Endeavor: Efficient PairHMM for Detection of DNA Variants in Genome-Scale Datasets

2026-06-24

AI Summary

This work addresses the high computational cost of the PairHMM algorithm in genomic variant calling, which becomes a severe performance bottleneck when processing long DNA sequences, as existing acceleration approaches suffer from limited scalability. The authors propose Endeavor, a novel framework that reformulates PairHMM computation to transcend the conventional reliance on anti-diagonal parallelism. Endeavor introduces, for the first time, fine-grained row-level parallelism coupled with an efficient synchronization mechanism, and integrates cross-platform SIMD vectorization to deliver high-throughput, high-accuracy acceleration on both CPUs (Intel/AMD) and GPUs (NVIDIA/AMD). Experimental results demonstrate that Endeavor achieves up to 2.14× higher peak throughput than GKL and over 2× that of GATK HaplotypeCaller on CPUs, while attaining up to 2.05× speedup on GPUs and enabling efficient processing of sequences as long as 100,000 base pairs.

Abstract

DNA variant calling represents a key operation in bioinformatics pipelines that aims at identifying genetic variants. Given an evidenced explosion in genomic data availability, there is an urgent need for a high-performant, portable and efficient solution for variant calling, which can further improve our understanding of genomic structure and genetic basis for complex diseases. In its most common formulation, the Pair Hidden Markov Model (PairHMM) algorithm for variant calling stands as the main bottleneck in the pipeline, accounting for up to 70% of the execution time in large-scale genomic datasets. The state-of-the-art approaches for accelerating PairHMM in CPUs and GPUs do not scale to long DNA sequences and only explore very limited anti-diagonal data parallelism, which yields poor performance. In this work, Endeavor is proposed as a new parallelization strategy for PairHMM that redefines its traditional formulation to explore row-level fine-grained parallelism without loss in solution accuracy. Based on this, a novel and portable SIMD-based approach is derived for efficient and high-performance processing of short and long sequences in CPUs and GPUs, leveraging novel levels of parallelism and synchronization to achieve high throughput in sequences up to 100k basepairs for the first time. Evaluation on Intel and AMD CPUs shows that Endeavor outperforms GKL up to 2.14x in peak throughput and GATK HaplotypeCaller by at least 2x in real-world datasets, while NVIDIA and AMD GPUs achieve up to 2.05x speedups in genome-scale datasets when compared to state-of-the-art GPU-based methods.

Problem

• DNA variant calling
• PairHMM
• genomic data
• performance bottleneck
• long DNA sequences

Innovation

• PairHMM
• fine-grained parallelism
• SIMD
• variant calling
• genome-scale