Performance modeling for data distribution in heterogeneous computing systems: work in progress
K Vanishree, Madhura Purnaprajna · Compilers, Architecture, and Synthesis for Embedded Systems · 2018
Balanced data distribution among devices in Heterogeneous Computing System (HCS) is key to improved application performance. This work presents a model that estimates data distribution ratio for CPU-GPU co-execution of a data-parallel application in an HCS node. This estimation is based on relative application performance on the devices. To find the relative performance, we build individual performance models of each device (CPU-GPU) using off-line profile information. Our CPU and GPU models have an average performance estimation error of 8.64% and 9.03% respectively w.r.t. the measured performance, across a set of data-parallel benchmarks. With CPU-GPU co-execution, an average performance improvement of 37.3% is seen across benchmarks.