The NVIDIA H100 with 80 GB memory and RTX4090 GPU with 24 GB memory.
Ubuntu 20.04.6; Pytorch 2.1.2+cu118; CUDA 11.8; gcc 7.5.0; DGL 2.1.0+cu118; PyG 2.5.0.
We select a representative set of 500 sparse matrices from the SuiteSparse collection, which span various sparsity patterns, to evaluate kernel performance. In addition, we also select classic graph datasets across different application domains such as IGB, Reddit for end-to-end performance evaluation.
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apt-get install libnuma-dev
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apt-get install libgoogle-perftools-dev
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apt-get install libboost-all-dev
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apt-get install libgoogle-perftools-dev \
- bash ./Libra-source/compile.sh
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bash ./eva100/kernel/spmm/test_spmm_h100_shell.sh
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python ./eva100/plot/kernel_spmm/h100_128/plot_cuda.py
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python ./eva100/plot/kernel_spmm/h100_128/plot_tcu.py
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bash ./eva100/kernel/spmm/test_sddmm_h100_shell.sh
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python ./eva100/plot/kernel_sddmm/h100_32/plot_cuda.py
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python ./eva100/plot/kernel_sddmm/h100_32/plot_tcu.py
- python ./eva100/plot/kernel_spmm/profile_all.py
- ./eva100/plot/ablation
- python ./eva100/plot/ablation/hybird/plot.py
- python ./eva100/plot/ablation/data_access/plot_cc_only_spmm.py
- python ./eva100/plot/ablation/data_access/plot_cc_only_sddmm.py
- python ./eva100/plot/ablation/threshold.py
- python ./eva100/plot/ablation/tcu_utilization/spmm/plot.py
- python ./eva100/plot/ablation/tcu_utilization/sddmm/plot.py
- python ./eva100/plot/ablation/data_access/spmm/plot.py
- python ./eva100/plot/ablation/data_access/sddmm/plot.py
- python ./eva100/plot/ablation/atomic/plot.py
- python ./eva100/plot/ablation/atomic/plot_size.py
- python ./eva100/plot/gnn/plot.py
- python ./eva100/plot/agnn/plot.py
- python ./eva100/plot/ablation/amd/plot_amd_spmm.py
- python ./eva100/plot/ablation/amd/plot_amd_sddmm.py
