iPuma: High-Performance Sequence Alignment on the Graphcore IPU

Abstract

String alignment algorithms are an essential tool for understanding DNA and protein sequences. They demand substantial computation in real-world applications, and are thus a prime target for hardware acceleration.However, GPUs struggle to provide sufficient acceleration. Meanwhile, the recent MIMD-capable AI accelerators such as the Graphcore Intelligence Processing Unit (IPU) have become technologically viable. In this paper we present iPuma, a new implementation of Smith-Waterman sequence alignment for the IPU, which offers generalized short and medium length, one-to-one, and many-to-many high-throughput alignments for both DNA and protein sequences. iPuma is integrated into two bioinformatics pipelines, MetaHipMer2 and PASTIS.On protein datasets, iPuma shows speedups of 2.7× and 1.6× over state-of-the-art GPU and CPU implementations, respectively. We test the scalability on up to 64 IPUs, attaining a peak scoring performance of 1763 GCUPS for protein and 1168 GCUPS for DNA sequences.