Supercompilers for parallel and vector computers
Supercompilers for parallel and vector computers
Parallel Programming with Polaris
Computer
Automatic Generation of OpenMP Directives and Its Application to Computational Fluid Dynamics Codes
ISHPC '00 Proceedings of the Third International Symposium on High Performance Computing
Optimizing supercompilers for supercomputers
Optimizing supercompilers for supercomputers
An evaluation of auto-scoping in OpenMP
WOMPAT'04 Proceedings of the 5th international conference on OpenMP Applications and Tools: shared Memory Parallel Programming with OpenMP
First experiences with intel cluster OpenMP
IWOMP'08 Proceedings of the 4th international conference on OpenMP in a new era of parallelism
Incorporation of OpenMP memory consistency into conventional dataflow analysis
IWOMP'08 Proceedings of the 4th international conference on OpenMP in a new era of parallelism
Proceedings of the 3rd International Workshop on Multicore Software Engineering
Common mistakes in OpenMP and how to avoid them: a collection of best practices
IWOMP'05/IWOMP'06 Proceedings of the 2005 and 2006 international conference on OpenMP shared memory parallel programming
OpenMP implementation of SPICE3 circuit simulator
IWOMP'05/IWOMP'06 Proceedings of the 2005 and 2006 international conference on OpenMP shared memory parallel programming
Leveraging multicore cluster nodes by adding OpenMP to flow solvers parallelized with MPI
HPCS'09 Proceedings of the 23rd international conference on High Performance Computing Systems and Applications
IWOMP'12 Proceedings of the 8th international conference on OpenMP in a Heterogeneous World
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The process of manually specifying scopes of variables when writing an OpenMP program is both tedious and error-prone. To improve productivity, an autoscoping feature was proposed in [1]. This feature leverages the analysis capability of a compiler to determine the appropriate scopes of variables. In this paper, we present the proposed autoscoping rules and describe the autoscoping feature provided in the Sun StudioTM 9 Fortran 95 compiler. To investigate how much work can be saved by using autoscoping and the performance impact of this feature, we study the process of parallelizing PANTA, a 50,000-line 3D Navier-Stokes solver, using OpenMP. With pure manual scoping, a total of 1389 variables have to be explicitly privatized by the programmer. With the help of autoscoping, only 13 variables have to be manually scoped. Both versions of PANTA achieve the same performance.