PeaPaw

Abstract

Performance and energy are two major concerns for application development on heterogeneous platforms. It is challenging for application developers to fully exploit the performance/energy potential of heterogeneous platforms. One reason is the lack of reliable prediction of the system’s performance/energy before application implementation. Another reason is that a heterogeneous platform presents a large design space for workload partitioning between different processors. To reduce such development cost, this article proposes a framework, PeaPaw, to assist application developers to identify a workload partition (WP) that has high potential leading to high performance or energy efficiency before actual implementation. The PeaPaw framework includes both analytical performance/energy models and two sets of workload partitioning guidelines. Based on the design goal, application developers can obtain a workload partitioning guideline from PeaPaw for a given platform and follow it to design one or multiple WPs for a given workload. Then PeaPaw can be used to estimate the performance/energy of the designed WPs, and the WP with the best estimated performance/energy can be selected for actual implementation. To demonstrate the effectiveness of PeaPaw, we have conducted three case studies. Results from these case studies show that PeaPaw can faithfully estimate the performance/energy relationships of WPs and provide effective workload partitioning guidelines.