Machine Characterization Based on an Abstract High-Level Language Machine
IEEE Transactions on Computers
A methodology for performance evaluation of parallel applications on multiprocessors
Journal of Parallel and Distributed Computing
A new approach to I/O performance evaluation: self-scaling I/O benchmarks, predicted I/O performance
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The process-flow model: examining I/O performance from the system's point of view
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Server-directed collective I/O in Panda
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Scalable message passing in Panda
Proceedings of the fourth workshop on I/O in parallel and distributed systems: part of the federated computing research conference
Microbenchmarking and Performance Prediction for Parallel
Microbenchmarking and Performance Prediction for Parallel
Exploiting local data in parallel array I/O on a practical network of workstations
Proceedings of the fifth workshop on I/O in parallel and distributed systems
Automatic parallel I/O performance optimization in Panda
Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures
Efficient input and output for scientific simulations
Proceedings of the sixth workshop on I/O in parallel and distributed systems
IEEE Transactions on Software Engineering - Special issue on architecture-independent languages and software tools parallel processing
Optimizing fastquery performance on lustre file system
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Taming parallel I/O complexity with auto-tuning
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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We present an analytical performance model for Panda, a library for synchronized i/o of large multidimensional arrays on parallel and sequential platforms, and show how the Panda developers use this model to evaluate Panda's parallel i/o performance and guide future Panda development. The model validation shows that system developers can simplify performance analysis, identify potential performance bottlenecks, and study the design trade-offs for Panda on massively parallel platforms more easily than by conducting empirical experiments. More importantly, we show that the outputs of the performance model can be used to help make optimal plans for handling application i/o requests, the first step toward our long-term goal of automatically optimizing i/o request handling in Panda.