Matchmaking Applications and Partitioning Strategies for Efficient Execution on Heterogeneous Platforms

Abstract

Heterogeneous platforms are mixes of different processing units. The key factor to their efficient usage is workload partitioning. Both static and dynamic partitioning strategies have been defined in previous work, but their applicability and performance differ significantly depending on the application to execute. In this paper, we propose an application-driven method to select the best partitioning strategy for a given workload. To this end, we define an application classification based on the application kernel structure -- i.e., The number of kernels in the application and their execution flow. We also enable five different partitioning strategies, which mix the best features of both static and dynamic approaches. We further define the performance-driven ranking of all suitable strategies for each application class. Finally, we match the best partitioning to a given application by simply determining its class and selecting the best ranked strategy for that class. We test the matchmaking on six representative applications, and demonstrate that the defined performance ranking is correct. Moreover, by choosing the best performing partitioning strategy, we can significantly improve application performance, leading to average speedup of 3.0x/5.3x over the Only-GPU/Only-CPU execution, respectively.