Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures

Abstract

Next generation workloads, such as genome sequencing, have an astounding impact in the healthcare sector. Sequence alignment, the first step in genome sequencing, has experienced recent breakthroughs, which resulted in next generation sequencing (NGS). As NGS applications are memory bounded with random memory access patterns, we propose the use of high bandwidth memories like 3D stacked HBM2, instead of traditional DRAMs like DDR4, along with energy efficient compute cores to improve both performance and energy efficiency. Three state-of-the-art NGS applications, Bowtie2, BWA-MEM and HISAT2, are used as case studies to explore and optimize NGS computing architectures. Then, using the gem5-X architectural simulator, we obtain an overall 68% performance improvement and 71% energy savings using HBM2 instead of DDR4. Furthermore, we propose an architecture based on ARMv8 cores and demonstrate that 16 ARMv8 64-bit OoO cores with HBM2 outperforms 32-cores of Intel Xeon Phi Knights Landing (KNL) processor with 3D stacked memory. Moreover, we show that by using frequency scaling we can achieve up to 59% and 61% energy savings for ARM in-order and OoO cores, respectively. Lastly, we show that many ARMv8 in-order cores at 1.5GHz match the performance of fewer OoO cores at 2GHz, while attaining 4.5x energy savings.

Gem5-X

“…It can also be used to explore and optimize architectures for HPC workloads. In particular, the work in Reference [49] demonstrates the use of gem5-X in enabling architectural exploration for the next-generation of genome sequencing HPC workloads.…”

“…Consequently, this situation has led to data centers consuming 0.8% (200 TWh) of the global energy in 2019 [24]. These new emerging applications, such as video analytics [2], autonomous driving, natural language processing, content recommendation [37], bioinformatics and genome sequencing [49], have different performance/energy requirements and are deployed on a variety of different platforms. To optimize these performance/energy constraints, a system-level simulator is required that is capable of simulating multi-threaded applications in a full-system environment with a complete operating system (OS) on a heterogeneous multi-core system.…”