HPF on intel Paragon and CRAFT on CRAY T3D: Basic performance measurements and experiments with a block-sparse CG-algorithm

Abstract

After the proposal for HPF had been finalized, it took only a very short time until the first compilers were visible at the surface; mainly those from APR and PGI. The parallel programming-model of HPF covers the partitioning of data-arrays at compile-time and data- and DO-loop-distribution onto PEs according to the owner sets rule. Non-distributed data-arrays and scalars are replicated. Efficiency can be achieved through a good work- and data-partitioning onto PEs.

CRAFT is the CRay Adaptive ForTran of Cray Research. In addition to the shared variable concept of HPF, CRAFT allows also ’private’ variables. Therefore CRAFT can be mixed with message passing and explicit shared memory functions and can perform shared to private coercion. Also, it is possible to share work on subroutine level, define sequential regions and explicit synchronization-points.

This paper presents experiences and results with the APR and PGI HPF-Compilers on the intel Paragon and CRAFT on the CRAY T3D. Motivated by the wide use of unstructured discretizations in CFD and structural mechanics, we examine the parallelization of a block-sparse Conjugate Gradient (CG-) algorithm. An overview over the adapted BCCS-format and the corresponding data-distribution is given. We describe the difficulties, restrictions and results of using this storage format for efficiently calculating the sparse matrix-vector-product, which is the dominating operation in the CG-algorithm.