用混合精度加速大规模基因关联分析，突破30万样本的计算瓶颈
Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression
- 设计基于张量核心的混合精度算法，降低数据移动开销
- 在30.5万样本上实现1.805 ExaOp/s性能，比传统软件快10万倍
- 适合需要处理超大规模基因组数据的研究者和高性能计算团队
我们利用NVIDIA Ampere/Hopper GPU中张量核心在FP64/FP32/FP16/INT8与FP64/FP32/FP16/FP8/INT8间日益扩大的性能差距，提升对英国生物银行30.5万例患者进行多变量全基因组关联分析（GWAS）时的计算效率。受减少数据移动启发的分块自适应精度线性代数技术，使低精度GPU算术获得更高显著性。核岭回归（KRR）在GWAS中的核心是计算密集型的三次复杂度矩阵操作，阻碍了向更大规模人群、基因型和表型维度的扩展。通过重新设计欧氏距离计算以利用INT8张量核心并利用对称性，加速了KRR矩阵生成；通过部署新型四精度基于楚列斯基分解的求解器，在接近满载的Alps系统上达到1.805混合精度ExaOp/s，相比仅用CPU的前沿REGENIE GWAS软件提速五数量级。
原文摘要 · Abstract (English)
We exploit the widening margin in tensor-core performance between [FP64/FP32/FP16/INT8,FP64/FP32/FP16/FP8/INT8] on NVIDIA [Ampere,Hopper] GPUs to boost the performance of output accuracy-preserving mixed-precision computation of Genome-Wide Association Studies (GWAS) of 305K patients from the UK BioBank, the largest-ever GWAS cohort studied for genetic epistasis using a multivariate approach. Tile-centric adaptive-precision linear algebraic techniques motivated by reducing data motion gain enhanced significance with low-precision GPU arithmetic. At the core of Kernel Ridge Regression (KRR) techniques for GWAS lie compute-bound cubic-complexity matrix operations that inhibit scaling to aspirational dimensions of the population, genotypes, and phenotypes. We accelerate KRR matrix generation by redesigning the computation for Euclidean distances to engage INT8 tensor cores while exploiting symmetry.We accelerate solution of the regularized KRR systems by deploying a new four-precision Cholesky-based solver, which, at 1.805 mixed-precision ExaOp/s on a nearly full Alps system, outperforms the state-of-the-art CPU-only REGENIE GWAS software by five orders of magnitude.
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