As is probably well known to most, MATLAB R2010b includes support to use GPUs. I have tried to run my SGeMM and DGeMM benchmarks (see http://wiki.accelereyes.com/wiki/index.php/Jacket_Floating_Point_Performance_(GFlops)
) on MATLAB-GPU (as I will name the MATLAB GPU facilitated method in the following). The test with a Tesla C2050 gives the following result when I take the ratio of Jacket GFlops relative to MATLAB-GPU GFlops.
As seen from the measurement, Jacket is superior to MATLABs implementation for the GPU - even for large matrices. The matrix size where the GPU breaks even with the CPU is around size 180 x 180 for Jacket and size 450 x 450 for MATLAB-GPU. This also says that Jacket is way faster than MATLAB-GPU.
I will do some more tests with other GPUs and publish these on Torben's Corner (see http://wiki.accelereyes.com/wiki/index.php/Torben%27s_Corner
). I have used MATLAB-GPU and it annoys me that the GPU needs at least 1.3 in CUDA capability - it is annoying as I usually use my laptop for development of the raw code. Also there is no support for lazy execution, and the function support is very limited.
I have not yet tried to map a full application to MATLAB-GPU. If someone has tried that it would be very interesting to have some info on that.
PS: I add the raw data below where you can see the individual performance. The title says GTX465 - I made a typo here. It really is the C2050. I'll change that later. The first figure is single precision and the second is double precision.