X- and Y-coordinates. Instead of one row after the next, pixels in memory follow a spiral-like curve. We need to amend our previous equation to interleave the coordinates. We could use many instructions to mask one bit at a time, shifting to construct the interleaved result, but that’s inefficient. We can do better. There is a well-known “bit twiddling” algorithm to interleave bits. Rather than shuffle one bit at a time, the algorithm shuffles groups of bits, parallelizing the problem. Implementing this algorithm in shader code improves performance. In practice, only the lower 7-bits (or less) of each coordinate are interleaved. That lets us use 32-bit instructions to “vectorize” the interleave, by putting the X- and Y-coordinates in the low and high 16-bits of a 32-bit register. Those 32-bit instructions let us interleave X and Y at the same time, halving the instruction count. Plus, we can exploit the GPU’s combined shift-and-add instruction. Putting the tricks together, we interleave in 10 instructions of M1 GPU assembly: # Inputs x, y in r0l, r0h. # Output in r1. add r2, #0, r0, lsl 4 or r1, r0, r2 and r1, r1, #0xf0f0f0f add r2, #0, r1, lsl 2 or r1, r1, r2 and r1, r1, #0x33333333 add r2, #0, r1, lsl 1 or r1, r1, r2 and r1, r1, #0x55555555 add r1, r1l, r1h, lsl 1 We could stop here, but what if there’s a dedicated instruction to interleave bits? PowerVR has a “shuffle” instruction shfl, and the M1 GPU borrows from PowerVR. Perhaps that instruction was borrowed too. Unfortunately, even if it was, the proprietary compiler won’t use it when compiling our test shaders. That makes it difficult to reverse-engineer the instruction – if it exists – by observing compiled shaders. It’s time to dust off a powerful reverse-engineering technique from magic kindergarten: guess and check. Dougall Johnson provided the guess. When considering the instructions we already know about, he took special notice of the “reverse bits” instruction. Since reversing bits is a type of bit shuffle, the interleave instruction should be encoded similarly. The bit reverse instruction has a two-bit field specifying the operation, with value 01. Related instructions to count the number of set bits and find the first set bit have values 10 and 11 respectively. That encompasses all known “complex bit manipulation” instructions. tr:first-child > td:nth-child(2) { text-align:center !important } td > strong > a:visited { color: #0000EE } 00 ? ? ? 01 Reverse bits 10 Count set bits 11 Find first set There is one value of the two-bit enumeration that is unobserved and unknown: 00. If this interleave instruction exists, it’s probably encoded like the bit reverse but with operation code 00 instead of 01. There’s a difficulty: the three known instructions have one single input source, but our instruction interleaves two sources. Where does the second source go? We can make a guess based on symmetry. Presumably to simplify the hardware decoder, M1 GPU instructions usually encode their sources in consistent locations across instructions. The other three instructions have a gap where we would expect the second source to be, in a two-source arithmetic instruction. Probably the second source is there. Armed with a guess, it’s our turn to check. Rather than handwrite GPU assembly, we can hack our compiler to replace some two-source integer operation (like multiply) with our guessed encoding of “interleave”. Then we write a compute shader using this operation (by “multiplying” numbers) and run it with the newfangled compute support in our driver. All that’s left is writing a shader that checks that the mystery instruction returns the interleaved result for each possible input. Since the instruction takes two 16-bit sources, there are about 4 billion (\(2^32\)) inputs. With our driver, the M1 GPU manages to check them all in under a second, and the verdict is in: this is our interleave instruction. As for our clever vectorized assembly to interleave coordinates? We can replace it with one instruction. It’s anticlimactic, but it’s fast and it passes the conformance tests. And that’s what matters. Thank you to Khronos and Software in the Public Interest for supporting open drivers.
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I like the job title “Design Engineer”. When required to label myself, I feel partial to that term (I should, I’ve written about it enough). Lately I’ve felt like the term is becoming more mainstream which, don’t get me wrong, is a good thing. I appreciate the diversification of job titles, especially ones that look to stand in the middle between two binaries. But — and I admit this is a me issue — once a title starts becoming mainstream, I want to use it less and less. I was never totally sure why I felt this way. Shouldn’t I be happy a title I prefer is gaining acceptance and understanding? Do I just want to rebel against being labeled? Why do I feel this way? These were the thoughts simmering in the back of my head when I came across an interview with the comedian Brian Regan where he talks about his own penchant for not wanting to be easily defined: I’ve tried over the years to write away from how people are starting to define me. As soon as I start feeling like people are saying “this is what you do” then I would be like “Alright, I don't want to be just that. I want to be more interesting. I want to have more perspectives.” [For example] I used to crouch around on stage all the time and people would go “Oh, he’s the guy who crouches around back and forth.” And I’m like, “I’ll show them, I will stand erect! Now what are you going to say?” And then they would go “You’re the guy who always feels stupid.” So I started [doing other things]. He continues, wondering aloud whether this aversion to not being easily defined has actually hurt his career in terms of commercial growth: I never wanted to be something you could easily define. I think, in some ways, that it’s held me back. I have a nice following, but I’m not huge. There are people who are huge, who are great, and deserve to be huge. I’ve never had that and sometimes I wonder, ”Well maybe it’s because I purposely don’t want to be a particular thing you can advertise or push.” That struck a chord with me. It puts into words my current feelings towards the job title “Design Engineer” — or any job title for that matter. Seven or so years ago, I would’ve enthusiastically said, “I’m a Design Engineer!” To which many folks would’ve said, “What’s that?” But today I hesitate. If I say “I’m a Design Engineer” there are less follow up questions. Now-a-days that title elicits less questions and more (presumed) certainty. I think I enjoy a title that elicits a “What’s that?” response, which allows me to explain myself in more than two or three words, without being put in a box. But once a title becomes mainstream, once people begin to assume they know what it means, I don’t like it anymore (speaking for myself, personally). As Brian says, I like to be difficult to define. I want to have more perspectives. I like a title that befuddles, that doesn’t provide a presumed sense of certainty about who I am and what I do. And I get it, that runs counter to the very purpose of a job title which is why I don’t think it’s good for your career to have the attitude I do, lol. I think my own career evolution has gone something like what Brian describes: Them: “Oh you’re a Designer? So you make mock-ups in Photoshop and somebody else implements them.” Me: “I’ll show them, I’ll implement them myself! Now what are you gonna do?” Them: “Oh, so you’re a Design Engineer? You design and build user interfaces on the front-end.” Me: “I’ll show them, I’ll write a Node server and setup a database that powers my designs and interactions on the front-end. Now what are they gonna do?” Them: “Oh, well, we I’m not sure we have a term for that yet, maybe Full-stack Design Engineer?” Me: “Oh yeah? I’ll frame up a user problem, interface with stakeholders, explore the solution space with static designs and prototypes, implement a high-fidelity solution, and then be involved in testing, measuring, and refining said solution. What are you gonna call that?” [As you can see, I have some personal issues I need to work through…] As Brian says, I want to be more interesting. I want to have more perspectives. I want to be something that’s not so easily definable, something you can’t sum up in two or three words. I’ve felt this tension my whole career making stuff for the web. I think it has led me to work on smaller teams where boundaries are much more permeable and crossing them is encouraged rather than discouraged. All that said, I get it. I get why titles are useful in certain contexts (corporate hierarchies, recruiting, etc.) where you’re trying to take something as complicated and nuanced as an individual human beings and reduce them to labels that can be categorized in a database. I find myself avoiding those contexts where so much emphasis is placed in the usefulness of those labels. “I’ve never wanted to be something you could easily define” stands at odds with the corporate attitude of, “Here’s the job req. for the role (i.e. cog) we’re looking for.” Email · Mastodon · Bluesky
We received over 2,200 applications for our just-closed junior programmer opening, and now we're going through all of them by hand and by human. No AI screening here. It's a lot of work, but we have a great team who take the work seriously, so in a few weeks, we'll be able to invite a group of finalists to the next phase. This highlights the folly of thinking that what it'll take to land a job like this is some specific list of criteria, though. Yes, you have to present a baseline of relevant markers to even get into consideration, like a great cover letter that doesn't smell like AI slop, promising projects or work experience or educational background, etc. But to actually get the job, you have to be the best of the ones who've applied! It sounds self-evident, maybe, but I see questions time and again about it, so it must not be. Almost every job opening is grading applicants on the curve of everyone who has applied. And the best candidate of the lot gets the job. You can't quantify what that looks like in advance. I'm excited to see who makes it to the final stage. I already hear early whispers that we got some exceptional applicants in this round. It would be great to help counter the narrative that this industry no longer needs juniors. That's simply retarded. However good AI gets, we're always going to need people who know the ins and outs of what the machine comes up with. Maybe not as many, maybe not in the same roles, but it's truly utopian thinking that mankind won't need people capable of vetting the work done by AI in five minutes.
Recently I got a question on formal methods1: how does it help to mathematically model systems when the system requirements are constantly changing? It doesn't make sense to spend a lot of time proving a design works, and then deliver the product and find out it's not at all what the client needs. As the saying goes, the hard part is "building the right thing", not "building the thing right". One possible response: "why write tests"? You shouldn't write tests, especially lots of unit tests ahead of time, if you might just throw them all away when the requirements change. This is a bad response because we all know the difference between writing tests and formal methods: testing is easy and FM is hard. Testing requires low cost for moderate correctness, FM requires high(ish) cost for high correctness. And when requirements are constantly changing, "high(ish) cost" isn't affordable and "high correctness" isn't worthwhile, because a kinda-okay solution that solves a customer's problem is infinitely better than a solid solution that doesn't. But eventually you get something that solves the problem, and what then? Most of us don't work for Google, we can't axe features and products on a whim. If the client is happy with your solution, you are expected to support it. It should work when your customers run into new edge cases, or migrate all their computers to the next OS version, or expand into a market with shoddy internet. It should work when 10x as many customers are using 10x as many features. It should work when you add new features that come into conflict. And just as importantly, it should never stop solving their problem. Canonical example: your feature involves processing requested tasks synchronously. At scale, this doesn't work, so to improve latency you make it asynchronous. Now it's eventually consistent, but your customers were depending on it being always consistent. Now it no longer does what they need, and has stopped solving their problems. Every successful requirement met spawns a new requirement: "keep this working". That requirement is permanent, or close enough to decide our long-term strategy. It takes active investment to keep a feature behaving the same as the world around it changes. (Is this all a pretentious of way of saying "software maintenance is hard?" Maybe!) Phase changes In physics there's a concept of a phase transition. To raise the temperature of a gram of liquid water by 1° C, you have to add 4.184 joules of energy.2 This continues until you raise it to 100°C, then it stops. After you've added two thousand joules to that gram, it suddenly turns into steam. The energy of the system changes continuously but the form, or phase, changes discretely. Software isn't physics but the idea works as a metaphor. A certain architecture handles a certain level of load, and past that you need a new architecture. Or a bunch of similar features are independently hardcoded until the system becomes too messy to understand, you remodel the internals into something unified and extendable. etc etc etc. It's doesn't have to be totally discrete phase transition, but there's definitely a "before" and "after" in the system form. Phase changes tend to lead to more intricacy/complexity in the system, meaning it's likely that a phase change will introduce new bugs into existing behaviors. Take the synchronous vs asynchronous case. A very simple toy model of synchronous updates would be Set(key, val), which updates data[key] to val.3 A model of asynchronous updates would be AsyncSet(key, val, priority) adds a (key, val, priority, server_time()) tuple to a tasks set, and then another process asynchronously pulls a tuple (ordered by highest priority, then earliest time) and calls Set(key, val). Here are some properties the client may need preserved as a requirement: If AsyncSet(key, val, _, _) is called, then eventually db[key] = val (possibly violated if higher-priority tasks keep coming in) If someone calls AsyncSet(key1, val1, low) and then AsyncSet(key2, val2, low), they should see the first update and then the second (linearizability, possibly violated if the requests go to different servers with different clock times) If someone calls AsyncSet(key, val, _) and immediately reads db[key] they should get val (obviously violated, though the client may accept a slightly weaker property) If the new system doesn't satisfy an existing customer requirement, it's prudent to fix the bug before releasing the new system. The customer doesn't notice or care that your system underwent a phase change. They'll just see that one day your product solves their problems, and the next day it suddenly doesn't. This is one of the most common applications of formal methods. Both of those systems, and every one of those properties, is formally specifiable in a specification language. We can then automatically check that the new system satisfies the existing properties, and from there do things like automatically generate test suites. This does take a lot of work, so if your requirements are constantly changing, FM may not be worth the investment. But eventually requirements stop changing, and then you're stuck with them forever. That's where models shine. As always, I'm using formal methods to mean the subdiscipline of formal specification of designs, leaving out the formal verification of code. Mostly because "formal specification" is really awkward to say. ↩ Also called a "calorie". The US "dietary Calorie" is actually a kilocalorie. ↩ This is all directly translatable to a TLA+ specification, I'm just describing it in English to avoid paying the syntax tax ↩
While Stripe is a widely admired company for things like its creation of the Sorbet typer project, I personally think that Stripe’s most interesting strategy work is also among its most subtle: its willingness to significantly prioritize API stability. This strategy is almost invisible externally. Internally, discussions around it were frequent and detailed, but mostly confined to dedicated API design conversations. API stability isn’t just a technical design quirk, it’s a foundational decision in an API-driven business, and I believe it is one of the unsung heroes of Stripe’s business success. This is an exploratory, draft chapter for a book on engineering strategy that I’m brainstorming in #eng-strategy-book. As such, some of the links go to other draft chapters, both published drafts and very early, unpublished drafts. Reading this document To apply this strategy, start at the top with Policy. To understand the thinking behind this strategy, read sections in reverse order, starting with Explore. More detail on this structure in Making a readable Engineering Strategy document. Policy & Operation Our policies for managing API changes are: Design for long API lifetime. APIs are not inherently durable. Instead we have to design thoughtfully to ensure they can support change. When designing a new API, build a test application that doesn’t use this API, then migrate to the new API. Consider how integrations might evolve as applications change. Perform these migrations yourself to understand potential friction with your API. Then think about the future changes that we might want to implement on our end. How would those changes impact the API, and how would they impact the application you’ve developed. At this point, take your API to API Review for initial approval as described below. Following that approval, identify a handful of early adopter companies who can place additional pressure on your API design, and test with them before releasing the final, stable API. All new and modified APIs must be approved by API Review. API changes may not be enabled for customers prior to API Review approval. Change requests should be sent to api-review email group. For examples of prior art, review the api-review archive for prior requests and the feedback they received. All requests must include a written proposal. Most requests will be approved asynchronously by a member of API Review. Complex or controversial proposals will require live discussions to ensure API Review members have sufficient context before making a decision. We never deprecate APIs without an unavoidable requirement to do so. Even if it’s technically expensive to maintain support, we incur that support cost. To be explicit, we define API deprecation as any change that would require customers to modify an existing integration. If such a change were to be approved as an exception to this policy, it must first be approved by the API Review, followed by our CEO. One example where we granted an exception was the deprecation of TLS 1.2 support due to PCI compliance obligations. When significant new functionality is required, we add a new API. For example, we created /v1/subscriptions to support those workflows rather than extending /v1/charges to add subscriptions support. With the benefit of hindsight, a good example of this policy in action was the introduction of the Payment Intents APIs to maintain compliance with Europe’s Strong Customer Authentication requirements. Even in that case the charge API continued to work as it did previously, albeit only for non-European Union payments. We manage this policy’s implied technical debt via an API translation layer. We release changed APIs into versions, tracked in our API version changelog. However, we only maintain one implementation internally, which is the implementation of the latest version of the API. On top of that implementation, a series of version transformations are maintained, which allow us to support prior versions without maintaining them directly. While this approach doesn’t eliminate the overhead of supporting multiple API versions, it significantly reduces complexity by enabling us to maintain just a single, modern implementation internally. All API modifications must also update the version transformation layers to allow the new version to coexist peacefully with prior versions. In the future, SDKs may allow us to soften this policy. While a significant number of our customers have direct integrations with our APIs, that number has dropped significantly over time. Instead, most new integrations are performed via one of our official API SDKs. We believe that in the future, it may be possible for us to make more backwards incompatible changes because we can absorb the complexity of migrations into the SDKs we provide. That is certainly not the case yet today. Diagnosis Our diagnosis of the impact on API changes and deprecation on our business is: If you are a small startup composed of mostly engineers, integrating a new payments API seems easy. However, for a small business without dedicated engineers—or a larger enterprise involving numerous stakeholders—handling external API changes can be particularly challenging. Even if this is only marginally true, we’ve modeled the impact of minimizing API changes on long-term revenue growth, and it has a significant impact, unlocking our ability to benefit from other churn reduction work. While we believe API instability directly creates churn, we also believe that API stability directly retains customers by increasing the migration overhead even if they wanted to change providers. Without an API change forcing them to change their integration, we believe that hypergrowth customers are particularly unlikely to change payments API providers absent a concrete motivation like an API change or a payment plan change. We are aware of relatively few companies that provide long-term API stability in general, and particularly few for complex, dynamic areas like payments APIs. We can’t assume that companies that make API changes are ill-informed. Rather it appears that they experience a meaningful technical debt tradeoff between the API provider and API consumers, and aren’t willing to consistently absorb that technical debt internally. Future compliance or security requirements—along the lines of our upgrade from TLS 1.2 to TLS 1.3 for PCI—may necessitate API changes. There may also be new tradeoffs exposed as we enter new markets with their own compliance regimes. However, we have limited ability to predict these changes at this point.
I've been biking in Brooklyn for a few years now! It's hard for me to believe it, but I'm now one of the people other bicyclists ask questions to now. I decided to make a zine that answers the most common of those questions: Bike Brooklyn! is a zine that touches on everything I wish I knew when I started biking in Brooklyn. A lot of this information can be found in other resources, but I wanted to collect it in one place. I hope to update this zine when we get significantly more safe bike infrastructure in Brooklyn and laws change to make streets safer for bicyclists (and everyone) over time, but it's still important to note that each release will reflect a specific snapshot in time of bicycling in Brooklyn. All text and illustrations in the zine are my own. Thank you to Matt Denys, Geoffrey Thomas, Alex Morano, Saskia Haegens, Vishnu Reddy, Ben Turndorf, Thomas Nayem-Huzij, and Ryan Christman for suggestions for content and help with proofreading. This zine is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, so you can copy and distribute this zine for noncommercial purposes in unadapted form as long as you give credit to me. Check out the Bike Brooklyn! zine on the web or download pdfs to read digitally or print here!