Scheduling algorithms for parallel Gaussian elimination with communication costs

Amoura, Abdel Krim; Bampis, Evripidis; König, Jean-Claude

doi:10.1109/71.707547

Cited by 31 publications

(16 citation statements)

References 13 publications

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“…Therefore, dependencies modeled by Bayesian networks are maintained along the search, and the efficiency of the evolution is improved. 30 …”

Section: Discussionmentioning

confidence: 97%

“…In additional to randomly generated task graphs, task graphs of three well-known real parallel applications, including Gauss elimination [29], Gauss-Jordan elimination [30] and fast Fourier transformation (FFT) [31], are treated as the workload in this section. The statistic significance of the performance difference is also investigated.…”

Section: Performance Evaluation Based On Task Graphs Of Well-known Apmentioning

confidence: 99%

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Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

Yang

Pan³

et al. 2011

Applied Soft Computing

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“…Therefore, dependencies modeled by Bayesian networks are maintained along the search, and the efficiency of the evolution is improved. 30 …”

Section: Discussionmentioning

confidence: 97%

Section: Performance Evaluation Based On Task Graphs Of Well-known Apmentioning

confidence: 99%

Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

Yang

Pan³

et al. 2011

Applied Soft Computing

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“…Amoura et al [46] propose a graph theoretical model to the Gaussian elimination method. When scheduling, the proposed algorithms, namely, GCO (Generalized Column-Oriented Scheduling), MGCO (the Modified Generalized Column-Oriented Scheduling), and BS (Blocking Scheduling) take the triangular architecture parameters into consideration.…”

Section: Related Workmentioning

confidence: 99%

A performance study of multiprocessor task scheduling algorithms

2007

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Multiprocessor task scheduling is an important and computationally difficult problem. A large number of algorithms were proposed which represent various tradeoffs between the quality of the solution and the computational complexity and scalability of the algorithm. Previous comparison studies have frequently operated with simplifying assumptions, such as independent tasks, artificially generated problems or the assumption of zero communication delay. In this paper, we propose a comparison study with realistic assumptions. Our target problems are two well known problems of linear algebra: LU decomposition and Gauss-Jordan elimination. Both algorithms are naturally parallelizable but have heavy data dependencies. The communication delay will be explicitly considered in the comparisons. In our study, we consider nine scheduling algorithms which are frequently used to the best of our knowledge: min-min, chaining, A * , genetic algorithms, simulated annealing, tabu search, HLFET, ISH, and DSH with task duplication. Based on experimental results, we present a detailed analysis of the scalability, advantages and disadvantages of each algorithm.

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“…This model was proved to be asymptotic to a real environment [19]. Then, we can rewrite (9) using t c (n k, j , α p ) and t c (c(e (k, j),(k+1,m) ), L p,q )), as follows:…”

Section: Gaussian Elimination Dagmentioning

confidence: 99%

“…4 presents one example of Gaussian elimination DAG at N = 6. According to the study in [19], the processing time of a task n k, j and the communication time of a data point c(e (k, j),(k+1,m) ) are defined as follows:…”

Section: Gaussian Elimination Dagmentioning

confidence: 99%

On the Performance of a Clustering-based Task Scheduling in a Heterogeneous System

Kanemitsu¹,

Hanada²,

Weyulu³

et al. 2017

Adv. sci. technol. eng. syst. j.

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Recent task scheduling algorithms for a generalized workflow job in heterogeneous system adopt list-based scheduling. In those algorithms, the response time cannot be effectively reduced if the given workflow job is data-intensive. If the workflow job is computationally intensive, an attempt is made to assign tasks to many processors, which can lead to resource starvation. To this end, a task scheduling algorithm that is based on clustering tasks, called CMWSL (Clustering for Minimizing the Worst Schedule Length) has been proposed. In CMWSL, the lower bound of the assignment unit size for each processor is derived in order to suppress the total number of executing processors for effective use of processors. After the lower bound is derived, the processor as a assignment target is determined and then the assignment unit as a task cluster is generated. As a final phase of CMWSL, task ordering is performed for every assigned task. In this paper, we compare several task ordering methods in CMWSL in a real environment to find the best task ordering policy.

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Scheduling algorithms for parallel Gaussian elimination with communication costs

Cited by 31 publications

References 13 publications

Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

A performance study of multiprocessor task scheduling algorithms

On the Performance of a Clustering-based Task Scheduling in a Heterogeneous System

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