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Abstract: This paper addresses the question as to whether there is potential gain to be made from executing such computations in such an environment. The example application considered here is matrix multiplication. As well as being data intensive, this is one of the class of embarrassingly parallel applications which do not require synchronization between concurrent processes during the course of the computation (Update)
Context of citations to this paper: More
...fault tolerance. We have explored the use of Arjuna for this type of applications, and have been encouraged from our initial results [Smith95] 5. An Assessment of Arjuna We now take a critical look at Arjuna, examining in turn its modularity, integration of mechanism and...
...are dense matrix computations, matrix multiplication and Cholesky factorization. A preliminary description of the former was given in [19]. In linear algebra computations it is common to employ block structuring to benefit from increased locality [8] In the implementation...
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The Design and Implementation of Arjuna - Parrington, Shrivastava.. (1995) (Correct)
Lessons Learned from Building and Using the Arjuna Distributed.. - Shrivastava (1995) (Correct)
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BibTeX entry: (Update)
Smith, J. A. and S.K. Shrivastava, "Fault tolerant execution of computationally and storage intensive programs on a network of workstations: a case study", BROADCAST Project deliverable report, July, 1995. http://citeseer.ist.psu.edu/smith95faulttolerant.html More
@article{ smith95faulttolerant,
author = "Jermy C. Smith and Fred J. Taylor",
title = "A Fault-Tolerant {GEQRNS} Processing Element for Linear Systolic Array {DSP} Applications",
journal = "IEEE Transactions on Computers",
volume = "44",
number = "9",
pages = "1121-1130",
year = "1995",
url = "citeseer.ist.psu.edu/smith95faulttolerant.html" }
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48 Implementing recoverable requests using queues (context) - Bernstein, Hsu et al. - 1990
39 Fail-safe pvm: A portable package for distributed programmin.. - Leon, Fisher et al. - 1993
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