Symbolic Performance Modeling of Parallel Systems
IEEE Transactions on Parallel and Distributed Systems
Compiler Synthesis of Task Graphs for Parallel Program Performance Prediction
LCPC '00 Proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing-Revised Papers
Performance Prediction of Data-Dependent Task Parallel Programs
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Symbolic Cost Estimation of Parallel Applications
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Sourcebook of parallel computing
Parallel program performance prediction using deterministic task graph analysis
ACM Transactions on Computer Systems (TOCS)
EMPS: An Environment for Memory Performance Studies
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 10 - Volume 11
New Software Technologies for the Development and Runtime Support of Complex Applications
International Journal of High Performance Computing Applications
How Well Can Simple Metrics Represent the Performance of HPC Applications?
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
A performance prediction framework for scientific applications
Future Generation Computer Systems
A genetic algorithms approach to modeling the performance of memory-bound computations
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
A performance prediction framework for scientific applications
Future Generation Computer Systems
Performance modeling for dynamic algorithm selection
ICCS'03 Proceedings of the 2003 international conference on Computational science
Modeling execution time of selected computation and communication kernels on grids
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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Despite the performance potential of parallel systems, several factors have hindered their widespread adoption. Of these, performance variability is among the most significant. Data parallel languages, which facilitate the programming of those systems, increase the semantic distance between the program's source code and its observable performance, thus aggravating the optimization problem.In this paper, we present a new methodology to automatically predict the performance scalability of data parallel applications on multicomputers. Our technique represents the execution time of a program as a symbolic expression that includes the number of processors (P), problem size (N), and other system-dependent parameters. This methodology is strongly based on information collected at compile-time. By extending an existing data parallel compiler (Fortran D95), we derive, during compilation, a symbolic cost model that represents the expected cost of each high-level code section and, inductively, of the complete program.