Performance estimation in a massively parallel system
Proceedings of the 1990 ACM/IEEE conference on Supercomputing
Predicting parallel applications performance on non-dedicated cluster platforms
ICS '98 Proceedings of the 12th international conference on Supercomputing
MPI-SIM: using parallel simulation to evaluate MPI programs
Proceedings of the 30th conference on Winter simulation
Performance prediction of large parallel applications using parallel simulations
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
On Performance Prediction of Parallel Computations with Precedent Constraints
IEEE Transactions on Parallel and Distributed Systems
Basic Linear Algebra Subprograms for Fortran Usage
ACM Transactions on Mathematical Software (TOMS)
Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
A Model for Moldable Supercomputer Jobs
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Stealing cycles: Can we get along?
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
DPS - Dynamic Parallel Schedules
HIPS '03 Proceedings of the Eighth International Workshop on High-Level Parallel Programming Models and Supportive Environments (HIPS'03)
A Malleable-Job System for Timeshared Parallel Machines
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Parallel program performance prediction using deterministic task graph analysis
ACM Transactions on Computer Systems (TOCS)
Implementing Malleability on MPI Jobs
Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques
Towards an intelligent grid scheduling system
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
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Dynamically allocating computing nodes to parallel applications is a promising technique for improving the utilization of cluster resources. We introduce the concept of dynamic efficiency which expresses the resource utilization efficiency as a function of time. We propose a simulation framework which enables predicting the dynamic efficiency of a parallel application. It relies on the DPS parallelization framework to which we add direct execution simulation capabilities. The high level flow graph description of DPS applications enables the accurate simulation of parallel applications without needing to modify the application code. Thanks to partial direct execution, simulation times and memory requirements may be reduced. In simulations under partial direct execution, the application's parallel behavior is simulated thanks to direct execution, and the duration of individual operations is obtained from a performance prediction model or from prior measurements. We verify the accuracy of our simulator by comparing the effective running time, respectively the dynamic efficiency, of parallel program executions with the running time, respectively the dynamic efficiency, predicted by the simulator. These comparisons are performed for an LU factorization application under different parallelization and dynamic node allocation strategies.