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TeraGrid is a national-scale computational science facility supported through a partnership among thirteen institutions, with funding from the US National Science Foundation [1]. Initially created through a Major Research Equipment Facilities Construction (MREFC [2]) award in 2001, the TeraGrid facility began providing production computing, storage,(More)
Rapid increases in computing and communication performance are exacerbating the long-standing problem of performance-limited input/output. Indeed, for many otherwise scalable parallel applications. input/output is emerging as a major performance bottleneck. The design of scalable input/output systems depends critically on the input/output requirements and(More)
Developers of application codes for massively parallel computer systems face daunting performance tuning and optimization problems that must be solved if massively parallel systems are to fullll their promise. Recording and analyzing the dynamics of application program, system software, and hardware interactions is the key to understanding and the(More)
This paper describes the program execution framework being developed by the Grid Application Development Software (GrADS) Project. The goal of this framework is to provide good resource allocation for Grid applications and to support adaptive reallocation if performance degrades because of changes in the availability of Grid resources. At the heart of this(More)
As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already diicult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer aaord to develop ad hoc, one-of-a-kind performance instrumentation software;(More)
The modest I/O configurations and file system limitations of many current high-performance systems preclude solution of problems with large I/O needs. I/O hardware and file system parallelism is the key to achieve high performance. We analyze the I/O behavior of several versions of two scientific applicationson the Intel Paragon XP/S. The versions involve(More)
We present an auto-tuning system for optimizing I/O performance of HDF5 applications and demonstrate its value across platforms, applications, and at scale. The system uses a genetic algorithm to search a large space of tunable parameters and to identify effective settings at all layers of the parallel I/O stack. The parameter settings are applied(More)