First Steps towards Efficient Genome Assembly on ARM-Based HPC

Abstract

Exponential advances in computational power have fueled advances in many disciplines, and biology is no exception. High-Performance Computing (HPC) is gaining traction as one of the essential tools in scientific research. Further advances to exascale capabilities will necessitate more energy-efficient hardware. In this article, we present our efforts to improve the efficiency of genome assembly on ARM-based HPC systems. We use vectorization to optimize the popular genome assembly pipeline of minimap2, miniasm, and Racon. We compare different implementations using the Scalable Vector Extension (SVE) instruction set architecture and evaluate their performance in different aspects. Additionally, we compare the performance of autovectorization to hand-tuned code with intrinsics. Lastly, we present the design of a CPU dispatcher included in the Racon consensus module that enables the automatic selection of the fastest instruction set supported by the utilized CPU. Our findings provide a promising direction for further optimization of genome assembly on ARM-based HPC systems.

Easing genomic surveillance: A comprehensive performance evaluation of long-read assemblers across multi-strain mixture data of HIV-1 and Other pathogenic viruses for constructing a user-friendly bioinformatic pipeline

“…Alternatively, a properly optimized code can achieve performance comparable to native x86 systems through dynamic binary translation (e.g., Rosetta). 81 An 8 GB memory is sufficient for either viral or bacterial genome assembly, while a 16 GB RAM or higher is recommended for larger genomes, such as human or other mammals. It’s worth noting that this study utilized 96 GB memory for all assemblers.…”

“…Alternatively, a properly optimized code can achieve performance comparable to native x86 systems through dynamic binary translation (e.g., Rosetta). 81 …”