Parallelizing Irregular Computations for Molecular Docking

AUTODOCK is a molecular docking software widely used in computational drug design. Its time-consuming executions have motivated the development of AUTODOCK-GPU, an OpenCL-accelerated version that can run on GPUs and CPUs. This work discusses the development of AUTODOCK-GPU from a programming perspective, detailing how our design addresses the irregularity of AUTODOCK while pushing towards higher performance. Details on required data transformations, re-structuring of complex functionality, as well as the performance impact of different configurations are also discussed. While AUTODOCK-GPU reaches speedup factors of 341x on a Titan V GPU and 51x on a 48-core Xeon Platinum 8175M CPU, experiments show that performance gains are highly dependent on the molecular complexity under analysis. Finally, we summarize our preliminary experiences when porting AUTODOCK onto FPGAs.