The clSpMV framework optimizes the Sparse Matrix Vector multiplication (SpMV) kernel on OpenCL enabled GPU platforms. Given a sparse matrix, the ultimate goal is to find the best representation of any sparse matrix, and choose the best kernel implementation on the platform of choice automatically.

The clSpMV framework uses the Cocktail Format to store a sparse matrix. The Cocktail Format is a combination of different sparse matrix formats. The idea is to represent specialized submatrices using specialized formats. Under the case that the sprase matrix should be represented by a single format, the clSpMV framework will also tell you which single format to choose. So far the package supports 9 different formats in the Cocktail Format:

Diagonal based formats: Diagonal, Banded Diagonal

Flat based formats: Sliced ELL, ELL, CSR, COO

Block based formats: Sliced Blocked ELL, Blocked ELL, Blocked CSR

The package has two major executables:

spmv_cocktail: Given a sparse matrix, analyze it, represent the matrix using the Cocktail Format, and perform the SpMV kernel.
Usage: spmv_cocktail matrix.mtx

spmv_all: Represent the matrix using any 1 of the 9 supported format, and evaluate the execution time of different implementations of that format.
Usage: spmv_all input_matrix.mtx method execution_times
The method is the format you want to use:
Method 0: measure the memory bandwidth and kernel launch overhead only
Method 1: use the csr matrix format, using the scalar implementations
Method 2: use the csr matrix format, using the vector implementations
Method 3: use the bdia matrix format
Method 4: use the dia matrix format
Method 5: use the ell matrix format
Method 6: use the coo matrix format
Method 7: use the bell matrix format
Method 8: use the bcsr matrix format
Method 9: use the sell matrix format
Method 10: use the sbell matrix format

I am interested in understanding where the framework is used. I really appreciate if you can spend 10 seconds and tell me the field (e.g. computer vision, molecular dynamics, ...) and the algorithm (eigen decomposition, conjugate gradient, ...) where the framework will be used.

@inproceedings{ics2012-su,
author = {Bor-Yiing Su and Kurt Keutzer},
title = {clSpMV: A Cross-Platform OpenCL SpMV Framework on GPUs},
booktitle = {Proceedings of the international conference on Supercomputing},
series = {ICS '12},
year = {2012},
location = {Venice, Italy}, }