NDLI: Source level transformations to improve I/O data partitioning

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Source level transformations to improve I/O data partitioning

Content Provider	ACM Digital Library
Author	Wang, Yijian Kaeli, David
Abstract	The main goal for parallel I/O is to increase I/O parallelism by providing multiple, independent data channels between processors and disks. To realize this goal, I/O streams need to be parallelized and partitioned at multiple system layers. Contention at any level can dramatically decrease performance and limit scalability. To address this disk contention bottleneck, it is important to carefully study disk access patterns.From our previous work on I/O profiling, we found that I/O access patterns of parallel scientific applications are usually very regular and highly predictable. Thus it is possible to detect I/O access patterns statically during compiler time. Large datasets are logically linearized in file space on disk, and these intensive data accesses follow a linear space traversal. In this paper, we present our recent work on compiler-directed I/O partitioning, based on Linear Disk Access Descriptors (LDAD). We use the SUIF compiler infrastructure to perform data-flow analysis and recognize LDADs. We then use these LDADs to guide our I/O data partitioning that utilizes multiple disks to significantly increase I/O throughput.
Starting Page	27
Ending Page	35
Page Count	9
DOI	10.1145/1162618.1162622
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-09-28
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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