######OpenCL BLAS library wrapper for Julia with samples
##What is this project?
This project focuses on running OpenCL BLAS with Julia matrices on GPU devices seamlessly, all OpenCL type definitions and functions were hand-typed from cl.h and clBLAS.h header.
Currently, I've only rewritten the example C program (single precision GEMM BLAS) provided with libclBLAS into test_sgemm.jl. I also made a separate high level function to manages all the memory involved with calling the C function clblasSgemm().
As a result, the OpenCLBLAS.sgemm() function cleans up the buffers, events, contexts, and queues in the GPU memory.
##Usage
The following example shows how a user can use the GEMM function to modify a matrix C.
include("sgemm.jl")
A = convert(Array{Float32,2}, [[11, 12, 13, 14, 15]';[21, 22, 23, 24, 25]';[31, 32, 33, 34, 35]';[41, 42, 43, 44, 45]'])
B = convert(Array{Float32,2}, [[11, 12, 13]';[21, 22, 23]';[31, 32, 33]';[41, 42, 43]';[51, 52, 53]'])
C = convert(Array{Float32,2}, [[11, 12, 13]';[21, 22, 23]';[31, 32, 33]';[41, 42, 43]'])
OpenCLBLAS.sgemm!('N','N',Float32(10),A,B, Float32(20), C)The result can be seen in this query:
julia> C
4x3 Array{Float32, 2}:
21370.0 22040.0 22710.0
37070.0 38240.0 39410.0
52770.0 54440.0 56110.0
68470.0 70640.0 72810.0
julia>##Progress
I'll add more OpenCLBLAS functions as I test them myself.
The following functions have been tested on a Windows x64 PC with an NVIDIA GPU:
-sGEMM
#####This project is based off the work of these projects:
OpenCL.jl by Jake Bolewski and Valentin Churavy
CUBLAS.jl by Nick Henderson
##License
The license can be found in the file 'LICENSE'.