Analysis of benchmark characteristics and benchmark performance prediction
ACM Transactions on Computer Systems (TOCS)
Performance coupling: case studies for improving the performance of scientific applications
Journal of Parallel and Distributed Computing
Measuring Cache and TLB Performance and Their Effect on Benchmark Runtimes
IEEE Transactions on Computers
Compile-Time Based Performance Prediction
LCPC '99 Proceedings of the 12th International Workshop on Languages and Compilers for Parallel Computing
Using Kernel Couplings to Predict Parallel Application Performance
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
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Standard benchmarking provides the run times for given programs on given machines, but fails to provide insight as to why those results were obtained (either in terms of machine or program characteristics), and fails to provide run times for that program on some other machine, or some other programs on that machine. We have developed a machine-independent model of program execution to characterize both machine performance and program execution. By merging these machine and program characterizations, we can estimate execution time for arbitrary machine/program combinations. Our technique allows us to identify those operations, either on the machine or in the programs, which dominate the benchmark results. This information helps designers in improving the performance of future machines and users in tuning their applications to better utilize the performance of existing machines. Here we apply our methodology to characterize benchmarks and predict their execution times. We present extensive run-time statistics for a large set of benchmarks including the SPEC and Perfect Club suites. We show how these statistics can be used to identify important short- comings in the programs. In addition, we give execution time estimates for a large sample of programs and machines and compare these against benchmark results. Finally, we develop a metric for program similarity that makes it possible to classify benchmarks with respect to a large