Benchmarking JPEG 2000 implementations on modern CPU and GPU architectures |
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| Institution: | 1. Poznan Supercomputing and Networking Center, Noskowskiego 10 Street, 61-704 Poznan, Poland;2. Intel Corporation, Pipers Way, SN3 1RJ Swindon Wiltshire, United Kingdom;1. Electronics and Micro-Electronics Laboratory, Faculty of Sciences, Monastir, Tunisia;2. University of Burgundy, Laboratory Le2i, UMR CNRS 6063, 21000 Dijon, France;1. Department of Mechanical Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran;2. School of Engineering, Tarbiat Modarres University, P.O. Box 14115-179, Tehran, Iran;1. Monash University, Clayton, Victoria, 3800, Australia;2. University of Queensland, St Lucia, Queensland, 4072, Australia;1. South Asian University, New Delhi, India;2. ABV-Indian Institute of Information Technology and Management, Gwalior, India |
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| 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. JPEG 2000 is a complex standard for data compression and coding, that provides many advanced capabilities demanded by more specialized applications. There are several JPEG 2000 implementations that utilize emerging parallel architectures with the built-in support for parallelism at different levels. Unfortunately, many available implementations are only optimized 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 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 various JPEG 2000 implementations 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. |
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| Keywords: | GPU Multi-core CPU JPEG 2000 Signal processing |
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