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Exploiting Fine-Grained Task-Level Parallelism for Variant Calling Acceleration
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DOI：10.1109/TPDS.2025.3600285.png)
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Abstract
En 中文
Variant calling, which identifies genomic differences relative to a reference genome, is critical for understanding disease mechanisms, identifying therapeutic targets, and advancing precision medicine. However, as two critical stages in this process, serial processing in local assembly and the computational dependencies in Pair-HMM make variant calling highly time-consuming. Moreover, optimizing only one of these stages often shifts the performance bottleneck to the other. This article observes that the similarity between reads allows parallel processing in the local assembly and that alignment information from the local assembly can significantly diminish the burdensome computations in Pair-HMM. Accordingly, this article co-optimizes the software and hardware for both steps to achieve the best performance. First, we collect <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-mer locations in each read during the local assembly process and utilize the similarity between reads to make it parallel. Second, we propose the mPair-HMM algorithm, leveraging location information to split a Pair-HMM computation task into multiple independent sub-tasks, improving the computation’s parallelism. To fully exploit the parallelism stemming from the novel algorithms, we propose an end-to-end accelerator VCAx for variant calling that accelerates both stages in collaboration. Evaluation results demonstrate that our implementation achieves up to a 7× speedup over the GPU baseline for local assembly and a 3.16× performance improvement compared to the state-of-the-art ASIC implementation for Pair-HMM.
Keywords:
Variant calling
local assembly
pair-HMM
hardware accelerator
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