# GPU Fast Convolution via the Overlap-and-Save Method in Shared Memory

arXiv: https://arxiv.org/abs/1910.01972 · HTML: https://mlsanity.com/paper/1910.01972/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction](https://mlsanity.com/paper/2112.15445/) (2021)

## Abstract (arXiv)

We present an implementation of the overlap-and-save method, a method for the convolution of very long signals with short response functions, which is tailored to GPUs. We have implemented several FFT algorithms (using the CUDA programming language) which exploit GPU shared memory, allowing for GPU accelerated convolution. We compare our implementation with an implementation of the overlap-and-save algorithm utilizing the NVIDIA FFT library (cuFFT). We demonstrate that by using a shared memory based FFT we can achieved significant speed-ups for certain problem sizes and lower the memory requirements of the overlap-and-save method on GPUs.