Simulating SVE-Optimised Genomics Workloads on Gem5

SVE (Scalable Vector Extension) is Arm's new vector instruction extension targeting high performance workloads. SVE offers many opportunities to optimise compute intensive workloads but, with the availability of SVE-enabled hardware still years away, we have to rely on simulation techniques in order to evaluate our implementations. Working with simulators can be tricky and it comes with many limitations but, used properly, a simulator like Gem5 is a valuable tool that can provide an opportunity to explore the possibilities opened by this new extension. As a use case, we focus our attention on the field of genomics, where the recent advent of high-throughput sequencing machines producing big amounts of genomic data has boosted the interest in efficient approximate string matching and alignment techniques. Genomics algorithms are the key computational building blocks for the downstream data analysis on resequencing projects where hundreds of GBytes of sequenced data are analysed against a reference genome in order to filter sequencing errors and detect potential genomic variation events. The computational requirements and sheer size of the input data used by these applications make them a challenging problem. Fortunately, they also exhibit a high degree of data parallelism, making them good candidates for vectorisation techniques. In this work we explore the unique opportunities that SVE provides in order to exploit the parallelism present in genomics algorithms. We discuss preliminary results, our simulation strategy, some of the obstacles and limitations we faced, and how to work around them in order to obtain meaningful results.