[hutzlibu]

GPU programming in general is definitely not trivial, as I can confirm with struggeling to learn WebGPU right now.

But it really depends on the problem, simple math operations on lots of data is usually indeed trivially faster. Like AI mostly is with math on matrices.

Or for example I just implemented a simple 2D raycastsolver in wgsl and as a first project it is totally not optimized - but even on my old laptop with crappy integrated GPU, but (relativly) fast CPU - I can now do 10000 raycasts per frame easily, while the cpu (wasm!) struggles with 500.

The raw power of the gpu is really awesome. But every step is hard and debugging a mess. Which is why only a handful of people seems do be doing it. But now would probably be a good time, to get into it.. as I think gpu compute just has started and will get big.

[paulmd]

I've been out of the space for a long time, and it's possible you know these already, but these are a couple weird tricks that can help:

* Radix sort is your friend. Fun fact, O(n log n) is not the fastest a sort can run, it's the fastest a comparison-based sort can run. Radix sort runs in O(N) time, and in fact parallelizes extremely well on GPUs. Extremely. They are great at it. And there are in-place radix sorts too, just a bit slower (same asymptotic performance tho).

* "Insert an element into this collection" style steps can be replaced by a sort and a prefix-sum operation. If you know the offset of the first element with key K, and you know the offset of the first element with key J, you know the offset and size of that "collection" for K within a flat array ("size(K) = offset(J) - offset(K)"). Both of these run super fast in parallel and if you can tweak your problem around to be some kind of sorting operation that usually produces good speedups like this. Easiest way to get a speedup from everything I've heard, if your program works fast from a lookup table it's a good approach.

* Recomputing (or duplicate computation) is often much faster than storing intermediate results. "Procedural generation" is interesting because you can re-compute the generation step on demand. Random123 is also very nice compared to a (CuRand) mersenne twister/etc - why are you, a believer in the cryptographic maxim that hash(key, msg) is uncorrelated to hash(key, msg+1), still storing RNG state? Being able to play back arbitrary parts of a datastream at will is incredibly powerful, you can fastforward and rewind through the data previously used to interact with an item, as long as you know the epoch of the interaction you want for a particular key. And because computation is cheaper than memory, and memory bandwidth - it's really actually practically free in program time terms to just do some math. This is a form of data compression and performance enhancement.

* Generally you must understand the idea of divergence and memory coalescing/alignment to keep those lanes executing. And it is highly preferable to use sorts and prefix scans and butterfly operations (reduction, etc) even within a warp, because traditional "mutex/atomic" paradigms don't work well with 100k threads. But this is just the programming idioms of this particular platform, I am sure LISP is similar too in terms of "oh that's how you do that" once you're accustomed.

("This thread would like to write 3 items to an output array, can I get an alignment into a datablock the warp group is going to allocate from a global counter via atomic_add and write to" is another common task element in many parallel workloads. That is the same prefix-sum idiom, or a butterfly reduce, just at a warp level. And then when all the warps have finished, you can sort this unsorted output by a key array to align it, and start processing it again...)

* Cuda Unbound (CUB) provides reference implementations of all of these operations, and it handles portability across CUDA generations. Having good primitives makes a big difference, most of the workload isn't business logic, it's library calls, so using a library that just does it right gives you a lot of bang for buck. Don't write it yourself.

* Texture maps aren't just for graphics, they are a black box that lets the GPU perform 2D and 3D coalescing and some interpolation.

* Intelligent use of constants memory is another one, probably as is the use of CPU memory. If a value will be seldom accessed, you can probably stuff it into host memory and just accept the slowdown. Or you can store only epochs on the GPU and recompute intermediate values as needed. Try to ensure that all threads in a warp will do it too (host access vs recomputing).

* Raytracing is of course impervious to all of this (so don't worry too much that you can't magically hammer a speedup out of it, nobody really can). You can accelerate the raycasting and intersection testing (and AMD and NVIDIA and Intel all do this differently) but as a general matter rays are completely random and uncoalesced and divergent, they bypass almost all of the ideal GPGPU "hot paths". Ray sorting/shader execution reordering is something that needs hardware assistance, and Intel and NVIDIA both have hardware along these lines. The idea of Intel of making a facility for async future/promise dispatch for sparse tasks (and then sorting the shaders to get good coalescing/etc) is really neat and they've said it's going to come to GPGPU. https://youtu.be/SA1yvWs3lHU?t=289

* You can, however, use your rays more efficiently. And that's an area of active focus for everyone. And I think more efficient use of TAAU samples is probably where raster is going too.

[gregjm]

Personally I would lean away from radix sort once the problem size gets too large, on Turing they made the per-SM L1 cache coherent and on Ampere they made L2 cache significantly larger so getting maximum throughput requires more actively designing for cache-friendly access patterns. The access pattern of radix sort will be very strided, so you get low cache utility for those longer distance swaps. If your problem fits into shared memory or even registers (for in-warp shuffles) then definitely, radix sort is super efficient!

Memory coalescing probably got a little more lenient than when you were programming. If any group of threads in a warp is accessing a 128B size-and-aligned region of memory, that’s perfectly matched the 128B line size between L1TEX cache and L2 cache and you get coalesced accesses. Even 32B size-and-aligned (e.g. each pair of threads accesses two halves of a 32B region) isn’t all that bad, since it aligns with the 32B L2 cache line size to global memory. You run the risk of getting poor load/store utility if you don’t access the other 96B in that L1TEX line somewhere in the same block, though.

Constant memory is less important than it used to be, since constant memory goes through the texture memory, and the L1 cache was unified and combined with the texture cache. There’s still an LDG.CONSTANT SASS instruction on Turing, though, so there’s still a difference in the hardware that escapes me right now. IIRC you can write a MOV instruction with the source operand in constant memory (rather than LDG.CONSTANT) which doesn’t put load on the load-store unit (LSU). Could be useful if you’re getting warp stalls due to memory pipe throttling.

[paulmd]

Interesting, love it!

> on Turing they made the per-SM L1 cache coherent and on Ampere they made L2 cache significantly larger

Yeah Turing/Ampere are more than people give them credit for. They are quite a bit more powerful in compute in general iirc, the core is really Volta-descended iirc and not Pascal.

> so getting maximum throughput requires more actively designing for cache-friendly access patterns. The access pattern of radix sort will be very strided, so you get low cache utility for those longer distance swaps. If your problem fits into shared memory or even registers (for in-warp shuffles) then definitely, radix sort is super efficient!

Does dumping warp-sorted data into the buffer help? Eg can each warp or each block sort their output so what they're dumping in can be "galloped", like a partially-sorted mergesort or something? Finding those boundaries between output cells is (ironically) another prefix-scan lol.

Is it just that you need a different sort, or does sorting just not work that well now?

Are prefix-scans still good at all?

Interesting, anything in particular to read about it from Anandtech or Chips+Cheese or what? Or should I just read the whitepapers? (/sigh, reading primary sources, my only weakness)

(I really wish there was an Agner Fog for GPUs!)

> IIRC you can write a MOV instruction with the source operand in constant memory (rather than LDG.CONSTANT) which doesn’t put load on the load-store unit (LSU).

hackerman.jpg

[gregjm]

> Interesting, anything in particular to read about it from Anandtech or Chips+Cheese or what? Or should I just read the whitepapers? (/sigh, reading primary sources, my only weakness)

I haven’t had great luck with consulting non-primary-sources, most of my knowledge comes from reading NVIDIA blog posts and GTC presentations as they become relevant. Lately I’ve been working with CUTLASS and reading through that documentation — maybe start with their presentations and work back through their references? I’ve learned a lot by reading the architecture tuning guides from NVIDIA, too.

> Does dumping warp-sorted data into the buffer help? Eg can each warp or each block sort their output so what they're dumping in can be "galloped", like a partially-sorted mergesort or something? Finding those boundaries between output cells is (ironically) another prefix-scan lol. > > Is it just that you need a different sort, or does sorting just not work that well now?

I’m not sure, honestly! All I know is that recently I’ve been looking at radix sort kernels with lower-than-expected memory throughout and low cache hit rates :)

> Are prefix-scans still good at all?

The CUB linear-time prefix scan kernels seem to be fantastic still, they operate basically at the speed of DRAM with really high compute utilization. When I’ve seen lower-than-expected performance with these kernels, it’s because of an issue with an inefficient transform being made as part of the input/output iterator ranges, or because of some local memory usage due to an indexing operation in a local variable that couldn’t be fully inlined into registers.