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Static Scheduling Algorithms for Allocating Directed Task Graphs to Multiprocessors
, 1999
"... Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported ..."
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Cited by 202 (4 self)
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Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported by the Hong Kong Research Grants Council under contract numbers HKUST 734/96E, HKUST 6076/97E, and HKU 7124/99E. Authors' addresses: Y.K. Kwok, Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong; email: ykwok@eee.hku.hk; I. Ahmad, Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Permission to make digital / hard copy of part or all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and / or a fee. 2000 ACM 03600300/99/12000406 $5.00 ACM Computing Surveys, Vol. 31, No. 4, December 1999 1.
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
 IEEE Transactions on Parallel and Distributed Systems
"... We present a low complexity heuristic named the Dominant Sequence Clustering algorithm (DSC) for scheduling parallel tasks on an unbounded number of completely connected processors. The performance of DSC is comparable or even better on average than many other higher complexity algorithms. We assume ..."
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Cited by 164 (9 self)
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We present a low complexity heuristic named the Dominant Sequence Clustering algorithm (DSC) for scheduling parallel tasks on an unbounded number of completely connected processors. The performance of DSC is comparable or even better on average than many other higher complexity algorithms. We assume no task duplication and nonzero communication overhead between processors. Finding the optimum solution for arbitrary directed acyclic task graphs (DAGs) is NPcomplete. DSC finds optimal schedules for special classes of DAGs such as fork, join, coarse grain trees and some fine grain trees. It guarantees a performance within a factor of two of the optimum for general coarse grain DAGs. We compare DSC with three higher complexity general scheduling algorithms, the MD by Wu and Gajski [19], the ETF by Hwang, Chow, Anger and Lee [12] and Sarkar's clustering algorithm [17]. We also give a sample of important practical applications where DSC has been found useful. Index Terms  Clustering, dire...
Hardwaresoftware codesign of embedded systems
 PROCEEDINGS OF THE IEEE
, 1994
"... This paper surveys the design of embedded computer systems, which use software running on programmable computers to implement system functions. Creating an embedded computer system which meets its performance, cost, and design time goals is a hardwaresoftware codesign problewhe design of the hard ..."
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Cited by 152 (5 self)
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This paper surveys the design of embedded computer systems, which use software running on programmable computers to implement system functions. Creating an embedded computer system which meets its performance, cost, and design time goals is a hardwaresoftware codesign problewhe design of the hardware and software components influence each other. This paper emphasizes a historical approach to show the relationships between wellunderstood design problems and the asyet unsolved problems in codesign. We describe the relationship between hardware and sofhvare architecture in the early stages of embedded system design. We describe analysis techniques for hardware and software relevant to the architectural choices required for hardwaresoftware codesign. We also describe design and synthesis techniques for codesign and related problems.
PerformanceEffective and LowComplexity Task Scheduling for Heterogeneous Computing
 IEEE Transactions on Parallel and Distributed Systems
, 2002
"... AbstractÐEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NPcomplete in general cases as well as in several restricted cases. Because of its key importance, this problem has b ..."
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Cited by 132 (0 self)
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AbstractÐEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NPcomplete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous EarliestFinishTime (HEFT) algorithm and the CriticalPathonaProcessor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertionbased approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics. Index TermsÐDAG scheduling, task graphs, heterogeneous systems, list scheduling, mapping. 1
Dynamic CriticalPath Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors
 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
, 1996
"... In this paper, we propose a static scheduling algorithm for allocating task graphs to fullyconnected multiprocessors. We discuss six recently reported scheduling algorithms and show that they possess one drawback or the other which can lead to poor performance. The proposed algorithm, which is calle ..."
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Cited by 121 (17 self)
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In this paper, we propose a static scheduling algorithm for allocating task graphs to fullyconnected multiprocessors. We discuss six recently reported scheduling algorithms and show that they possess one drawback or the other which can lead to poor performance. The proposed algorithm, which is called the Dynamic CriticalPath (DCP) scheduling algorithm, is different from the previously proposed algorithms in a number of ways. First, it determines the critical path of the task graph and selects the next node to be scheduled in a dynamic fashion. Second, it rearranges the schedule on each processor dynamically in the sense that the positions of the nodes in the partial schedules are not fixed until all nodes have been considered. Third, it selects a suitable processor for a node by looking ahead the potential start times of the remaining nodes on that processor, and schedules relatively less important nodes to the processors already in use. A global as well as a pairwise comparison is c...
Models of Machines and Computation for Mapping in Multicomputers
, 1993
"... It is now more than a quarter of a century since researchers started publishing papers on mapping strategies for distributing computation across the computation resource of multiprocessor systems. There exists a large body of literature on the subject, but there is no commonlyaccepted framework ..."
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Cited by 79 (1 self)
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It is now more than a quarter of a century since researchers started publishing papers on mapping strategies for distributing computation across the computation resource of multiprocessor systems. There exists a large body of literature on the subject, but there is no commonlyaccepted framework whereby results in the field can be compared. Nor is it always easy to assess the relevance of a new result to a particular problem. Furthermore, changes in parallel computing technology have made some of the earlier work of less relevance to current multiprocessor systems. Versions of the mapping problem are classified, and research in the field is considered in terms of its relevance to the problem of programming currently available hardware in the form of a distributed memory multiple instruction stream multiple data stream computer: a multicomputer.
Benchmarking and Comparison of the Task Graph Scheduling Algorithms
, 1999
"... The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NPcompleteness of the problem has stimulated researchers to propose a myriad of ..."
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Cited by 79 (2 self)
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The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NPcompleteness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather purposeless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories a...
A Comparison of Multiprocessor Scheduling Heuristics
 In Proceedings of the 1994 International Conference on Parallel Processing, volume II
, 1994
"... Many algorithms for scheduling DAGs on multiprocessors have been proposed, but there has been little work done to determine their effectiveness. Since multiprocessor scheduling is an NPhard problem, no exact tractible algorithm exists, and no baseline is available from which to compare the resulti ..."
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Cited by 58 (0 self)
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Many algorithms for scheduling DAGs on multiprocessors have been proposed, but there has been little work done to determine their effectiveness. Since multiprocessor scheduling is an NPhard problem, no exact tractible algorithm exists, and no baseline is available from which to compare the resulting schedules. Furthermore, performance guarantees have been found for only a few simple DAGs. This paper is an attempt to quantify the differences in five of the heuristics. Classification criteria are defined for the DAGs, and the differences between the heuristics are noted for various criteria. The comparison is made between a graph based method, two critical path methods, and two list scheduling heuristics. The empirical performance of the five heuristics is compared when they are applied to the randomly generated DAGs. This work is supported by NSF grant number CCR9203319 0 1 Introduction One of the primary problems in executing programs efficiently on multiprocessor systems with ...
On Exploiting Task Duplication in Parallel Program Scheduling
, 1998
"... One of the main obstacles in obtaining high performance from messagepassing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplicationbased scheduling can mitigate this overhead by all ..."
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Cited by 58 (7 self)
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One of the main obstacles in obtaining high performance from messagepassing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplicationbased scheduling can mitigate this overhead by allocating some of the tasks redundantly on more than one processors. In this paper, we focus on the problem of using duplication in static scheduling of task graphs on parallel and distributed systems. We discuss five previously proposed algorithms, and examine their merits and demerits. We describe some of the essential principles for exploiting duplication in a more useful manner, and based on these principles propose an algorithm which outperforms the previous algorithms. The proposed algorithm generates optimal solutions for a number of task graphs. The algorithm assumes an unbounded number of processors. For scheduling on a bounded number of processors, we propose a second algorithm which...
Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors using A Parallel Genetic Algorithm
 JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
, 1997
"... Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NPcomplete even with simplifying assumptio ..."
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Cited by 35 (5 self)
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Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NPcomplete even with simplifying assumptions, and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinato...