Benchmarking Data and Compute Intensive Applications on Modern CPU and GPU Architectures

Abstract

The use of graphics hardware for non-graphics applications has become popular among many scientific programmers and researchers as we have observed a higher rate of theoretical performance increase than the CPUs in recent years. However, performance gains may be easily lost in the context of a specific parallel application due to various both hardware and software factors. Consequently, software benchmarks and performance testing are still the best techniques to compare the effciency of emerging parallel architectures with the built-in support for parallelism at different levels. Unfortunately, many available benchmarks are either relatively simple application kernels, they have been optimized only for a certain parallel architecture or they do not take advantage of recent capabilities provided by modern hardware and low level APIs. Thus, the main aim of this paper is to present a comprehensive real performance analysis of selected applications following the complex standard for data compression and coding -JPEG 2000. It consists of a chain of data and compute intensive tasks that can be treated as good examples of software benchmarks for modern parallel hardware architectures. In this paper we compare achieved performance results of our standard based benchmarks executed on selected architectures for different data sets to identify possible bottlenecks. We discuss also best practices and advices for parallel software development to help users to evaluate in advance and then select appropriate solutions to accelerate the execution of their applications.