Best Practices for Simulated Annealing in Multiprocessor Task Distribution Problems

Orsila, Heikki; Salminen, Erno; Hämäläinen, Timo D.

doi:10.5772/5559

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…It is necessary to define the solution space; define the neighborhood of each solution, in other words, introduce the elementary operations on the solution space; define the target function of the algorithm. (Orsila, 2008) gives experimental proofs of the efficiency of simulated annealing for job shop scheduling. This work also suggests an improvement over the standard algorithm: heuristics.…”

Section: Selecting the Methodsmentioning

confidence: 99%

Job Shop Scheduling and Co-Design of Real-Time Systems with Simulated Annealing

Zorin

Костенко

2014

Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems

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This paper describes a method of job shop scheduling and co-designing a multiprocessor system with the minimal number of processors. The program is represented with a direct acyclic graph, and there is a fixed real-time deadline as well as a restriction on the reliability of the system. The system is supposed to tolerate both hardware and software faults. A simulated annealing algorithm is proposed for the problem, and it is evaluated both experimentally and theoretically in terms of asymptotic convergence. The algorithm is also applied to a practical problem of scheduling in radiolocation systems.

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Section: Selecting the Methodsmentioning

confidence: 99%

Job Shop Scheduling and Co-Design of Real-Time Systems with Simulated Annealing

Zorin

Костенко

2014

Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems

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“…A proper range for k is in [1,3]. Mathematical properties of k is discussed in more detail in [8]. The rationale is choosing an initial temperature where the biggest single task will have a fair transition probability of being moved from one PE to another.…”

Section: Determining Temperature Upper and Lower Boundsmentioning

confidence: 99%

“…The chosen final temperature is T tmin f= ktmaxsum ' (5) where tmin is the minimum execution time for any task on any PE and tmaxsum the sum of execution times for all tasks on the slowest PE in the system. Derivation of Equations (4)(5) is explained in [8] (Orsila case). To and Tf are inside range (0,1].…”

Section: Determining Temperature Upper and Lower Boundsmentioning

confidence: 99%

“…Our earlier papers have surveyed SA for task distribu tion [8], devised a method for automatically determining SA parameters [9] for distributing STGs, optimized memory consumption in distributing STGs [10], compared SA to other algorithms with respect to its convergence proprties [II], and optimized task graphs for heterogeneous architectures [12].…”

Section: B Algorithms For Task Mappingmentioning

confidence: 99%

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Parameterizing simulated annealing for distributing Kahn Process Networks on multiprocessor SoCs

Orsila

Salminen

Hämäläinen

2009

2009 International Symposium on System-on-Chip

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Mapping an application on Multiprocessor System on-Chip (MPSoC) is a crucial step in architecture exploration. The problem is to minimize optimization effort and application execution time. Simulated annealing (SA) is a versatile algorithm for hard optimization problems, such as task distribution on MPSoCs. We propose an improved automatic parameter selection method for SA to save optimization effort. The method deter mines a proper annealing schedule and transition probabilities for SA, which makes the algorithm scalable with respect to application and platform size. Applications are modeled as Kahn Process Networks (KPNs). The method was improved to optimize KPNs and save optimization effort by doing sensitivity analysis for processes. The method is validated by mapping 16 to 256 node KPNs onto an MPSoC. We optimized 150 KPNs for 3 architectures. The method saves over half the optimization time and loses only 0.3% in performance to non-automated SA. Results are compared to non-automated SA, Group migration, random mapping and brute force algorithms. Global optimum solution are obtained by brute force and compared to our heuristics.Global optimum convergence for KPNs has not been reported before. We show that 35% of optimization runs reach within 5% of the global optimum. In one of the selected problems global optimum is reached in as many as 37% of optimization runs. Results show large variations between KPNs generated with different parameters. Cyclic graphs are found to be harder to parallelize than acyclic graphs.

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“…A simulated annealing approach for the scheduling of sequential tasks with dependencies onto heterogeneous multiprocessor systems is proposed in Reference 25. In Reference 26, the best practices for defining the temperature, the acceptance functions, and move heuristics are presented. Although the given information is helpful for scheduling with simulated annealing in general, the scheduling approaches themselves are not suitable for parallel tasks.…”

Section: Related Workmentioning

confidence: 99%

The search‐based scheduling algorithm HP* for parallel tasks on heterogeneous platforms

Dietze

Rünger

2020

Concurrency and Computation

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Scheduling is a widely used method in parallel computing, which assigns tasks to compute resources of a parallel environments. In this article, we consider independent parallel tasks to be scheduled onto a heterogeneous execution platform consisting of a set of multicores of different architecture. Each parallel task has an internal potential parallelism which allows a parallel execution on any multicore processors. However, the execution time may differ due a different computation speed of different multicores. In this article, we propose a new search-based scheduling algorithm HETEROGENEOUS PARALLEL TASK SCHEDULING BASED ON A* (called HP*) to solve the problem of scheduling independent parallel tasks onto heterogeneous multicore platforms. Specifically, we propose a heuristic cost function needed for an informed search. Also, three pruning techniques are proposed, which are shown to significantly reduce the search space of HP*. Performance measurements on a heterogeneous platform are performed and the results of HP* are compared to scheduling results of other popular scheduling methods. The performance results with benchmark tasks from the SPLASH-3 benchmark suite demonstrate the good scheduling results and the improvements achieved by HP*. K E Y W O R D S heterogeneous platforms, parallel tasks, pruning techniques, search-based scheduling This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Best Practices for Simulated Annealing in Multiprocessor Task Distribution Problems

Cited by 12 publications

References 11 publications

Job Shop Scheduling and Co-Design of Real-Time Systems with Simulated Annealing

Job Shop Scheduling and Co-Design of Real-Time Systems with Simulated Annealing

Parameterizing simulated annealing for distributing Kahn Process Networks on multiprocessor SoCs

The search‐based scheduling algorithm HP* for parallel tasks on heterogeneous platforms

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