Fast model-based test case classification for performance analysis of multimedia MPSoC platforms

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Fast model-based test case classification for performance analysis of multimedia MPSoC platforms

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International Conference on Hardware Software Codesign archive
Proceedings of the 7th IEEE/ACM international conference on Hardware/software codesign and system synthesis table of contents

Grenoble, France

SESSION: Perfomance analysis and optimization for heterogeneous multiprocesses system table of contents

Pages 413-422

Year of Publication: 2009

ISBN:978-1-60558-628-1

Authors

Deepak Gangadharan	National University of Singapore, Singapore, Singapore
Samarjit Chakraborty	Institute for Real-Time Computer Systems, TU Munich, Munich, Germany
Roger Zimmermann	National University of Singapore, Singapore, Singapore

Sponsors

ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
SIGDA: ACM Special Interest Group on Design Automation

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ACM New York, NY, USA

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ABSTRACT

Currently, performance analysis of multimedia-MPSoC platforms largely rely on simulation. The execution of one or more applications on such a platform is simulated for a library of test video clips. If all specified performance constraints are satisfied for this library, then the architecture is assumed to be well-designed. This is similar to testing software for functional correctness. However, in contrast to functional testing, simulating a set of video clips for a complex application/architecture is extremely time consuming. In this paper we propose a technique for clustering a library of video clips, such that it is sufficient to simulate only one clip from each cluster rather than the entire library. Our clustering is scalable, i.e., the number of clusters may be determined based on the number of clips that the system designer wishes to simulate (which is independent of the input library size). For each video clip in the library, we perform a fast bitstream analysis from which the workload generated while processing this clip on the given architecture may be estimated. This workload information, in conjunction with a workload model and a performance model of the architecture, is used for the clustering. This entire process does not involve any simulation and is hence extremely fast. We illustrate its utility through a detailed case study using an MPEG-2 decoder application running on an MPSoC platform. As part of validation of our methodology, it was observed that video clips falling into the same cluster exhibit similar worst case buffer backlogs and worst case delays for one macroblock. Overall the results demonstrate that the proposed method provides a very fast and accurate analysis and hence can be of significant benefit to the system designer.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	http://www.tns.lcs.mit.edu/manuals/mpeg2/.
	2	L. Eeckhout, H. Vandierendonck, and K. D. Bosschere. Workload design: Selecting representative program-input pairs. In Proceedings of International Conference on Parallel Architectures and Compilation Techniques (PACT), 2002.
	3	S. V. Gheorghita, T. Basten, and H. Corporaal. Scenario selection and prediction for dvs-aware scheduling of multimedia applications. Journal of Signal Processing Systems, 50(2):137--161, 2008.
	4	A. D. Gordon. Classification. Chapman & Hall/CRC, 1999.
	5	J. Hamers and L. Eeckhout. Resource prediction for media stream decoding. In Proceedings of the 10th Design, Automation and Test in Europe (DATE), 2007.
	6	K. Hoste and L. Eeckhout. Microarchitecture-independent workload characterization. IEEE Micro, 27(3):63--72, 2007.
	7	L. K. John, P. Vasudevan, and J. Sabarinathan. Workload characterization: Motivation, goals and methodology. In International Workshop on Workload Characterization (WWC), 1999.
	8	I. T. Jolliffe. Principal component analysis. Springer New York, 2002.
	9	A. Joshi, A. Phansalkar, L. Eeckhout, and L. K. John. Measuring benchmark similarity using inherent program characteristics. IEEE Transactions on Computers, 55(6):769--782, 2006.
	10	A. Maxiaguine, Y. Liu, S. Chakraborty, and W. T. Ooi. Identifying representative workloads in designing mpsoc platforms for media processing. In 2nd Workshop on Embedded Systems for Real-Time Multimedia (ESTImedia), 2004.
	11	W. Pan and A. Ortega. Complexity-scalable transform coding using variable complexity algorithms. In Proceedings of the Data Compression Conference (DCC), 2000.
	12	M. J. Rutten, J. T. J. V Eijndhoven, E. G. T. Jaspers, P. V. D. Wolf, E-J. D. Pol, O. P. Gangwal, and A. Timmer. A heterogeneous multiprocessor architecture for flexible media processing. IEEE Design & Test of Computers, 19(4):39--50, 2002.
	13	G. V. Varatkar and R. Marculescu. On-chip traffic modeling and synthesis for mpeg-2 video applications. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 12(1):108--119, 2004.
	14	H. Yicheng, S. Chakraborty, and W. Ye. Using offline bitstream analysis for power-aware video decoding in portable devices. In Proceedings of the 13th ACM International Conference on Multimedia (MM), 2005.
	15	H. Yicheng, V. A. Tran, and W. Ye. A workload prediction model for decoding mpeg video and its application to workload-scalable transcoding. In Proceedings of the 15th ACM International Conference on Multimedia (MM), 2007.