Algorithm Selection: A Quantitative Computation-intensive Optimization Approach

Potkonjak, M.; Rabaey, J.

doi:10.1109/iccad.1994.629749

Cited by 8 publications

(10 citation statements)

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“…It has been demonstrated that throughput optimization using algorithm selection belongs to the class of NP-complete problems [15]. Since the problem presented in [15] is a special case of the problem discussed in this paper, we can conclude that the new problem is also of high complexity, at least NP-complete.…”

Section: Complexitymentioning

confidence: 92%

“…The problem of throughput optimization using algorithm selection was recently addressed in [15]. However, our scope of the problem is signicantly broadened: we consider both algorithm selection and architecture selection, and more importantly, w e simultaneously consider two important transformations with algorithm/architecture matching: retiming and pipelining.…”

Section: Introductionmentioning

confidence: 99%

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Optimal ILP-based approach for throughput optimization using simultaneous algorithm/architecture matching and retiming

DeCastelo-Vide-e-Souza¹,

Potkonjak

Parker³

1995

Proceedings of the 32nd ACM/IEEE Conference on Design Automation Conference - DAC '95

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{ System level design and behavior transformations have been rapidly establishing themselves as design steps with the most inuential impact onnal performance metrics, throughput and latency, o f a design. In this paper we develop a formal ILPbased approach for throughput and latency optimization when algorithm-architecture matching, retiming, and pipelining are considered simultaneously. The effectiveness of the approach is demonstrated on several real-life examples.

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Section: Complexitymentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Optimal ILP-based approach for throughput optimization using simultaneous algorithm/architecture matching and retiming

DeCastelo-Vide-e-Souza¹,

Potkonjak

Parker³

1995

Proceedings of the 32nd ACM/IEEE Conference on Design Automation Conference - DAC '95

Self Cite

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“…Rice already proposed algorithm selection for operation system task schedulers [272]. It is also relevant for adaptive middleware [22,230], planning [81], hardware design [262], and generally all application domains in which:…”

Section: Summary: Algorithm Selection Approachesmentioning

confidence: 99%

Automatic Algorithm Selection for Complex Simulation Problems

Ewald¹

2012

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To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments, without demanding expert knowledge on simulation.The first part of the thesis surveys existing approaches to solve the algorithm selection problem and discusses techniques to analyze simulation algorithm performance. A unified categorization of existing algorithm selection techniques is worked out, as these stem from various research domains (e.g., finance, artificial intelligence).The second part introduces a software framework for automatic simulation algorithm selection and describes its constituents, as well as their integration into the modeling and simulation framework JAMES II. The implemented selection mechanisms are able to cope with three situations: a) no prior knowledge is available, b) the impact of problem features on performance is unknown, and c) a relationship between problem features and algorithm performance can be established empirically.An experimental evaluation of the developed methods concludes the thesis. It is shown that an automated algorithm selection may significantly increase the overall performance of a simulation system. Some of the presented mechanisms also support the research on simulation methods, as they facilitate their development and evaluation. ZusammenfassungDie Auswahl des passendsten Simulationsalgorithmus für eine bestimmte Aufgabe ist oftmals schwierig. Dies liegt an der komplexen Interaktion zwischen Modelleigenschaften, Implementierungsdetails und der Laufzeitumgebung, welche die Gesamtleistung stark beeinflussen kann. Eine automatisierte Auswahl von Simulationsalgorithmen unterstützt die Anwender eines Simulationssystems, indem sie eine geeignete Konfiguration des Systems ohne zusätzliches Fachwissen aus dem Bereich Simulation ermöglicht.Der erste Teil der Arbeit befasst sich eingehend mit Vorarbeiten zur automatischen Algorithmenauswahl, sowie mit der Leistungsanalyse von Simulationsalgorithmen. Eine einheitliche Kategorisierung wird erarbeitet um die aus den verschiedensten Fachgebieten (z.B. Finanzwesen, Künstliche Intelligenz) stammenden Ansätze zur Algorithmenauswahl einzuordnen.Der zweite Teil der Arbeit stellt ein Rahmenwerk zur automatischen Auswahl von Simulationsalgorithmen vor und beschreibt dessen Bestandteile, einschließlich ihrer Integration ins Modellierungsund Simulationsrahmenwerk JAMES II. Die realisierten Auswahlmechanismen beziehen sich dabei auf drei Situationen: a) keinerlei Vorwissen ist vorhanden, b) der Einfluss von Problemeigenschaften auf die Leistung ist unbekannt, und c) ein Zusammenhang zwischen Problemeigenschaften und Algorithmenleistung konnte empirisch ermittelt werden.Eine experimentelle Untersuchung der entwickelten Methoden schließt die Arbeit ab. Es wird gezeigt, dass eine aut...

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“…These heuristic algorithms only get the approximately optimal result, but the partitioning time can be decreased largely. Potkonjak and Rabaey proposed an ASIC Selecting Algorithm which is similar to classical task assigning problem [6]. Dava proposed an algorithm which solves classical co-synthesis problem for multi-cycle Distributed System [7].…”

Section: Introductionmentioning

confidence: 99%

Hardware/Software Partitioning for Heterogeneous Multicore SoC Using Genetic Algorithm

Luo

Dou

et al. 2012

2012 Second International Conference on Intelligent System Design and Engineering Application

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Hardware/software partitioning is a crucial problem in hardware/software co-design. In this paper, we deeply investigate genetic algorithm (GA) for hardware/software partitioning , our co-design targets a heterogeneous multicore system on chip (SoC) which consists of several different types of processing engines(PE), Communicating structure adopts NOC, We use GA for four task graphs to simulate the hardware/software partitioning, experiments show our method is an effective hardware/software partitioning algorithm.

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Algorithm Selection: A Quantitative Computation-intensive Optimization Approach

Cited by 8 publications

References 0 publications

Optimal ILP-based approach for throughput optimization using simultaneous algorithm/architecture matching and retiming

Optimal ILP-based approach for throughput optimization using simultaneous algorithm/architecture matching and retiming

Automatic Algorithm Selection for Complex Simulation Problems

Hardware/Software Partitioning for Heterogeneous Multicore SoC Using Genetic Algorithm

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