Efficient Variant Calling on Human Genome Sequences Using a GPU-Enabled Commodity Cluster

Human genome sequences are very large in size and require significant compute and storage resources for processing and analysis. Variant calling is a key task performed on an individual's genome to identify different types of variants. Knowing these variants can lead to new advances in disease diagnosis and treatment. In this work, we propose a new approach for accelerating variant calling pipelines on a large workload of human genomes using a commodity cluster with graphics processing units (GPUs). Our approach has two salient features: First, it enables a pipeline stage to use GPUs and/or CPUs based on the availability of resources in the cluster. Second, it employs a mutual exclusion strategy for executing a pipeline stage on the GPUs of a cluster node so that the stages (for other sequences) can be executed using CPUs if needed. We evaluated our approach on a 8-node cluster with bare metal servers and virtual machines (VMs) containing different types of GPUs. On publicly available genome sequences, our approach was 3.6X-5X faster compared to an approach that used only the cluster CPUs.