Workflow optimization of performance and quality of service for bioinformatics application in high performance computing

Abstract Nowadays, High Performance Computing (HPC) systems commonly used in bioinformatics, such as genome sequencing, incorporate multi-processor architectures. Typically, most bioinformatics applications are multi-threaded and dominated by memory-intensive operations, which are not designed to take full advantage of these HPC capabilities. Therefore, the application end-user is responsible for optimizing the application performance and improving scalability with various performance engineering concepts. Additionally, most of the HPC systems are operated in a multi-user (or multi-job) environment; thus, Quality of Service (QoS) methods are essential for balancing between application performance, scalability and system utilization. We propose a QoS workflow that optimizes the balancing ratio between parallel efficiency and system utilization. Accordingly, our proposed optimization workflow will advise the end user of a selection criteria to apply toward resources and options for a given application and HPC system architecture. For example, the BWA-MEM algorithm is a popular and modern algorithm for aligning human genome sequences. We conducted various case studies on BWA-MEM using our optimization workflow, and as a result compared to a state-of-the-art baseline, the application performance is improved up to 67%, scalability extended up to 200%, parallel efficiency improved up to 39% and overall system utilization increased up to 38%.