Streaming Applications on Heterogeneous Platforms

TL;DR

This paper investigates the impact of streaming on heterogeneous platforms using 56 benchmarks, identifying code types and demonstrating up to 90% performance improvements with multiple streams.

Contribution

It provides a systematic analysis of streaming necessity, classifies code types, and offers a generic approach for applying multiple streams on heterogeneous systems.

Findings

Up to 90% performance improvement with multiple streams.

Identified two non-streamable and three streamable code types.

Provided a statistical view of data transfer overhead across benchmarks.

Abstract

Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Currently, very few cases have been streamed to demonstrate the streaming performance impact and a systematic investigation of streaming necessity and how-to over a large number of test cases remains a gap. In this paper, we use a total of 56 benchmarks to build a statistical view of the data transfer overhead, and give an in-depth analysis of the impacting factors. Among the heterogeneous codes, we identify two types of non-streamable codes and three types of streamable codes, for which a streaming approach has been proposed. Our experimental results on the CPU-MIC platform show that, with multiple streams, we can improve the application performance by up 90%. Our work can serve as a generic flow of using multiple streams on heterogeneous platforms.