This implements 5 different algorithms for the parallel scan problem in pytorch. The algorithms are:
- Naive: The naive algorithm that uses a for loop to compute the scan.
- Original: Code written by https://twitter.com/francoisfleuret which motivated this project.
- Heinsen: The algorithm described in https://arxiv.org/abs/2311.06281
- CUDA_V1: The (currently fastest) algorithm which uses a custom CUDA kernel + CUB to do the reduction on a block level. This is the recommended algorithm to use.
- CUDA_V2: This does a device level reduction using CUB, it is slower than CUDA_V1 but works for much larger input size.
Benchmarking was done on a 3090 GPU.
original_pscan_fn(A, X, Y_init) setup: from main import original_pscan_fn 911.40 us 1 measurement, 1000 runs , 1 thread
heinsen_pscan(A, X, Y_init) setup: from main import heinsen_pscan 783.34 us 1 measurement, 1000 runs , 1 thread
pscan_cuda_fn(A, X, Y_init) setup: from main import pscan_cuda_fn 113.88 us 1 measurement, 1000 runs , 1 thread
pscan_cuda_v2_fn(A, X, Y_init) setup: from main import pscan_cuda_v2_fn 149.98 us 1 measurement, 1000 runs , 1 thread
backward_wrapper(pscan_cuda_fn,A, X, Y_init) setup: from main import pscan_cuda_fn, backward_wrapper 394.81 us 1 measurement, 1000 runs , 1 thread
backward_wrapper(heinsen_pscan, A, X, Y_init) setup: from main import heinsen_pscan, backward_wrapper 1.63 ms 1 measurement, 1000 runs , 1 thread
backward_wrapper(original_pscan_fn, A, X, Y_init) setup: from main import original_pscan_fn, backward_wrapper 2.07 ms 1 measurement, 1000 runs , 1 thread
backward_wrapper(pscan_cuda_v2_fn, A, X, Y_init) setup: from main import pscan_cuda_v2_fn, backward_wrapper 453.88 us 1 measurement, 1000 runs , 1 thread