An Event-Driven Approach To Genotype Imputation On A Custom RISC-V FPGA Cluster

TL;DR

This paper introduces an event-driven genotype imputation method implemented on a custom RISC-V FPGA cluster, achieving significant speedups over traditional x86 solutions by leveraging concurrency and optimization techniques.

Contribution

It presents a novel event-driven FPGA implementation of the Li and Stephens model for genotype imputation, demonstrating substantial performance improvements.

Findings

270X reduction in processing time on 48 FPGAs

Approximate 5 orders of magnitude speedup with optimization

Scalable performance across multiple FPGA resources

Abstract

This paper proposes an event-driven solution to genotype imputation, a technique used to statistically infer missing genetic markers in DNA. The work implements the widely accepted Li and Stephens model, primary contributor to the computational complexity of modern x86 solutions, in an attempt to determine whether further investigation of the application is warranted in the event-driven domain. The model is implemented using graph-based Hidden Markov Modeling and executed as a customized forward/backward dynamic programming algorithm. The solution uses an event-driven paradigm to map the algorithm to thousands of concurrent cores, where events are small messages that carry both control and data within the algorithm. The design of a single processing element is discussed. This is then extended across multiple FPGAs and executed on a custom RISC-V NoC FPGA cluster called POETS. Results demonstrate how the algorithm scales over increasing hardware resources and a 48 FPGA run demonstrates a 270X reduction in wall-clock processing time when compared to a single-threaded x86 solution. Optimisation of the algorithm via linear interpolation is then introduced and tested, with results demonstrating a wall-clock reduction time of approx. 5 orders of magnitude when compared to a similarly optimised x86 solution.