Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Compiler-supported simulation of highly scalable parallel applications
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Demonstrating the scalability of a molecular dynamics application on a Petaflop computer
ICS '01 Proceedings of the 15th international conference on Supercomputing
POEMS: End-to-End Performance Design of Large Parallel Adaptive Computational Systems
IEEE Transactions on Software Engineering
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
Pattern Matching and I/O Replay for POSIX I/O in Parallel Programs
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
An idiom-finding tool for increasing productivity of accelerators
Proceedings of the international conference on Supercomputing
Bridging performance analysis tools and analytic performance modeling for HPC
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Hierarchical model validation of symbolic performance models of scientific kernels
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Compiler-Directed performance model construction for parallel programs
ARCS'10 Proceedings of the 23rd international conference on Architecture of Computing Systems
An exploration of performance attributes for symbolic modeling of emerging processing devices
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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Performance and workload modeling has numerous uses at every stage of the high-end computing lifecycle: design, integration, procurement, installation and tuning. Despite the tremendous usefulness of performance models, their construction remains largely a manual, complex, and time-consuming exercise. We propose a new approach to the model construction, called modeling assertions (MA), which borrows advantages from both the empirical and analytical modeling techniques. This strategy has many advantages over traditional methods: incremental construction of realistic performance models, straightforward model validation against empirical data, and intuitive error bounding on individual model terms. We demonstrate this new technique on the NAS parallel CG and SP benchmarks by constructing high fidelity models for the floating-point operation cost, memory requirements, and MPI message volume. These models are driven by a small number of key input parameters thereby allowing efficient design space exploration of future problem sizes and architectures.