Paper Info
Title
Processing HDF5 Datasets on Multi-core Architectures
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.
Year
DOI
Venue
2013
10.1109/AINA.2013.153
AINA
Keywords
Field
DocType
processing hdf5 datasets,scientific data characteristic,scientific datasets,scientific application,multi-core architectures,multi-core node,various processing,multi-core architecture,effective processing,scientific middleware,processing thread,multi-core processor,hierarchical data format,multi core,grid computing,multi threading,hdf5,middleware,cloud computing,multithreaded programming
Middleware,Multithreading,Grid computing,Computer science,Scheduling (computing),Thread (computing),Multi-core processor,Distributed computing,Scalability,Cloud computing
Conference
ISSN
Citations 
PageRank 
1550-445X
0
0.34
References 
Authors
15
3
Name
Order
Citations
PageRank
Rajdeep Bhowmik1214.21
Jessica Hartog2464.31
Madhusudhan Govindaraju385496.53