51 research outputs found

    Expanded delta networks for very large parallel computers

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    In this paper we analyze a generalization of the traditional delta network, introduced by Patel [21], and dubbed Expanded Delta Network (EDN). These networks provide in general multiple paths that can be exploited to reduce contention in the network resulting in increased performance. The crossbar and traditional delta networks are limiting cases of this class of networks. However, the delta network does not provide the multiple paths that the more general expanded delta networks provide, and crossbars are to costly to use for large networks. The EDNs are analyzed with respect to their routing capabilities in the MIMD and SIMD models of computation.The concepts of capacity and clustering are also addressed. In massively parallel SIMD computers, it is the trend to put a larger number processors on a chip, but due to I/O constraints only a subset of the total number of processors may have access to the network. This is introduced as a Restricted Access Expanded Delta Network of which the MasPar MP-1 router network is an example

    Expanded delta networks for very large parallel computers

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    An Analysis of Diffusive Load-Balancing \Lambda

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    U.S.A. Abstract Diffusion is a well-known algorithm for load-balancing in which tasks move from heavily-loaded processors to lightly-loaded neighbors. This paper presents a rigorous analysis of the performance of the diffusion algorithm on arbitrary networks. We derive both lower and upper bounds on the running time of the algorithm. These bounds are stated in terms of the network's bandwidth. For the case of the generalized mesh with wrap-around (which includes common networks like the ring, 2D-torus, 3D-torus and hypercube), we derive tighter bounds and conclude that the diffusion algorithm is inefficient for lower dimensional meshes. \Lambd

    Selection of Optimal Computing Platforms through the Suitability Measure a

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    Selection of spaceborne computing platforms requires balance among several competing factors. Traditional performance analysis techniques are illsuited for this purpose due to their overriding concern with runtime. The suitability measure is a new approach that quantifies the match between a computing platform and a program. It analyzes a program at the opcode and control flow levels, and compares this to a machine's capability to support the unique characteristics of the program. In this paper we develop the suitability measure and a series of program analysis methods. Experimental results confirm that machines that provide a better match to the program yield a higher suitability score. We prove that loops provide the only contribution to the suitability value, and also that the number of loop iterations is irrelevant, leading to the conclusion that a single pass through a loop is sufficient to derive a suitability value
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