By Topic

Performance analysis of PDE based parallel algorithms on different computer architectures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kopan, I. ; Inf. Inst. Comput. Sci. & Eng. Program, ITU, Istanbul, Turkey ; Celebi, M.S.

Performance of an algorithm mainly depends on both computer architecture and software. An Intel Xeon processor based HPC cluster and Intel Itanium2 based symmetric multiprocessing (SMP) architectures are used for performance analysis of PDE based parallel algorithm. Algorithm is parallelized using MPI and performance measurements are done using Tuning and Analysis Utilities (TAU). Computational optimization reveals data independency and helps compiler to generate more efficient program for that specific processor. Removing data dependency inside loop is the key in this work. In iterative algorithms, like Gauss-Seidel method, each processor communicates with the same processors at every iteration. This feature makes persistent connection preferable. MPI has different types of communication methods for different communication characteristics. Persistent connection is one of them. Persistent connection removes connection overhead by leaving connection open for further communications. It can be preferred if data is transferred repeatedly between same nodes. In this work source code changed to help compiler to generate more efficient program for the specific processor. Also MPI persistent connection is used for communication at each iteration in Gauss-Seidel method. In some parallel algorithms, communication must be synchronized. Making communication between processors at the same time becomes a bottleneck if communication medium is shared. This fact has been studies and analyzed.

Published in:

Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on

Date of Conference:

2-4 Sept. 2009