Nvidia has said that most of the outlandish performance increase figures touted by GPGPU vendors was down to poor original code rather than sheer brute force computing power provided by GPUs.
Both AMD and Nvidia have been using real-world code examples and projects to promote the performance of their respective GPGPU accelerators for years, but now it seems some of the eye popping figures including speed ups of 100x or 200x were not down to just the computing power of GPGPUs. Sumit Gupta, GM of Nvidia’s Tesla business said that such figures were generally down to starting with unoptimized CPU code.
During Intel’s Xeon Phi pre-launch press conference call, the firm cast doubt on some of the orders of magnitude speed up claims that had been bandied about for years. Now Gupta told The INQUIRER that while those large speed ups did happen, it was possible because of poorly optimized code to begin with, thus the bar was set very low.
Gupta said, “Most of the time when you saw the 100x, 200x and larger numbers those came from universities. Nvidia may have taken university work and shown it and it has an 100x on it, but really most of those gains came from academic work. Typically we find when you investigate why someone got 100x [speed up] is because they didn’t have good CPU code to begin with. When you investigate why they didn’t have good CPU code you find that typically they are domain scientist’s not computer science guys – biologists, chemists, physics – and they wrote some C code and it wasn’t good on the CPU. It turns out most of those people find it easier to code in CUDA C or CUDA Fortran than they do to use MPI or Pthreads to go to multi-core CPUs, so CUDA programming for a GPU is easier than multi-core CPU programming.”
Sign up for our Technology Newsletter 
Comments