Parallel solution of irregular, sparse matrix problems using High Performance Fortran

Recently, the first commercial High Performance Fortran (HPF) subset compilers have appeared. This article reports on our experiences with the xHPF compiler of Applied Parallel Research, version 1.2, for the Intel Paragon. At this stage, we do not expect very High Performance from our HPF programs, even though performance will eventually be of paramount importance for the acceptance of HPF. Instead, our primary objective is to study how to convert large Fortran 77 (F77) programs to HPF such that the compiler generates reasonably efficient parallel code. We report on a case study that identifies several problems when parallelizing code with HPF; most of these problems affect current HPF compiler technology in general, although some are specific for the xHPF compiler. We discuss our solutions from the perspective of the scientific programmer, and presenttiming results on the Intel Paragon. The case study comprises three programs of different complexity with respect to parallelization. We use the dense matrix-matrix product to show that the distribution of arrays and the order of nested loops significantly influence the performance of the parallel program. We use Gaussian elimination with partial pivoting to study the parallelization strategy of the compiler. There are various ways to structure this algorithm for a particular data distribution. This example shows how much effort may be demanded from the programmer to support the compiler in generating an efficient parallel implementation. Finally, we use a small application to show that the more complicated structure of a larger program may introduce problems for the parallelization, even though all subroutines of the application are easy to parallelize by themselves. The application consists of a finite volume discretization on a structured grid and a nested iterative solver. Our case study shows that it is possible to obtain reasonably efficient parallel programs with xHPF, although the compiler needs substantial support from the programmer.

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Three-Dimensional Electromagnetic Particle-in-Cell Code Using High Performance Fortran on PC Cluster

Lecture Notes in Computer Science - High Performance Computing ◽

10.1007/3-540-47847-7_48 ◽

2002 ◽

pp. 515-525 ◽

Cited By ~ 1

Author(s):

DongSheng Cai ◽

Yaoting Li ◽

Ken-ichi Nishikawa ◽

Chiejie Xiao ◽

Xiaoyan Yan

Keyword(s):

High Performance ◽

Three Dimensional ◽

Particle In Cell ◽

Pc Cluster ◽

High Performance Fortran

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Application-based fault tolerance techniques for sparse matrix solvers

The International Journal of High Performance Computing Applications ◽

10.1177/1094342017694946 ◽

2017 ◽

Vol 32 (5) ◽

pp. 627-640

Author(s):

Simon McIntosh–Smith ◽

Rob Hunt ◽

James Price ◽

Alex Warwick Vesztrocy

Keyword(s):

Fault Tolerance ◽

High Performance Computing ◽

High Performance ◽

Sparse Matrix ◽

Sparse Matrices ◽

Error Correcting Codes ◽

Computing Systems ◽

Hardware Costs ◽

Extreme Scale ◽

Performance Computing

High-performance computing systems continue to increase in size in the quest for ever higher performance. The resulting increased electronic component count, coupled with the decrease in feature sizes of the silicon manufacturing processes used to build these components, may result in future exascale systems being more susceptible to soft errors caused by cosmic radiation than in current high-performance computing systems. Through the use of techniques such as hardware-based error-correcting codes and checkpoint-restart, many of these faults can be mitigated at the cost of increased hardware overhead, run-time, and energy consumption that can be as much as 10–20%. Some predictions expect these overheads to continue to grow over time. For extreme scale systems, these overheads will represent megawatts of power consumption and millions of dollars of additional hardware costs, which could potentially be avoided with more sophisticated fault-tolerance techniques. In this paper we present new software-based fault tolerance techniques that can be applied to one of the most important classes of software in high-performance computing: iterative sparse matrix solvers. Our new techniques enables us to exploit knowledge of the structure of sparse matrices in such a way as to improve the performance, energy efficiency, and fault tolerance of the overall solution.

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