Comment by raphlinus | Hacker News Reader

Comment by raphlinus | Hacker News Reader

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That sounds like a great project, one I heartily recommend to people wanting to get their feet wet.

First, the basic principles of how to write an efficient prefix sum are the same as before. Definitely read [1]. Also see [2] for an exploration into how much subgroups help over threadgroup shared memory. In fact, [2] has much of the answer you seek, as it's already written in Vulkan and GLSL (compiling to SPIR-V), though it does miss a direct performance comparison to CUB.

Second, doing this depends on WebGPU exposing threadgroup shared memory and subgroups operations. I'm not sure where the standard and implementations are on this; there are significant challenges. For example, subgroup shuffle is not available in DX12, though subgroup reduce operations such as WavePrefixSum are. So in general an implementation will need to do runtime detection of capabilities and choose a most-optimum kernel based on that.

In theory, it should be possible to achieve comparable performance. But it's likely that WebGPU implementations haven't really been tuned for performance yet, while CUB has seen a ton of work. So, as always, the thing to do is implement and measure.

[1]: https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-co...

[2]: https://cachemiss.xyz/blog/parallel-reduce-and-scan-on-the-G...

> First, the basic principles of how to write an efficient prefix sum are the same as before. Definitely read [1].

That's slightly outdated. The state-of-the art is: https://research.nvidia.com/publication/single-pass-parallel...

(EDIT: well the state of the art is CUB, but that paper is a bit more approachable than the CUB docs, which are still very good).

> though it does miss a direct performance comparison to CUB.

For perspective, CUB prefix-sum is as fast as a memcpy on the GPU. It achieves the peak memory bandwidth of the device.

Great paper. I believe it's possible to do this efficiently in Vulkan + SPIR-V, and also adapt it to WebGPU given the caveats mentioned above, but it's hard to say for sure without doing it. Clearly that paper uses very sophisticated techniques and would not be trivial to implement - it goes considerably past the [2] I linked.

I agree 100% that prefix sum is an excellent litmus test for this platform.

I'll give one example to fill in more specifics. The fence-free descriptor updates described in section 4.4 of your linked paper depend on 64 bit atomics. These are available using the VK_KHR_shader_atomic_int64 extension, and appear (using vulkan.gpuinfo.org) to be available on NV and AMD hardware but not Intel (and basically not at all on mobile). On Vulkan, you query at runtime for the presence of this extension, and swap in the appropriate kernel based on the result of the query; optimizing for hardware without 64 bit atomics might give you a different approach.

Not having to deal with all this is the reason NVidia owns the space :)

Followup: I did a very rough prototype on my current Vulkan runner; all the good stuff is in the prefix.comp kernel code. It's not doing the fancy stuff, in particular it doesn't parallelize the lookback code, so that ends up taking a bunch of time. But it does give an example of how to use the Vulkan memory model, for which I've seen very little in the way of published material.

If anyone is interested in tuning this and doing a performance comparison, please get in touch. It'd make a good blog post, I think, but already the time I've spent on it is a distraction from the renderer.

https://github.com/linebender/piet-gpu/tree/prefix

Thanks, that was super interesting to read.

Is subgroupExclusiveAdd a primitive, or is it implemented somewhere? (i've been searching through the git repo without much luck).

This has motivated me to learn a bit more about using Vulkan for compute. I hope I get to it tonight and tomorrow - benefits of the pandemic :D

subgroupExclusiveAdd is part of the subgroup extension, and expected to be implemented in hardware. Best place to read about that is here:

https://www.khronos.org/blog/vulkan-subgroup-tutorial

Which devices do implement this in hardware ?

None of the NVIDIA compute devices do, right? Do AMD GPUs implement this ?

CPUs from Intel, ARM, RISCV, MIPS, Power support horizontal SIMD add instructions that perform a tree-reduction.

I'm skeptical of GPUs being able to actually implement this on hardware. A CPU puts the whole vector in a single 512-bit register, but in a GPU, even for a single half-warp, the operation cannot take 16x64bit registers. The operation would need to be performed on memory, and move the content to the different registers, do warp-local shuffles for the reduction, etc. So we would be talking about a quite big macro-op here. I wouldn't see the point of doing this in hardware, when the compiler or driver could just call a library function that does this for you.

But then we are back to the issue that, if you can't write those library functions in pure Vulkan, you are quite limited w.r.t what one can do.

There are many prefix-sum like algorithms, like, e.g., a weighted prefix-sum (prefix_sum(x * w)) [0]. In a GPU, you don't want to do the vector multiply first followed by the prefix-sum, but do it all at once. You can probably work around this in Vulkan by doing the vector multiply in shared memory, and then the prefix sum there (while in CUDA you wouldn't need shared memory for that at all), but that won't really work if the reduction is more complicated than just a sum.

[0] Convolved prefix sums happen a lot in signal processing with linear-recurrence relations, e.g., in IIR filters, e.g. see [1] https://dl.acm.org/doi/10.1145/3229631.3229649 and [2] https://dl.acm.org/doi/10.1145/3296957.3173168. The weighted prefix sum example is the simplest possible instance of those.

Right, I haven't looked at PTX or Intel Gen ASM, but on AMD this compiles to a lg(n) series of add instructions each with a warp local shuffle (v_add_nc_u32 with a row_shr: modifier). It's not going to shared memory. I've experimented a bit with hand-rolling scans (using subgroupShuffleUp) and get similar but not identical asm (that intrinsic gives you a wave_ror: modifier). So I think you could write this using even lower-level intrinsics and also get good performance.

I strongly suspect Intel and NVidia are similar here, but haven't experimented.

[1]: http://shader-playground.timjones.io/4d7402168290f8922da5df3...

There's actually a lot more to say here, but it really gets into the weeds. To me, the fundamental question is: can you write Vulkan 1.2 compute shaders that exploit most of the performance potential of GPU hardware in a portable way? I believe the answer is yes, but it requires validation. It is, however, not a good approach if the goal is to squeeze out the last 5% or 10% of performance.

That makes sense. Given that one can write good compute shaders with Vulkan, the next obvious question worth answering to me is whether one should do that.

If I had to write a new foundational library, e.g., something like CUTLASS or cuFFT for device code, I won't start with code that fully uses a device, but I'd like to be able to do that if I want to.

If I need to reach for CUDA to do that, then Vulkan, WebGPU, etc. will need to have great FFI with CUDA C++. Nobody wants to have to "drop up" to a higher-level CUDA C++ API like CUTLASS, but being forced to do so by "dropping down" that API to a C FFI wrapper.

It would be like having Rust, but without any unsafe code support except for C FFI, C actually not existing, and having to drop "down" to C++ for everything, and then numbing down the C++ to C FFI for interoperability. You might as well just stick with C++ and spare you the pain.

I hope Vulkan and WebGPU get enough extensions so that foundational libraries can be written in those languages.

Thanks for your continued discussion!

I wrote some more of my thoughts on the xi Zulip #gpu channel (login required, open to everybody with Github): https://xi.zulipchat.com/#narrow/stream/197075-gpu/topic/Pre...

@Raph I don't have Zulip, but you have motivated me to play a lot the last weekends with Vulkan compute, and I am a bit disappointed.

I think I was expecting to have a similar level of control as with CUDA or OpenCL, but it turns out that wgpu/Vulkan compute is more like "OpenGL compute shaders", which is "compute for a rendering pipeline", something quite different than "CUDA/OpenCL compute", which has nothing to do with rendering.

If all you want is to stick a compute shader in your rendering pipeline, then wgpu/vulkan are great IMO.

If you want to do CFD, linear algebra, or machine learning, then it feels like fighting against a technology that just wasn't meant to be used for that. I managed to write a tiny explicit CFD code with Vulkan compute just fine, but the moment I wanted to implement a slightly more complicated algorithm, I was limited by things like how to properly do linear algebra (e.g. I needed a algebraic multi-grid preconditioner and a conjugate gradients implementation, and ended up fighting against how to control what's get put into shared memory when). When trying some NN kernels, I got stuck with the same issue. I managed to implement an FFT and convolutions with Vulkan, but... I couldn't manage to get convolutions achieve the same perf as with CUDA cause with Vulkan I had to use a "pull" algorithm (read from all points in the window, write to one point), but that just performed way worse than the "push" algorithms that are possible to implement with CUDA (maintain a shared memory cache where you write, and as you read one value from the window, write its contribution to multiple locations, then do a full cache write to global memory). In numbers, I went from ~150 Gb/s throughput with Vulkan to ~700Gb/s with CUDA.

I think maybe Vulkan/SPIRV could benefit from better compilers that take the "pull" algorithm and compile it to a "push" one using shared memory, but you are then at the mercy of the compiler for a 4x perf diff.

I think that if I have a rendering pipeline in which I want to integrate some compute, then wgpu/Vulkan are great, particularly because chances are that the compute you need is per "vertex/pixel/..." kind of naive-massively-parallel kind of compute. But if I just have a raw compute pipeline, then it feels like using the wrong tool for the job.

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