Efficient Parameter Tuning for a Structure-Based Virtual Screening HPC Application

TL;DR

This paper introduces two parallel Bayesian Optimization-based autotuning methods for virtual screening software in HPC environments, significantly improving parameter configurations for better quality-throughput trade-offs.

Contribution

It extends sequential Bayesian Optimization with parallel asynchronous approaches and integrates ML predictions to efficiently tune parameters in distributed HPC virtual screening applications.

Findings

Configurations up to 42% better than default and state-of-the-art autotuner.

Effective exploration of parameter space with domain-specific metrics.

Improved quality-throughput balance in drug discovery workflows.

Abstract

Virtual screening applications are highly parameterized to optimize the balance between quality and execution performance. While output quality is critical, the entire screening process must be completed within a reasonable time. In fact, a slight reduction in output accuracy may be acceptable when dealing with large datasets. Finding the optimal quality-throughput trade-off depends on the specific HPC system used and should be re-evaluated with each new deployment or significant code update. This paper presents two parallel autotuning techniques for constrained optimization in distributed High-Performance Computing (HPC) environments. These techniques extend sequential Bayesian Optimization (BO) with two parallel asynchronous approaches, and they integrate predictions from Machine Learning (ML) models to help comply with constraints. Our target application is LiGen, a real-world virtual screening software for drug discovery. The proposed methods address two relevant challenges: efficient exploration of the parameter space and performance measurement using domain-specific metrics and procedures. We conduct an experimental campaign comparing the two methods with a popular state-of-the-art autotuner. Results show that our methods find configurations that are, on average, up to 35-42% better than the ones found by the autotuner and the default expert-picked LiGen configuration.