Download OpenMP in the Petascale Era: 7th International Workshop on by Ralph Altenfeld, Markus Apel, Dieter an Mey, Bernd Böttger, PDF

By Ralph Altenfeld, Markus Apel, Dieter an Mey, Bernd Böttger, Stefan Benke (auth.), Barbara M. Chapman, William D. Gropp, Kalyan Kumaran, Matthias S. Müller (eds.)

This ebook constitutes the refereed lawsuits of the seventh foreign Workshop on OpenMP, IWOMP 2011, held in Chicago, IL, united states in June 2011. The thirteen revised complete papers provided have been conscientiously reviewed and chosen for inclusion within the complaints. The papers are prepared in topical sections on utilizing OpenMP with program, instruments for OpenMP, extensions of OpenMP, and implementation and performance.

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Changing the work distribution schedule substantially improved application performance and system utilization. This tool is specific to Solaris and Studio compilers, although the performance model is applicable to other OpenMP compilers, Linux and UNIX systems. Keywords: OpenMP, performance analysis, DTrace, high performance computing, parallel computing, parallel speed-up, parallel efficiency, automatic parallelization. 1 Introduction No program is 100% parallel, and no operating system can provide 100% of processor cycles to applications.

Most authors of parallel software retain “manual control” by parallelizing codes themselves with tools such as OpenMP. In the sequential world, programmers are now used to type-safe languages and sophisticated tools and Integrated Development Environments (IDEs). They expect their IDEs to provide code refactoring, navigation, dynamic compilation, automatic builds, “instant” and rapid feedback through structured editors and static analysis, as well as debugging support. This paper shows how the infrastructure for automatic parallelization, specifically the “polyhedral model” for program analysis, can be employed to benefit OpenMP programmers.

S4 : TM [5][ m ] = ... S5 : ... for ( m = 1; m <= 5; m ++) for ( n = 1; n <= 5; n ++) ... = TM [ n ][ m ]; S6 : } Fig. 5. Example taken from [16], originally from BT in NAS parallel benchmark. Array TM must be declared private for the parallelization to be correct. , omp for) are ignored, and the program is assumed to be purely sequential. This gives the analyzer the dependences in the original program, represented in a PRDG. Next, we view OpenMP work-sharing directives as prescribing a program transformation that changes the execution order by assigning new time-stamps to the statement instances in the program.

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