Accelerating minor allele frequency computation with graphics processors

Mian Lu, Jiuxin Zhao, Qiong Luo, Bingqiang Wang

Abstract: The computation of minor allele frequency (MAF) is at the core of a Genome-Wide Association Study (GWAS). Due to the high computation intensity and high precision requirement, so far the scale of MAF computation analysis is up to hundreds of individuals. To enable the computation for thousands of individuals, we have developed GAMA, a high performance MAF computation program with GPU acceleration. Specifically, we design a parallel reduction algorithm that matches the GPU's data-parallel architecture. To implement the new algorithm efficiently on the GPU, we utilize the fast, on-chip local memory shared within each GPU multiprocessor effectively. To avoid user-level thread synchronization, we exploit the GPU thread-warp based scheduling. Furthermore, we address the floating point underflow issue through a logarithm transformation. As a result, GAMA enables MAF computation for up to a thousand individuals for the first time. On a server equipped with an NVIDIA Tesla C2070 GPU and two Intel Xeon E5520 2.27 GHz CPUs, GAMA outperforms a state-of-the-art single-threaded MAF computation tool and our optimized parallel implementation (16-threaded) on the CPU by around 47 and 3.5 times, respectively.