Tesla C2050 versus C1060 on Real MATLAB Applications

John Melonakos Benchmarks 7 Comments

Following our recent Jacket v1.4 Fermi architecture release, many of you requested data comparing the new NVIDIA Fermi-based Tesla C2050 versus the older Tesla C1060. Over the years, AccelerEyes has developed an extensive suite of benchmark MATLAB applications, which are included in every Jacket installation. Using this suite of tests, we compared performance of the C2050 vs C1060 and are pleased to report the results here. We hope this information will be useful to Jacket programmers. All tests were run on the same standard workstation with Jacket 1.4. The only thing that changed was the actual GPU board. In every case the C2050 beat the C1060. Double-precision examples on the Fermi-based board outperformed the older board by 50% in every …

SGEMM, MTIMES & CUBLAS performance on the GPU

ArrayFire Benchmarks, CUDA 5 Comments

AccelerEyes is focused on not only providing the most easy to use GPU programming platform for CUDA capable GPUs by leveraging the MATLAB® language, our engineering organization is always looking for ways to improve the performance of all areas in the Jacket platform. A case in point is some recent work with matrix multiplication, specifically (Single General Matrix Multiply) SGEMM, or MTIMES.  The Jacket 1.3 release was based on CUBLAS for matrix multiplication and given the importance of matrix multiplication to so many of our customers, we decided to find out if we could improve performance of the function. Update: The new MTIMES routine in Jacket 1.4 has improved sigificantly since these benchmarks of the Release Candidate were taken. Have …