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53
BandwidthCentric Allocation of Independent Tasks on Heterogeneous Platforms
 In International Parallel and Distributed Processing Symposium (IPDPS’2002). IEEE Computer
, 2001
"... In this paper, we consider the problem of allocating a large number of independent, equalsized tasks to a heterogenerous "grid" computing platform. Such problems arise in collaborative computing eorts like SETI@home. We use a tree to model a grid, where resources can have dierent speeds ..."
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Cited by 84 (28 self)
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In this paper, we consider the problem of allocating a large number of independent, equalsized tasks to a heterogenerous "grid" computing platform. Such problems arise in collaborative computing eorts like SETI@home. We use a tree to model a grid, where resources can have dierent speeds of computation and communication, as well as dierent overlap capabilities. We dene a base model, and show how to determine the maximum steadystate throughput of a node in the base model, assuming we already know the throughput of the subtrees rooted at the node's children. Thus, a bottomup traversal of the tree determines the rate at which tasks can be processed in the full tree. The best allocation is bandwidthcentric: if enough bandwidth is available, then all nodes are kept busy; if bandwidth is limited, then tasks should be allocated only to the children which have suciently small communication times, regardless of their computation power. We then show how nodes with other capabilities ones that allow more or less overlapping of computation and communication than the base model can be transformed to equivalent nodes in the base model. We also show how to handle a more general communication model. Finally, we present simulation results of several demanddriven task allocation policies that show that our bandwidthcentric method obtains better results than allocating tasks to all processors on a rstcome, rst serve basis. Key words: heterogeneous computer, allocation, scheduling, grid, metacomputing. Corresponding author: Jeanne Ferrante The work of Larry Carter and Jeanne Ferrante was performed while visiting LIP. 1 1
Adaptive parallel computing on heterogeneous networks with mpC
 Parallel Computing
, 2002
"... The paper presents a new advanced version of the mpC parallel language. The language was designed specially for programming highperformance parallel computations on heterogeneous networks of computers. The advanced version allows the programmer to define at runtime all the main features of the unde ..."
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Cited by 47 (25 self)
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The paper presents a new advanced version of the mpC parallel language. The language was designed specially for programming highperformance parallel computations on heterogeneous networks of computers. The advanced version allows the programmer to define at runtime all the main features of the underlying parallel algorithm, which have an impact on the application execution performance. The mpC programming system uses this information along with the information about the performance of the executing network to map the processes of the parallel program to this network so as to achieve better execution time.
The masterslave paradigm with heterogeneous processors
, 2001
"... In this paper, we revisit the masterslave tasking paradigm in the context of heterogeneous processors. We assume that communications take place in exclusive mode. We present a polynomial algorithm that gives the optimal solution when a single communication is needed before the execution of the task ..."
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Cited by 39 (12 self)
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In this paper, we revisit the masterslave tasking paradigm in the context of heterogeneous processors. We assume that communications take place in exclusive mode. We present a polynomial algorithm that gives the optimal solution when a single communication is needed before the execution of the tasks on the slave processors. When communications are required both before and after the task processing, we show that the problem is at least as difficult as a problem whose complexity is open. In this case, we present a guaranteed approximation algorithm. Finally, we present asymptotically optimal algorithms when communications are required before the processing of each task, or both before and after the processing of each task.
Data partitioning with a realistic performance model of networks of heterogeneous computers
 In International Parallel and Distributed Processing Symposium IPDPS’2004. IEEE Computer
, 2004
"... The paper presents a performance model that can be used to optimally schedule arbitrary tasks on a network of heterogeneous computers when there is an upper bound on the size of the task that can be solved by each computer. We formulate a problem of partitioning of an nelement set over p heterogene ..."
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Cited by 25 (15 self)
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The paper presents a performance model that can be used to optimally schedule arbitrary tasks on a network of heterogeneous computers when there is an upper bound on the size of the task that can be solved by each computer. We formulate a problem of partitioning of an nelement set over p heterogeneous processors using this advanced performance model and give its efficient solution of the complexity O(p 3 ×log 2 n).
On performance analysis of heterogeneous parallel algorithms
 Parallel Computing
, 2004
"... AbstractThe paper presents an approach to performance analysis of heterogeneous parallel algorithms. As a typical heterogeneous parallel algorithm is just a modification of some homogeneous one, the idea is to compare the heterogeneous algorithm with its homogeneous prototype, and to assess the h ..."
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Cited by 20 (7 self)
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AbstractThe paper presents an approach to performance analysis of heterogeneous parallel algorithms. As a typical heterogeneous parallel algorithm is just a modification of some homogeneous one, the idea is to compare the heterogeneous algorithm with its homogeneous prototype, and to assess the heterogeneous modification rather than analyse the algorithm as an isolated entity. A criterion of optimality of heterogeneous parallel algorithms is suggested. A parallel algorithm of matrix multiplication on heterogeneous clusters is used to illustrate the proposed approach. 1. Introduction. Heterogeneous networks of computers are a promising distributedmemory parallel architecture. In the most general case, a heterogeneous network includes PCs, workstations, multiprocessor servers, clusters of workstations, and even supercomputers. Unlike traditional homogeneous parallel platforms, the heterogeneous parallel
Partitioning a Square into Rectangles: NPCompleteness and Approximation Algorithms
 Algorithmica
, 2000
"... In this paper, we deal with two geometric problems arising from heterogeneous parallel computing: how to partition the unit square into p rectangles of given area s 1 ; s 2 ; : : : ; s p (such s i = 1), so as to minimize (i) either the sum of the p perimeters of the rectangles (ii) or the large ..."
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Cited by 13 (7 self)
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In this paper, we deal with two geometric problems arising from heterogeneous parallel computing: how to partition the unit square into p rectangles of given area s 1 ; s 2 ; : : : ; s p (such s i = 1), so as to minimize (i) either the sum of the p perimeters of the rectangles (ii) or the largest perimeter of the p rectangles. For both problems, we prove NPcompleteness and we introduce approximation algorithms.
An Overview of Heterogeneous High Performance and Grid Computing
 In Engineering the Grid
, 2006
"... Abstract. This paper is an overview the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing. This work is placed in the context of scientific computing. The simulation of very large systems often requires computational capabilities which cannot be sat ..."
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Cited by 12 (2 self)
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Abstract. This paper is an overview the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing. This work is placed in the context of scientific computing. The simulation of very large systems often requires computational capabilities which cannot be satisfied by a single processing system. A possible way to solve this problem is to couple different computational resources, perhaps distributed geographically. Heterogeneous distributed computing is a means to overcome the limitations of single computing systems.
Wrekavoc: a Tool for Emulating Heterogeneity
"... Computer science and especially heterogeneous distributed computing is an experimental science. Simulation, emulation, or insitu implementation are complementary methodologies to conduct experiments in this context. In this paper we address the problem of defining and controlling the heterogeneity ..."
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Cited by 12 (4 self)
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Computer science and especially heterogeneous distributed computing is an experimental science. Simulation, emulation, or insitu implementation are complementary methodologies to conduct experiments in this context. In this paper we address the problem of defining and controlling the heterogeneity of a platform. We evaluate the proposed solution, called Wrekavoc, with microbenchmark and by implementing algorithms of the literature. 1.
Data Redistribution Algorithms For Heterogeneous Processor Rings
, 2004
"... We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim at op ..."
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Cited by 7 (5 self)
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We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim at optimizing the data redistribution, both for unidirectional and bidirectional rings, and we give complete proofs of correctness. One major contribution of the paper is that we are able to prove the optimality of the proposed algorithms in all cases except that of a bidirectional heterogeneous ring, for which the problem remains open.
STATIC LU DECOMPOSITION ON HETEROGENEOUS PLATFORMS
 INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2001; 15; 310
, 2001
"... In this paper, the authors deal with algorithmic issues on heterogeneous platforms. They concentrate on dense linear algebra kernels, such as matrix multiplication or LU decomposition. Blockcyclic distribution techniques used in ScaLAPACK are no longer sufficient to balance the load among processor ..."
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Cited by 7 (0 self)
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In this paper, the authors deal with algorithmic issues on heterogeneous platforms. They concentrate on dense linear algebra kernels, such as matrix multiplication or LU decomposition. Blockcyclic distribution techniques used in ScaLAPACK are no longer sufficient to balance the load among processors running at different speeds. The main result of this paper is to provide a static data distribution scheme that leads to an asymptotically perfect load balancing for LU decomposition, thereby providing solid foundations toward the design of a clusteroriented version of ScaLAPACK.