Simple n-body program to test Cuda performance
As long CUDA is installed and nvcc is in your PATH, you should be able to do:
make
./nvCuda01.bin
./nvCuda02.bin
./nvCuda03.bin
This repository contains a few progressive examples of a compute-only n-body calculation of the Biot-Savart influence of N desingularized vorticies on one another.
Program nvCuda01.cu is the simplest implementation. On the CPU side, the program parallelizes
with a basic OpenMP parallel for loop over the target particles. On the GPU side, we use CUDA
without unified or pinned memory (full transfers), with one target particle per "thread."
Program nvCuda02.cu speeds this up considerably. The CPU now uses omp simd to vectorize the
inner loop over source particles. The GPU uses shared memory to load blocks of source particles
in a coalesced manner before all threads operate on that block. This program represents the
"80" part of the "80-20 rule": that you can go most of the way with some simple methods.
Program nvCuda03 adds some enhancements in an attempt to eke out even more performance, though
only on the GPU side. First, we moved the GPU timers to not count allocation and deallocation,
in order to be more consistent with the CPU timers. Second, we now break the computation up
along the source-particle dimension, to allow for greater concurrency, which requires atomicAdd
to write results back to main GPU memory. Finally, we added support for multiple GPU systems.
Finally, nvCuda04 saves one flop per inner loop by presquaring the target radius, but adds
six more by performing Kahan summation on the accumulators. This further reduces errors inherent
in summing large arrays of numbers, but seems incompatible with the omp simd clause.
If you want to see how other libraries and methodologies improve performance on this problem, look at some of my other repositories: