compression

cuSPARSE Generic APIs - Hardware Memory Compression

Description

The sample demonstrates how to optimize sparse vector - dense vector scaling and sum (cusparseAxpby) by exploiting NVIDIA Ampere architecture Hardware Memory Compression

cuSPARSE Optimization Notes

Nsight Compute can be used to understand the effect of the memory compression

nv-nsight-cu-cli --metrics lts__gcomp_input_sectors_compression_achieved_algo_sdc4to1.sum,lts__gcomp_input_sectors_compression_achieved_algo_sdc4to2.sum,fbpa__dram_read_sectors.sum,fbpa__dram_write_sectors.sum,lts__average_gcomp_input_sector_compression_rate.pct ./compression_example

Building

• Command line

  nvcc -I<cuda_toolkit_path>/include compression_example.c -o compression_example -lcusparse -lcuda

• Linux

  make

• Windows/Linux

  mkdir build
  cd build
  cmake ..
  make

  On Windows, instead of running the last build step, open the Visual Studio Solution that was created and build.

Support

• Supported SM Architectures: SM 8.0, SM 8.6, SM 8.9, SM 9.0
• Supported OSes: Linux, Windows, QNX, Android
• Supported CPU Architectures: x86_64, ppc64le, arm64
• Supported Compilers: gcc, clang, Intel icc, IBM xlc, Microsoft msvc, Nvidia HPC SDK nvc
• Language: C++14

Prerequisites

• CUDA 11.0 toolkit (or above) and compatible driver (see CUDA Driver Release Notes).
• CMake 3.9 or above on Windows