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2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA) (2013)
Barcelona, Spain Spain
Mar. 25, 2013 to Mar. 28, 2013
ISSN: 1550-445X
ISBN: 978-1-4673-5550-6
pp: 666-673
ABSTRACT
In order to make scientific middleware and applications more scalable, there is a need to design them in such a way that they can utilize the evolving multi-core processor architectures available in grid and cloud computing environments. In this paper, we analyze various processing and scheduling techniques on multi-core architectures based on scientific data characteristics and access patterns. More specifically, we conduct fine-grained analysis of scientific datasets such as HDF5 to make effective processing and scheduling decisions in multi-threaded programming. We present performance analysis on how processing threads can be scheduled on multi-core nodes to enhance the performance of scientific applications that process HDF5 data. To accomplish this we introduce a dynamic marking scheme to keep track of the progress of threads on each core. This can be used to help determine work allocation, which results in a decrease in overall application execution time.
INDEX TERMS
Multi-core, HDF5, Scientific Applications
CITATION
Rajdeep Bhowmik, Jessica Hartog, Madhusudhan Govindaraju, "Processing HDF5 Datasets on Multi-core Architectures", 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), vol. 00, no. , pp. 666-673, 2013, doi:10.1109/AINA.2013.153
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