TY - JOUR
T1 - Parallel Loop Scheduling for High Performance Computers
AU - Yue, Kelvin K.
AU - Lilja, David J.
PY - 1995/1/1
Y1 - 1995/1/1
N2 - Executing loop iterations in parallel on a multiprocessor system is one of the many ways to improve the execution of a program. However, due to the scheduling overhead and the potential for load imbalance among processors, maximum performance might not be attained. This article reviews current loop scheduling algorithms and studies their scheduling overhead versus load balancing tradeoffs. Using analytical models, simulations, and experimental measurements, the performance and the scalability of chunk scheduling, self-scheduling, guided self-scheduling, factoring, and trapezoid self-scheduling are compared.
AB - Executing loop iterations in parallel on a multiprocessor system is one of the many ways to improve the execution of a program. However, due to the scheduling overhead and the potential for load imbalance among processors, maximum performance might not be attained. This article reviews current loop scheduling algorithms and studies their scheduling overhead versus load balancing tradeoffs. Using analytical models, simulations, and experimental measurements, the performance and the scalability of chunk scheduling, self-scheduling, guided self-scheduling, factoring, and trapezoid self-scheduling are compared.
KW - Analytical modeling
KW - Parallel loop scheduling
KW - Performance analysis
KW - Scalability
KW - Shared memory multiprocessor
UR - http://www.scopus.com/inward/record.url?scp=84947791056&partnerID=8YFLogxK
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U2 - 10.1016/S0927-5452(06)80016-X
DO - 10.1016/S0927-5452(06)80016-X
M3 - Article
AN - SCOPUS:84947791056
SN - 0927-5452
VL - 10
SP - 243
EP - 264
JO - Advances in Parallel Computing
JF - Advances in Parallel Computing
IS - C
ER -