Question

I am interested to know whether anyone has written an application that takes advantage of a GPGPU by using, for example, nVidia CUDA. If so, what issues did you find and what performance gains did you achieve compared with a standard CPU?

Answer 1

I have been doing gpgpu development with ATI’s stream SDK instead of Cuda. What kind of performance gain you will get depends on a lot of factors, but the most important is the numeric intensity. (That is, the ratio of compute operations to memory references.)

A BLAS level-1 or BLAS level-2 function like adding two vectors only does 1 math operation for each 3 memory references, so the NI is (1/3). This is always run slower with CAL or Cuda than just doing in on the cpu. The main reason is the time it takes to transfer the data from the cpu to the gpu and back.

For a function like FFT, there are O(N log N) computations and O(N) memory references, so the NI is O(log N). If N is very large, say 1,000,000 it will likely be faster to do it on the gpu; If N is small, say 1,000 it will almost certainly be slower.

For a BLAS level-3 or LAPACK function like LU decomposition of a matrix, or finding its eigenvalues, there are O( N^3) computations and O(N^2) memory references, so the NI is O(N). For very small arrays, say N is a few score, this will still be faster to do on the cpu, but as N increases, the algorithm very quickly goes from memory-bound to compute-bound and the performance increase on the gpu rises very quickly.

Anything involving complex arithemetic has more computations than scalar arithmetic, which usually doubles the NI and increases gpu performance.

_{(source: earthlink.net)}

Here is the performance of CGEMM — complex single precision matrix-matrix multiplication done on a Radeon 4870.