TY - GEN
T1 - Automatic evolution of parallel sorting programs on multi-cores
AU - Chennupati, Gopinath
AU - Muhammad Atif Azad, R.
AU - Ryan, Conor
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Sorting algorithms that offer the potential for data-parallel execution on parallel architectures are an excellent tool for the current generation of multi-core processors that often require skilled parallelization knowledge to fully realize the potential of the hardware. We propose to automate the evolution of natively parallel programs using the Grammatical Evolution (GE) approach to utilise the computational potential of multi-cores. The proposed system, Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS), applies GE mapping along with explicit OpenMP #pragma compiler directives to automatically evolve data-level parallel iterative sorting algorithms. MCGE-PS is assessed on the generation of four non-recursive sorting programs in C. We show that it generated programs that can solve the problem that are also parallel. On a high performance Intel processor, MCGE-PS significantly reduced the execution time of the evolved programs for all the benchmark problems.
AB - Sorting algorithms that offer the potential for data-parallel execution on parallel architectures are an excellent tool for the current generation of multi-core processors that often require skilled parallelization knowledge to fully realize the potential of the hardware. We propose to automate the evolution of natively parallel programs using the Grammatical Evolution (GE) approach to utilise the computational potential of multi-cores. The proposed system, Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS), applies GE mapping along with explicit OpenMP #pragma compiler directives to automatically evolve data-level parallel iterative sorting algorithms. MCGE-PS is assessed on the generation of four non-recursive sorting programs in C. We show that it generated programs that can solve the problem that are also parallel. On a high performance Intel processor, MCGE-PS significantly reduced the execution time of the evolved programs for all the benchmark problems.
KW - Automatic parallelization
KW - Evolutionary parallelization
KW - Grammatical evolution
KW - OpenMP
KW - Program synthesis
KW - Recursion
UR - http://www.scopus.com/inward/record.url?scp=84925874000&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16549-3_57
DO - 10.1007/978-3-319-16549-3_57
M3 - Conference contribution
AN - SCOPUS:84925874000
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 706
EP - 717
BT - Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Proceedings
A2 - Squillero, Giovanni
A2 - Mora, Antonio M.
PB - Springer Verlag
T2 - 18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015
Y2 - 8 April 2015 through 10 April 2015
ER -