On-line use of off-line derived mappings for iterative automatic target recognition tasks and a particular class of hardware platforms

Budenske, John; Ramanujan, R.V.; Siegel, Howard Jay

doi:10.1109/hcw.1997.581413

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…This is appropriate for off-line matching and scheduling, rather than for real-time use (although in some applications, off-line precomputed GA mapping can be used on-line in real time [BuR97]). …”

Section: Resultsmentioning

confidence: 99%

Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach

Wang

Siegel

Roychowdhury

et al. 1997

Journal of Parallel and Distributed Computing

329

230

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To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random Search.

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Section: Resultsmentioning

confidence: 99%

Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach

Wang

Siegel

Roychowdhury

et al. 1997

Journal of Parallel and Distributed Computing

329

230

View full text Add to dashboard Cite

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“…It is often difficult to make head-tohead comparisons between distinct efforts as schedulers are developed for particular system environments, language representations, and application domains. In this section, we review a number of successful projects [84], [85], [86], [87], [88], [89] and highlight differences with the AppLeS approach in terms of computing environment, program model, performance model, and scheduling strategy.…”

Section: Related Workmentioning

confidence: 99%

“…VDCE [85] and SEA [86] target applications structured as dependency graphs with coarse-grained tasks (calls to functions from mathematical libraries in VDCE, data-flow-style programming in SEA). IOS [87] targets real-time, fine-grained, iterative, image recognition applications that are also structured as dependency graphs. Dome [89] and SPP(X) [88] provide a language abstraction for the application program, which is compiled into a low-level task dependency graph representation automatically.…”

Section: Related Workmentioning

confidence: 99%

Adaptive computing on the grid using AppLeS

Berman

Wolski

Casanova

et al. 2003

IEEE Trans. Parallel Distrib. Syst.

357

199

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Ensembles of distributed, heterogeneous resources, also known as Computational Grids, have emerged as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in heterogeneous, multiuser grid environments. In this article, we discuss the AppLeS project and outline our findings.

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“…To overcome the explosion of the search space for realistic problems, one can apply approximation algorithms (heuristic-guided search), or genetic algorithms [4]. (b) The non-deterministic nature of the program execution time renders even an optimal solution approximate, or infeasible, depending upon the resource allocation model.…”

Section: Introductionmentioning

confidence: 99%

“…Once the data ow allows a program to be scheduled, gather information about the state of the grid and compute a new schedule for the remaining components of the metaprogram. (b3) Use static scheduling algorithms to compute schedules for various scenarios, and at run time adopt the schedule which best ÿts the current conditions [4]. If the grid is shared by multiple metaprograms this may not be feasible.…”

Section: Introductionmentioning

confidence: 99%

Robust scheduling of metaprograms

Bölöni

Marinescu

2002

J. Sched.

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SUMMARYScheduling coarse-grain tasks, e.g. metaprograms on a grid, uses estimation of the execution times of individual components to compute optimal schedules. Various factors (hazards) lead to estimation errors, which a ect both the performance of the schedule and its resource utilization. We introduce the concept of robustness of a schedule and present an analysis technique to determine the chance that a metaprogram exceeds its execution time due to components outside its critical path. The results of this analysis are used to compute schedules less sensitive to hazards. This translates into more accurate reservation requirements for critical systems, and reduced expected execution time for noncritical metaprograms executed repeatedly. We introduce the concept of the entropy of a schedule and conjecture that a more robust schedule is one that minimizes the entropy of a schedule.

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On-line use of off-line derived mappings for iterative automatic target recognition tasks and a particular class of hardware platforms

Cited by 8 publications

References 14 publications

Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach

Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach

Adaptive computing on the grid using AppLeS

Robust scheduling of metaprograms

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