Work

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Year: 2022

Type: article

Abstract: Computational biology has increasingly turned to agent-based modeling to explore complex biological systems. Biological diffusion (diffusion, decay, secretion, and uptake) is a key driver of biological tissues. GPU computing can vastly accelerate the diffusion and decay operators in the partial differential equations used to represent biological transport in an agent-based biological modeling system. In this article, we utilize OpenACC to accelerate the diffusion portion of PhysiCell, a cross-platform agent-based biosimulation framework. We demonstrate an almost 40× speedup on the state-of-the-art NVIDIA Ampere 100 GPU compared to a serial run on AMD’s EPYC 7742. We also demonstrate 9× speedup on the 64-core AMD EPYC 7742 multicore platform. By using OpenACC for both the CPUs and the GPUs, we maintain a single source code base, thus creating a portable yet performant solution. With the simulator’s most significant computational bottleneck significantly reduced, we can continue cancer simulations over much longer times.

Source: Computing in Science & Engineering

DOI: 10.1109/mcse.2022.3226602

Authors: Matt Stack, Paul Macklin, Robert Searles, Sunita Chandrasekaran

Institutions: Nvidia (United States), Indiana University Bloomington, Indiana University, University of Delaware

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: SHF: Small: PAW: Novel Functionality in Programming Models to Productively Abstract Wavefront Parallel Pattern