Performance Portability of Generated Cardiac Simulation Kernels Through Automatic Dimensioning and Load Balancing on Heterogeneous Nodes

Abstract

Electrophysiology simulation applications, such as the community-developed OPENCARP framework for in-silico experiments, involve applying a broad range of ionic model kernels with different computational weights and arithmetic intensity characteristics. Efficiently processing such kernels on modern heterogeneous architectures necessitates to accurately dimension the set of computing resources to use and to actively balance the load on the available computing units, to account for discrepancies in kernel duration and distinct computing unit speeds. We thus propose the following contributions: 1) the adaptation of an existing load-balancing algorithm to transparently manage the mapping of these ionic model kernels onto the heterogeneous units of a computing node; 2) a resource dimensioning heuristic that constraints the number of devices that should be used to maximize efficiency, according to the selected ionic models' computational weight; 3) the integration of these mechanisms in OPENCARP, building on prior work that took advantage of LLVM's MLIR framework to generate multiple device-specialized variants of kernels from ionic models expressed in OPENCARP's high-level DSL; 4) a thorough experimentation of the mechanisms on a comprehensive series of 30 ionic models provided by OPENCARP. The experiments show that when using the combination of the load-balancing algorithm and the resource dimensioning heuristic to compute each ionic model, the geometric mean of speedup is 9.97× with respect to the original multi-threaded code on an architecture with two A100 GPUs and 2× 32-cores AMD Zen3 CPUs.