Computing Acceleration to Genome-Wide Association Study Based on CPU/FPGA Heterogeneous System

Genome Wide Association Study (GWAS) reveals the influence of single nucleotide polymorphisms (SNP) and other genetic markers on the complex genetic disease traits, making a significant contribution to the prevention and treatment of genetic diseases. Since GWAS needs to calculate large numbers of pairwise tests, it is highly time-consuming and this limits its widespread promotion in many medical applications. To solve the above problem, many studies deploy GWAS on the CPU/GPU platform. By taking advantages of GPUs' high parallelism, the completion time of GWAS can be significantly decreased. However, GPUs have high energy consumption, which increases GWAS's operating cost. Considering FPGA has low power, high parallelism and programmability, this article aims to use FPGA to design a hardware accelerator dedicated to GWAS for the purpose of optimizing both completion time and energy cost of GWAS. First, the CPU/FPGA heterogenous computing system is built, and the computationally-intensive GWAS tasks such as the contingency table creation (CTC) and Kirkwood Superposition Approximation (KSA) screening are offloaded to run on FPGA. Then, a specific FPGA architectures dedicated to CTC and KSA tasks of GWAS are implemented by using a set of architectural design technologies such as hardware and software co-optimization, systolic array, loop unrolling, pipeline and parallelism. To evaluate the performance of FPGA, we compare its completion time, energy cost and performance per Watt with those of CPU and GPU. The results show the FPGA and GPU can respectively achieve 105-fold and 148-fold speedup compared with CPU. Although FPGA's computing performance is a bit lower than that of GPU, it has higher energy efficiency and its performance per Watt is 1.4 times that of GPU.