# Parallel approach to sliding window sums

arXiv: https://arxiv.org/abs/1811.10074 · HTML: https://mlsanity.com/paper/1811.10074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Sliding Window Sum Algorithms for Deep Neural Networks](https://mlsanity.com/paper/2305.16513/) (2023)
- [Accelerating Biological Spatial Cluster Analysis with the Parallel Integral Image Technique](https://mlsanity.com/paper/2410.16291/) (2024)
- [Accelerating Machine Learning Primitives on Commodity Hardware](https://mlsanity.com/paper/2310.05218/) (2023)

## Abstract (arXiv)

Sliding window sums are widely used in bioinformatics applications, including sequence assembly, k-mer generation, hashing and compression. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost for the bioinformatics applications. We develop a generic vectorized sliding sum algorithm with speedup for window size w and number of processors P is O(P/w) for a generic sliding sum. For a sum with commutative operator the speedup is improved to O(P/log(w)). When applied to the genomic application of minimizer based k-mer table generation using AVX instructions, we obtain a speedup of over 5X.