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The International Journal of Supercomputing Applications is a peer-reviewed journal published by SAGE, covering Distributed and Parallel Computing Systems, Parallel Computing and Optimization Techniques, Advanced Data Storage Technologies.
| Publisher | SAGE |
|---|---|
| Reference style | Author–year (Harvard) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', The International Journal of Supercomputing Applications, 12(3), pp. 45–58.
Formats any DOI in The International Journal of Supercomputing Applications style. No sign-up. |
| Publishes research in | Distributed and Parallel Computing Systems Parallel Computing and Optimization Techniques Advanced Data Storage Technologies Matrix Theory and Algorithms Scientific Computing and Data Management |
| ISSN | 0890-2720 |
| h-index | 29 |
| i10-index | 66 |
| Total citations | 3,590 |
| Top institutions publishing here | Ames Research Center |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This report presents a methodology for measuring the performance of supercomputers. It includes 13 Fortran programs that total over 50,000 lines of source code. They represent applications in several areas of engi neering and scientific computing, and in many cases the codes are currently being used by computational re search and development groups. We also…
We present four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture. We emphasize Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left- and right-singular vectors) for sparse matrices arising from two practical applications: information retrieval and seismic…
This paper summarizes progress in the use of direct methods for solving very large sparse symmetric positive definite systems of linear equations on vector supercomputers. Sparse di rect solvers based on the multifrontal method or the general sparse method now outperform band or envelope solvers on vector supercomputers such as the CRAY X-MP. This departure…
The method of empirical eigenfunctions (Karhunen-Loève procedure) is developed within a framework suitable for dealing with large scientific datasets. It is shown that this furnishes an intrinsic representation of any given database which is always, in a well-defined mathematical sense, the optimal description. The methodology is illustrated by a variety of examples, arising out of…
Linear algebra algorithms based on the BLAS or ex tended BLAS do not achieve high performance on mul tivector processors with a hierarchical memory system because of a lack of data locality. For such machines, block linear algebra algorithms must be implemented in terms of matrix-matrix primitives (BLAS3). Designing ef ficient linear algebra algorithms for…
