An adaptive offline implementation selector for heterogeneous parallel platforms

Abstract

Heterogeneous parallel platforms, comprising multiple processing units and architectures, have become a cornerstone in improving the overall performance and energy efficiency of scientific and engineering applications. Nevertheless, taking full advantage of their resources comes along with a variety of difficulties: developers require technical expertise in using different parallel programming frameworks and previous knowledge about the algorithms used underneath by the application. To alleviate this burden, we present an adaptive offline implementation selector that allows users to better exploit resources provided by heterogeneous platforms. Specifically, this framework selects, at compile time, the tuple device-implementation that delivers the best performance on a given platform. The user interface of the framework leverages two C++ language features: attributes and concepts. To evaluate the benefits of this framework, we analyse the global performance and convergence of the selector using two different use cases. The experimental results demonstrate that the proposed framework allows users enhancing performance while minimizing efforts to tune applications targeted to heterogeneous platforms. Furthermore, we also demonstrate that our framework delivers comparable performance figures with respect to other approaches.