TY - JOUR
T1 - Synthetic signature program for performance scalability
AU - Panadero, Javier
AU - Wong, Alvaro
AU - Rexachs, Dolores
AU - Luque, Emilio
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Due to the complexity of message-passing applications, prediction of the scalability is becoming an increasingly complex goal. To make an efficient use of the system, it is important to predict the application scalability in a target system. Based on prediction models, such as PAS2P (Parallel Application Signature for Performance Prediction), we propose to create a Synthetic Signature (SS) program that allows us to predict the application performance using a limited set of resources and in a bounded analysis time. The SS uses the Scalable Logical Traces (SLT) as input, containing the relevant behavior of the communications and compute of the application. We model this information given by the process’s small-scaled PAS2P signatures to generate a Scaled Trace for N number of processes. Basically, the SS will be executed per iterations in order to obtain the performance prediction. The prediction error was 3.59% for all applications tested using 4 nodes of the system.
AB - Due to the complexity of message-passing applications, prediction of the scalability is becoming an increasingly complex goal. To make an efficient use of the system, it is important to predict the application scalability in a target system. Based on prediction models, such as PAS2P (Parallel Application Signature for Performance Prediction), we propose to create a Synthetic Signature (SS) program that allows us to predict the application performance using a limited set of resources and in a bounded analysis time. The SS uses the Scalable Logical Traces (SLT) as input, containing the relevant behavior of the communications and compute of the application. We model this information given by the process’s small-scaled PAS2P signatures to generate a Scaled Trace for N number of processes. Basically, the SS will be executed per iterations in order to obtain the performance prediction. The prediction error was 3.59% for all applications tested using 4 nodes of the system.
KW - HPC systems
KW - MPI application
KW - Scalability prediction
UR - http://www.scopus.com/inward/record.url?scp=84968611387&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-32149-3_33
DO - 10.1007/978-3-319-32149-3_33
M3 - Article
AN - SCOPUS:84968611387
SN - 0302-9743
SP - 345
EP - 355
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -