r0cketdyne / SparseRooflineBenchmark

The Sparse Roofline Benchmark Suite

The Sparse Roofline Benchmark Suite

The Sparse Roofline Benchmark Suite (SRBS) is a collection of sparse kernels that are used to evaluate the performance of sparse matrix kernels on modern architectures.

Installation

The SRBS uses Julia to generate datasets. To install Julia, please follow the instructions on the Julia website.

An example reference C++ implementation is provided in the example/ directory. All of the necessary files required to read a test dataset and benchmark the implementation are provided as header-only dependencies. The only requirement is a C++20 compliant compiler.

Usage

Once Julia is installed, you can generate test data for a particular problem with the src/generate.jl script:

% julia src/generate.jl --help

The script will generate a dataset for a particular problem and write it to the given destination.

For example, to generate RMAT data for spmv in the default location under ./data/ run the following command:

% julia src/generate.jl spmv rmat 

Implementations of a given kernel are expected to be given as executables that take the same arguments as the reference implementation. All of the implementations take the same arguments, namely:

% ./spmv --help
Usage: ./spmv [OPTIONS]
Options:
  -h, --help      Print this help message
  -i, --input     Specify the path for the inputs
  -o, --output    Specify the path for the outputs
  -v, --verbose   Print verbose output

The above example implementation can be generated from examples/spmv.cpp. An example command to generate the executable using g++ is:

cd examples/
g++ -std=c++20 -o spmv spmv.cpp

Harnesses are provided to parse these arguments in several languages. To benchmark your implementation in C++, simply include src/benchmark.hpp. This file includes the necessary header-only dependencies to parse the arguments and read the input data, and calls the experiment function, which is expected to benchmark the kernel.

Formats

The SRBS uses the Binsparse format to store tensors on disk and in memory. In particular, the header of the file is a JSON object that describes the dimensions and format of the tensor, and the data for the tensor is stored as a set of named vectors. These objects may all be stored in the same file, (e.g. HDF5 or ZARR), or in separate files (e.g. .npy).