This is a WIP CUDA implementation of the Conway's Game of Life created to explore the wonderful world of GPGPU with a visualizer written using SDL2.
All performance measured on an Intel i9 9900K and an RTX 2080Ti (PCIe 3.0 x8).
- CPU: ~60 million cells/sec
- GPU (CUDA): ~3000 million cells/sec
Performance in this case is limited by the CPU <-> GPU memory transfer bandwidth which accounts for ~93% of the runtime. Measured banwidth on a PCIe 3.0 x8 slot is ~6.5 GB/s (~80% utilization), and ~14 GB/s (~88% utilization) on a PCIe 3.0 x16 slot which leads to a significant increase in performance, up to 4300 million cells/sec.
- CPU: ~60 million cells/sec
- GPU (CUDA): ~3000 million cells/sec
Performance is again limited by the CPU <-> GPU memory transfer bandwidth, and therefore largely independent of the grid size. The GPU performance being no less than the empty grid case despite unpredictable branches is likely a testament to nvcc's kernel optimization ability.
- CPU: ~60 million cells/sec
- GPU: ~15000 million cells/sec
To alleviate the bottleneck caused by the memory transfer, we now use the remaining, previously unused, 7 bits of the uint8_t matrix which we pass to the kernel to compute not 1, but 7 generations of the GoL, without ever copying data back to the CPU.
However, as it is not possible to create a robust global (inter-block) thread barrier in CUDA, this is achieved through several kernel invocations which comes with an overhead which may be investigated in the future.
Further overhead, which is preventing us from reaching a theoretical 8 fold speed increase, comes from the bitwise operations required, and it is unavoidable. Nevertheless, using SIMD (AVX2) for CPU-side bitwise operations yielded a significant performance boost.
TODO...