Open
Description
openedon May 16, 2022
Describe the bug
Multiplication of either CSC or CSR sparse matrices is prohibitively slow.
To reproduce
The Minimal Working Example (MWE) for this bug:
using CUDA
using SparseArrays
A = sprand(20000,700,0.3)
B = sprand(700,20000,0.3)
d_A = CUSPARSE.CuSparseMatrixCSR(A)
d_B = CUSPARSE.CuSparseMatrixCSR(B)
@code_llvm d_A * d_B # dispatches to generic_mm also CSC dispatches to generic_mm
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Expected behavior
Fast calculation, llvm code indicating that it has been correctly dispatched.
Version info
Details on Julia:
Julia Version 1.7.2
Commit bf53498635 (2022-02-06 15:21 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-12.0.1 (ORCJIT, skylake)
Environment:
JULIA_NUM_THREADS = 8
Details on CUDA:
CUDA toolkit 11.7, artifact installation
NVIDIA driver 510.60.2, for CUDA 11.6
CUDA driver 11.6
Libraries:
- CUBLAS: 11.10.1
- CURAND: 10.2.10
- CUFFT: 10.7.2
- CUSOLVER: 11.3.5
- CUSPARSE: 11.7.3
- CUPTI: 17.0.0
- NVML: 11.0.0+510.60.2
- CUDNN: 8.30.2 (for CUDA 11.5.0)
- CUTENSOR: 1.4.0 (for CUDA 11.5.0)
Toolchain:
- Julia: 1.7.2
- LLVM: 12.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80
1 device:
0: NVIDIA GeForce RTX 2070 with Max-Q Design (sm_75, 6.692 GiB / 8.000 GiB available)
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