# Space Efficient Sequence Alignment for SRAM-Based Computing: X-Drop on the Graphcore IPU

arXiv: https://arxiv.org/abs/2304.08662 · HTML: https://mlsanity.com/paper/2304.08662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2023.

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

- [What Artificial Intelligence can do for High-Performance Computing systems?](https://mlsanity.com/paper/2602.00014/) (2026)

## Abstract (arXiv)

Dedicated accelerator hardware has become essential for processing AI-based workloads, leading to the rise of novel accelerator architectures. Furthermore, fundamental differences in memory architecture and parallelism have made these accelerators targets for scientific computing. The sequence alignment problem is fundamental in bioinformatics; we have implemented the X-Drop algorithm, a heuristic method for pairwise alignment that reduces search space, on the Graphcore Intelligence Processor Unit (IPU) accelerator. The X-Drop algorithm has an irregular computational pattern, which makes it difficult to accelerate due to load balancing. Here, we introduce a graph-based partitioning and queue-based batch system to improve load balancing. Our implementation achieves 10× speedup over a state-of-the-art GPU implementation and up to 4.65× compared to CPU. In addition, we introduce a memory-restricted X-Drop algorithm that reduces memory footprint by 55× and efficiently uses the IPU's limited low-latency SRAM. This optimization further improves the strong scaling performance by 3.6×.