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33
Centralized Versus Distributed Schedulers for Multiple BagofTasks Applications
 IEEE Trans. Parallel Distributed Systems
, 2008
"... applications ..."
A Realistic Model and an Efficient Heuristic for Scheduling with Heterogeneous Processors
, 2001
"... Scheduling computational tasks on processors is a key issue for highperformance computing. Although a large number of scheduling heuristics have been presented in the literature, most of them target only homogeneous resources. Moreover, these heuristics often rely on a model where the number of proc ..."
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Cited by 25 (13 self)
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Scheduling computational tasks on processors is a key issue for highperformance computing. Although a large number of scheduling heuristics have been presented in the literature, most of them target only homogeneous resources. Moreover, these heuristics often rely on a model where the number of processors is bounded but where the communication capabilities of the target architecture are not restricted. In this paper, we deal with a more realistic model for heterogeneous networks of workstations, where each processor can send and/or receive at most one message at any given timestep. First, we state a complexity result that shows that the model is at least as difficult as the standard one. Then, we show how to modify classical list scheduling techniques to cope with the new model. Next we introduce a new scheduling heuristic which incorporates loadbalancing criteria into the decision process of scheduling and mapping ready tasks. Experimental results conducted using six classical testbeds: (LAPLACE, LU, STENCIL, FORKJOIN, DOOLITTLE, and LDMt) show very promising results.
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 16 (11 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.
Heuristics for Work Distribution of a Homogeneous Parallel Dynamic Programming Scheme on Heterogeneous Systems”, Proceedings of the 3 rd International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks (HeteroPar’04
 MS in mathematics and engineering from the Moscow Aviation Institute in 1980 and his PhD in engineering from HeavyMachinery Research Institute in
"... Abstract — In this paper the possibility of including automatic optimization techniques in the design of parallel dynamic programming algorithms in heterogeneous systems is analyzed. The main idea is to automatically approach the optimum values of a number of algorithmic parameters (number of proces ..."
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Cited by 9 (3 self)
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Abstract — In this paper the possibility of including automatic optimization techniques in the design of parallel dynamic programming algorithms in heterogeneous systems is analyzed. The main idea is to automatically approach the optimum values of a number of algorithmic parameters (number of processes, number of processors, processes per processor), and thus obtain low execution times. Hence, users could be provided with routines which execute efficiently, and independently of the experience of the user in heterogeneous computing and dynamic programming, and which can adapt automatically to a new network of processors or a new network configuration. I.
LoadBalancing Iterative Computations on Heterogeneous Clusters
"... We focus on mapping iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors, which are arranged along a virtual ring. At each iteration, independent calculations are carried out in parallel, and some communications take place between consecutive p ..."
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Cited by 9 (2 self)
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We focus on mapping iterative algorithms onto heterogeneous clusters. The application data is partitioned over the processors, which are arranged along a virtual ring. At each iteration, independent calculations are carried out in parallel, and some communications take place between consecutive processors in the ring. The question is to determine how to slice the application data into chunks, and assign these chunks to the processors, so that the total execution time is minimized. A major
Algorithmic Issues for (Distributed) Heterogeneous Computing Platforms
 In Rajkumar Buyya and Toni Cortes, editors, Cluster Computing Technologies, Environments, and Applications (CCTEA'99). CSREA
, 1999
"... Future computing platforms will be distributed and heterogeneous. Such platforms range from heterogeneous networks of workstations (NOWs) to collections of NOWs and parallel servers scattered throughout the world and linked through highspeed networks. Implementing tightlycoupled algorithms on such ..."
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Cited by 8 (6 self)
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Future computing platforms will be distributed and heterogeneous. Such platforms range from heterogeneous networks of workstations (NOWs) to collections of NOWs and parallel servers scattered throughout the world and linked through highspeed networks. Implementing tightlycoupled algorithms on such platforms raises several challenging issues. New data distribution and load balancing strategies are required to squeeze the most out of heterogeneous platforms. In this paper, we rst summarize previous results obtained for heterogeneous NOWs, dealing with the implementation of standard numerical kernels such as nitedierence stencils or dense linear solvers. Next we target distributed collections of heterogeneous NOWs, and we discuss data allocation strategies for dense linear solvers on top of such platforms. These results indicate that a major algorithmic and software eort is needed to come up with eÆcient numerical libraries on the computational grid. Keywords: metacomputing, heter...
Centralized versus Distributed Schedulers for BagofTasks Applications
 IEEE TRANS. PARALLEL DISTRIB. SYST
, 2008
"... Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where com ..."
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Cited by 8 (2 self)
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Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where communication is restricted to a tree embedded in the network, and the applications consist of a large number of independent tasks (Bags of Tasks) that originate at the tree’s root. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary for different applications. The goal of scheduling is to maximize the throughput of each application while ensuring a fair sharing of resources between applications. We can find the optimal asymptotic rates by solving a linear programming problem that expresses all necessary problem constraints, and we show how to construct a periodic schedule from any linear program solution. For singlelevel trees, the solution is characterized by processing tasks with larger communicationtocomputation ratios at children with larger bandwidths. For multilevel trees, this approach requires global knowledge of all application and platform parameters. For largescale platforms, such global coordination by a centralized scheduler may be unrealistic. Thus, we also investigate decentralized schedulers that use only local information at each participating resource. We assess their performance via simulation and compare to an optimal centralized solution obtained via linear programming. The best of our decentralized heuristics achieves the same performance on about 2/3 of our test cases but is far worse in a few cases. Although our results are based on simple assumptions and do not explore all parameters (such as the maximum number of tasks that can be held on a node), they provide insight into the important question of fairly and optimally scheduling
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.
MatrixProduct on Heterogeneous MasterWorker Platforms
"... This paper is focused on designing efficient parallel matrixproduct algorithms for heterogeneous masterworker platforms. While matrixproduct is wellunderstood for homogeneous 2Darrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses th ..."
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Cited by 7 (6 self)
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This paper is focused on designing efficient parallel matrixproduct algorithms for heterogeneous masterworker platforms. While matrixproduct is wellunderstood for homogeneous 2Darrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: Centralized data. We assume that all matrix files originate from, and must be returned to, the master. The master distributes data and computations to the workers (while in ScaLAPACK, input and output matrices are supposed to be equally distributed among participating resources beforehand). Typically, our approach is useful in the context of speeding up MATLAB or SCILAB clients running on a server (which acts as the master and initial repository of files). Heterogeneous starshaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. Also, the workers are connected to the master by links of different capacities. This framework is realistic when deploying the application from the server, which is responsible for enrolling authorized resources. Limited memory. As we investigate the parallelization of large problems, we cannot assume that full matrix column blocks can be stored in the worker memories and be reused for subsequent updates (as in ScaLAPACK). We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on a platform at our site. The experiments show that our matrixproduct algorithm has smaller execution times than existing ones, while it also uses fewer resources.
Algorithms and Tools for (Distributed) Heterogeneous Computing: A Prospective Report
, 1999
"... We discuss algorithms and tools to help program and use metacomputing resources in the forthcoming years. Metacomputing with highly distributed heterogeneous environments stands to become a major, if not dominant, method to implement all kinds of parallel applications. In this report, we survey some ..."
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Cited by 6 (1 self)
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We discuss algorithms and tools to help program and use metacomputing resources in the forthcoming years. Metacomputing with highly distributed heterogeneous environments stands to become a major, if not dominant, method to implement all kinds of parallel applications. In this report, we survey some general aspects of metacomputing (hardware, system and administration issues, as well as the application eld). Next we identify some algorithmic issues and software challenges that must be solved to eÆciently program and/or transparently use such platforms: Data decomposition techniques for cluster computing, Granularity issues for metacomputing, Scheduling and loadbalancing methods, Programming models. We illustrate each of these issues and challenges by the analysis of several case studies: Cluster ScaLAPACK, AppLeS, Globus, Legion, Albatross and Netsolve. We conclude this report by stating some nal remarks and recommendations. mbox Acknowledgments: This research report is...