TY - JOUR
T1 - Automatic Performance Tuning of Parallel Mathematical Libraries
AU - Salawdeh, Ihab
AU - Morajko, Anna
AU - César, Eduardo
AU - Margalef, Tomàs
AU - Luque, Emilio
PY - 2010/1/1
Y1 - 2010/1/1
N2 - Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, their use involves a large number of decisions, such as choosing partitioning strategies, pre-conditioners, and solving strategies, which have a great impact on the performance of resulting applications. This work proposes a performance methodology for automatic tuning of applications written with the PETSc library. This methodology consists of strategies for: choosing the appropriate data representation and solving algorithms based on historical performance information and data mining techniques, distributing the workload among application processes, and taking advantage of the library memory pre-allocation capacities.
AB - Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, their use involves a large number of decisions, such as choosing partitioning strategies, pre-conditioners, and solving strategies, which have a great impact on the performance of resulting applications. This work proposes a performance methodology for automatic tuning of applications written with the PETSc library. This methodology consists of strategies for: choosing the appropriate data representation and solving algorithms based on historical performance information and data mining techniques, distributing the workload among application processes, and taking advantage of the library memory pre-allocation capacities.
KW - Mathematical Libraries Performance
KW - Performance Models
UR - http://www.scopus.com/inward/record.url?scp=84894196758&partnerID=8YFLogxK
U2 - 10.3233/978-1-60750-530-3-407
DO - 10.3233/978-1-60750-530-3-407
M3 - Article
SP - 407
EP - 414
JO - Advances in Parallel Computing
JF - Advances in Parallel Computing
SN - 0927-5452
IS - 1
ER -