Optimized approach based on time prediction and space chunking for polyhedron programs parallelization on multicores
Authors: 
Asma DAB, Yosr SlamaAuthors Info & Claims
Asma DAB, Yosr SlamaAuthors Info & ClaimsPages 22 - 26
Abstract
A complex challenge in parallel computing is cores load balancing aiming to minimize the parallel program overall execution time called makespan. As the performance of some parallel architectures such as multicores may vary during program execution, an effective mapping should support this unknown variation to avoid drawbacks on makespan. In fact, mapping or static load balancing method may not be effective when the target machine state changes during program execution. Thread affinity has appeared as an important technique to improve the program performance and for better stability.
In this context, we propose a predictive approach allowing parallel nested loops adaptation to processor's performance using iterations chunking at runtime. Our approach is based on thread pinning, space chunking and performance detection at runtime. Thus, parting from a parallel program, we define a first set of loop nest iterations called chunk. This first chunk is run using an initial mapping assuming homogeneous cores. Then, performance assessment will correct the mapping by predicting the future core's state. Then, this new mapping will be applied to a new chunk for further evaluation and prediction and so on. The process would stop when the program is fully run or when judging that chunking is no longer effective.
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- Optimized approach based on time prediction and space chunking for polyhedron programs parallelization on multicores
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Published: 15 March 2018
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HP3C 2018
HP3C 2018: 2018 2nd International Conference on High Performance Compilation, Computing and Communications
March 15 - 17, 2018
Hong Kong, Hong Kong
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