Abstract
CAPE, which stands for Checkpointing-Aided Parallel Execution,is a checkpoint-based approach to automatically translate and execute OpenMP programs on distributed-memory architectures. This approach demonstrates high-performance and complete compatibility with OpenMP on distributed-memory systems. In CAPE, checkpointing is one of the main factors acted on the performance of the system. This is shown over two versions of CAPE. The first version based on complete checkpoints is too slow as compared to the second version based on Discontinuous Incremental Checkpointing. This paper presents an improvement of Discontinuous Incremental Checkpointing, and a new execution model for CAPE using new techniques of checkpointing. It contributes to improve the performance and make CAPE even more flexible.
Highlights
In order to minimize programmers’ difficulties when developing parallel applications, a parallel programming tool at a higher level should be as easy-to-use as possible
In order to execute an OpenMP program on distributedmemory systems, CAPE uses a set of templates to translate an OpenMP source code into a CAPE source code
Concerning to bypass these limitations, we developed CAPE [10, 15] which stands for Checkpointing-Aided Parallel Execution
Summary
In order to minimize programmers’ difficulties when developing parallel applications, a parallel programming tool at a higher level should be as easy-to-use as possible. MPI is a tool for high-performance computing on distributed-memory environments, while OpenMP has been developed for shared-memory architectures. Most prominent approaches include the use of an SSI [3], SCASH [4], the use of the RC model [5], performing a source-to-source translation to a tool like MPI [6, 7] or Global Array [8], or Cluster OpenMP [9]. Among all these solutions, the use of a Single System Image (SSI) is the most straightforward approach. This one cannot be considered as a fully-compliant implementation of the OpenMP standard either
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