Parallel Acceleration of Genome Variation Detection on Multi-Zone Heterogeneous System

Abstract

Genomic variation is critical for understanding the genetic basis of disease. Pindel, a widely used structural variant caller, leverages short-read sequencing data to detect variation at single-base resolution; however, its hotspot module imposes substantial computational demands, limiting efficiency in large-scale whole-genome analyses. Heterogeneous architectures offer a promising solution, yet disparities in hardware design and programming models preclude direct porting of the original algorithm. To address this, we introduce MTPindel, a novel heterogeneous parallel optimization framework tailored to the MT-3000 processor. Focusing on Pindel’s most compute-intensive modules, we design multi-core and task-level parallel algorithms that exploit the MT-3000’s accelerator domains to balance and accelerate workload distribution. On 128 MT-3000–equipped nodes of the Tianhe next-generation supercomputer, MTPindel achieves an impressive 122.549 times of speedup and 95.74% parallel efficiency, with only a 0.74% error margin relative to the original implementation. This work represents a pioneering effort in heterogeneous parallelization for variant detection, paving the way for rapid, large-scale genomic analyses in research and clinical settings.