Porting industrial codes and developing sparse linear solvers on parallel computers
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Porting
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A parallel algorithm for four-index transformation and MP2 energy evaluation, for distributed memory parallel (MIMD) machines is presented. The underlying serial algorithm for the present parallel effort is the four-index transform. The scheme works through parallelization over AO integrals and, therefore, spreads the O(n3) memory requirement across the processors, reducing it to O(n2). In this sense, the scheme superimposes a shared memory architecture onto the distributed memory setup. A detailed analysis of the algorithm is presented for networks with 4, 6, 8, 10, and 12 processors employing a smaller test case of 86 contractions. Model direct MP2 calculations for systems of sizes ranging from 160 to 238 basis functions are reported for 11- and 22-processor networks. A gain of at least 40% and above is observed for the larger systems. © 1997 by John Wiley & Sons, Inc.
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We report two aspects of a computational molecular dynamics study of large-scale problems on a distributed-memory MIMD parallel computer: (1) efficiency and scalability results on Intel Paragon parallel computers with up to 512 nodes and (2) a new method for dynamic load balancing.
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A Distributed shared memory systems represent a successful hybrid of two parallel computer classes: distributed computer systems and shared memory multiprocessors. They provide the shared memory abstraction in systems with physically distributed memories, and consequently combine the advantages of both approaches. Distributed shared memory is the abstraction that supports the shared memory in a physically non-shared (distributed) architecture. Shared memory is a simple yet powerful paradigm for structuring systems. Recently, there has been an interest in extending this paradigm to non-shared memory architectures as well. For example, the virtual address spaces for all variables in a distributed variable-based system could be viewed as constituting a global distributed shared memory. The Distributed shared memory system is designed on the basis of page-based, shared-variable-based or object-based access. There are certain advantages and disadvantages of each access methodology. We propose a shared variable based DSM model for managing distributed shared memory. These paper also present implementation levels with its several issues require to be consider. We have presented an implementation of this model in the context of a linux operating system.
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Abstract The paper discusses data management techniques for mapping a large data space onto the memory hierarchy of a distributed memory MIMD system. Experimental results for structural biology computations using the Molecular Replacement Method are presented.
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Porting
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Porting
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Distributed memory
Code (set theory)
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A comparative analysis of data management schemes for Distributed Memory MIMD systems for applications which need a very large shared data space, is presented. Work allocation, data distribution, fetching and allocation policies, as well as optimization techniques for iterative computations, are discussed. Measurements for a case study, the electron density averaging for the determination of the atomic structure of viruses are analyzed. We argue that future MPPs should provide primitives to support user controlled data management, rather than solutions, e.g., virtual memory or shared memory.
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