3 ms·

I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend

I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways. As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linear_algebra/matrix.cpp#L133 https://github.com/dsharlet/array/blob/master/examples/linea...

bjourne 3y ago
So I actually tested your code: https://gist.github.com/bjourne/c2d0db48b2e50aaadf884e4450c6aa50 https://gist.github.com/bjourne/c2d0db48b2e50aaadf884e4450c6... On my machine single-threaded OpenBLAS (called via NumPy) multiplies two single precision 4096x4096 matrices in 0.95 seconds. Your code takes over 30 seconds when compiled with clang++. And yes I used -O3, -march=native, and all that jazz. Btw, your code crashes g++ which doesn't necessarily mean that it is incorret, but it indicates that the code may be difficult for the compiler to optimize. For comparison, my own matrix multiplication code (https://github.com/bjourne/c-examples/blob/master/libraries/tensors/multiply.c https://github.com/bjourne/c-examples/blob/master/libraries/...) run in single-threaded mode takes 0.89 seconds. Which actually beats OpenBLAS, but OpenBLAS retakes the lead for larger arrays when multi-threading is added. You can look at my code for how to write a decent inner kernel. Writing it in pure C without intrinsics and hoping that the compiler will optimize it definitely will not work. It also is not true that "Parallelism would be easy to add". Unless your algorithm is designed from the start to exploit multi-threading, attempting to bolt it on later will not yield good performance.
- dsharlet 3y ago
  The makefile asks for -O2 with clang. I find that -O3 almost never helps in clang. (In gcc it does.) Here's what I see: $ clang++ --version clang version 18.0.0 $ time make bin/matrix mkdir -p bin clang++ -I../../include -I../ -o bin/matrix matrix.cpp -O2 -march=native -ffast-math -fstrict-aliasing -fno-exceptions -DNDEBUG -DBLAS -std=c++14 -Wall -lstdc++ -lm -lblas 1.25user 0.29system 0:02.74elapsed 56%CPU (0avgtext+0avgdata 126996maxresident)k 159608inputs+120outputs (961major+25661minor)pagefaults 0swaps $ bin/matrix ... reduce_tiles_z_order time: 3.86099 ms, 117.323 GFLOP/s blas time: 0.533486 ms, 849.103 GFLOP/s $ OMP_NUM_THREADS=1 bin/matrix ... reduce_tiles_z_order time: 3.89488 ms, 116.303 GFLOP/s blas time: 3.49714 ms, 129.53 GFLOP/s My inner loop in perf: https://gist.github.com/dsharlet/5f51a632d92869d144fc3d6ed6b0fdad https://gist.github.com/dsharlet/5f51a632d92869d144fc3d6ed6b... BLAS inner loop in perf (a chunk of it, it is unrolled massively): https://gist.github.com/dsharlet/5b2184a285a798e0f0c6274dc422392d https://gist.github.com/dsharlet/5b2184a285a798e0f0c6274dc42... Despite being on a current-ish version of clang, I've been getting similar results from clang for years now. Anyways, I'm not going to debate any further. It works for me :) If you want to keep writing code the way you have, go for it.
  - bjourne 3y ago
    -O2 did improve performance significantly, but it's still 0.7 s for NumPy and 5.1 seconds for your code on 4096x4096 matrices. Either you're using a slow version of BLAS or you are benchmarking with matrices that are comparatively tiny (384x1536 is nothing).
    - dsharlet 3y ago
      BLAS is getting almost exactly 100% of the theoretical peak performance of my machine (CPU frequncy * 2 fmadd/cycle * 8 lanes * 2 ops/lane), it's not slow. I mean, just look at the profiler output... You're probably now comparing parallel code to single threaded code.
      - bjourne 3y ago
        No, multi-threaded OpenBLAS improves performance to 0.15s.
        dsharlet 3y ago
        I dunno man. My claim was that for specific cases with unique properties, it's not hard to beat BLAS, without getting too exotic with your code. BLAS doesn't have routines for multiplies with non-contiguous data, various patterns of sparsity, mixed precision inputs/outputs, etc. The example I gave is for a specific case close-ish to the case I cared about. You're changing it to a very different case, presumably one that you cared about, although 4096x4096 is oddly square and a very clean power of 2... I said right at the beginning of this long digression that what is hard about reproducing BLAS is its generality.
        bjourne 3y ago
        When I run your benchmark with matrices larger than 1024x1024 it errors out in the verification step. Since your implementation isn't even correct I think my original point about OpenBLAS being extremely difficult to replicate still stands.

bjourne 3y ago

So I actually tested your code: https://gist.github.com/bjourne/c2d0db48b2e50aaadf884e4450c6aa50 https://gist.github.com/bjourne/c2d0db48b2e50aaadf884e4450c6... On my machine single-threaded OpenBLAS (called via NumPy) multiplies two single precision 4096x4096 matrices in 0.95 seconds. Your code takes over 30 seconds when compiled with clang++. And yes I used -O3, -march=native, and all that jazz. Btw, your code crashes g++ which doesn't necessarily mean that it is incorret, but it indicates that the code may be difficult for the compiler to optimize. For comparison, my own matrix multiplication code (https://github.com/bjourne/c-examples/blob/master/libraries/tensors/multiply.c https://github.com/bjourne/c-examples/blob/master/libraries/...) run in single-threaded mode takes 0.89 seconds. Which actually beats OpenBLAS, but OpenBLAS retakes the lead for larger arrays when multi-threading is added. You can look at my code for how to write a decent inner kernel. Writing it in pure C without intrinsics and hoping that the compiler will optimize it definitely will not work. It also is not true that "Parallelism would be easy to add". Unless your algorithm is designed from the start to exploit multi-threading, attempting to bolt it on later will not yield good performance.

dsharlet 3y ago
The makefile asks for -O2 with clang. I find that -O3 almost never helps in clang. (In gcc it does.) Here's what I see: $ clang++ --version clang version 18.0.0 $ time make bin/matrix mkdir -p bin clang++ -I../../include -I../ -o bin/matrix matrix.cpp -O2 -march=native -ffast-math -fstrict-aliasing -fno-exceptions -DNDEBUG -DBLAS -std=c++14 -Wall -lstdc++ -lm -lblas 1.25user 0.29system 0:02.74elapsed 56%CPU (0avgtext+0avgdata 126996maxresident)k 159608inputs+120outputs (961major+25661minor)pagefaults 0swaps $ bin/matrix ... reduce_tiles_z_order time: 3.86099 ms, 117.323 GFLOP/s blas time: 0.533486 ms, 849.103 GFLOP/s $ OMP_NUM_THREADS=1 bin/matrix ... reduce_tiles_z_order time: 3.89488 ms, 116.303 GFLOP/s blas time: 3.49714 ms, 129.53 GFLOP/s My inner loop in perf: https://gist.github.com/dsharlet/5f51a632d92869d144fc3d6ed6b0fdad https://gist.github.com/dsharlet/5f51a632d92869d144fc3d6ed6b... BLAS inner loop in perf (a chunk of it, it is unrolled massively): https://gist.github.com/dsharlet/5b2184a285a798e0f0c6274dc422392d https://gist.github.com/dsharlet/5b2184a285a798e0f0c6274dc42... Despite being on a current-ish version of clang, I've been getting similar results from clang for years now. Anyways, I'm not going to debate any further. It works for me :) If you want to keep writing code the way you have, go for it.
- bjourne 3y ago
  -O2 did improve performance significantly, but it's still 0.7 s for NumPy and 5.1 seconds for your code on 4096x4096 matrices. Either you're using a slow version of BLAS or you are benchmarking with matrices that are comparatively tiny (384x1536 is nothing).
  - dsharlet 3y ago
    BLAS is getting almost exactly 100% of the theoretical peak performance of my machine (CPU frequncy * 2 fmadd/cycle * 8 lanes * 2 ops/lane), it's not slow. I mean, just look at the profiler output... You're probably now comparing parallel code to single threaded code.
    - bjourne 3y ago
      No, multi-threaded OpenBLAS improves performance to 0.15s.
      - dsharlet 3y ago
        I dunno man. My claim was that for specific cases with unique properties, it's not hard to beat BLAS, without getting too exotic with your code. BLAS doesn't have routines for multiplies with non-contiguous data, various patterns of sparsity, mixed precision inputs/outputs, etc. The example I gave is for a specific case close-ish to the case I cared about. You're changing it to a very different case, presumably one that you cared about, although 4096x4096 is oddly square and a very clean power of 2... I said right at the beginning of this long digression that what is hard about reproducing BLAS is its generality.
        bjourne 3y ago
        When I run your benchmark with matrices larger than 1024x1024 it errors out in the verification step. Since your implementation isn't even correct I think my original point about OpenBLAS being extremely difficult to replicate still stands.