Energy-Aware Performance Characterization of Kokkos Kernels Across Precision Levels

Abstract

Kokkos is a performance-portable programming model used in GPU-accelerated HPC applications comprising kernels with diverse computational and memory behaviors. We characterize kernels from two applications to evaluate how FP32, FP64, and mixed FP32/FP64 affect arithmetic intensity, performance throughput, and GPU energy consumption. We introduce an Energy-Aware Roofline Model that incorporates energy into performance analysis and a Thermal-Aware Inter-Kernel Energy Model that visualizes energy consumption alongside duration, GPU temperature, and execution order. We study both applications with: (1) fine-grained, application-level instrumentation through the Kokkos Performance Tools PAPI Connector and (2) coarse-grained PAPI instrumentation applied to a dedicated-kernel benchmark using vendor-specific power management components. Our analysis shows that precision effects vary by application, kernel, and workload. Mixed FP32/FP64 kernels generally achieve arithmetic intensity comparable to FP32, with modestly lower throughput and, in most cases, a modest increase in energy consumption. FP64 kernels generally exhibit the highest energy consumption among the evaluated precision configurations.