Program Control Language: a programming language for adaptive distributed applications

Ensink, Brian; Stanley, J D; Adve, Vikram

doi:10.1016/s0743-7315(03)00117-5

Cited by 29 publications

(17 citation statements)

References 41 publications

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“…In the language and compiler approaches for implementing program adaptation, our work is similar to Program Control Language (PCL) [2] in that centralized design of adaptation strategies can be specified at a high level for distributed programs. The expressive power of PCL comes from its underlying framework which offers a global representation of the distributed program as a graph of task nodes, the static task graph (STG), connected by edges indicating precedence relationships.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches to support program adaptation include: languages and compilers for specifying adaptation strategies [1,2,3] and runtime platforms or middleware for adaptive execution [4,5,6,7]. These efforts are primarily centered around resource management to achieve efficient utilization of the environment, such as adaptive load-balancing and scheduling of application tasks, to match resource constraints or dynamic operating conditions of the environment.…”

Section: Introductionmentioning

confidence: 99%

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Modular, Fine-Grained Adaptation of Parallel Programs

Kang

Selvarasu

Ramakrishnan

et al. 2009

Lecture Notes in Computer Science

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Abstract. We present a modular approach to realizing fine-grained adaptation of program behavior in a parallel environment. Using a compositional framework based on function call interception and manipulation, the adaptive logic to monitor internal program states and control the behavior of program modules is written and managed as a separate code, thus supporting centralized design of complex adaptation strategies for adapting to dynamic changes within an application. By 'catching' the functions that execute in synchronization across the parallel environment and inserting the adaptive logic operations at the intercepted control points, the proposed method provides a convenient way of synchronous adaptation without disturbing the parallel execution and communication structure already established in the original program. Applying our method to a CFD (computational fluid dynamics) simulation program to implement example adaptation scenarios, we demonstrate how effectively applications can change their behavior through fine-grained control.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modular, Fine-Grained Adaptation of Parallel Programs

Kang

Selvarasu

Ramakrishnan

et al. 2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…These problems may be categorized as static (15,16,17,19,29) and dynamic (2,14,18,19,26,27) in nature. Some of the other related methods have been reported in the literature, such as, Integer programming (7,23), Branch and Bound technique (28), Matrix Reduction technique (11,30,31), Reliability Optimization (1,12,20,21,24), Load Balancing (2,9,10) and Modeling (3,6,8). The series parallel redundancyallocation problem has been studied with different approaches, such as, Dynamic programming (4, 10, 13), Integer programming (7,23), and Heuristic techniques (5, 22, 25).…”

Section: Introductionmentioning

confidence: 99%

Performance Upgradation through Task Allocation of Distributed Networks

Govil¹

2018

IJCA

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Normally the distributed network has to execute the tasks that shall be the more than the number of processors. The assignment problem is a case of linear programming helps to solve the problems related to tasks and processors. The problem of execution of "m" tasks to "n" processors (m > n) in a distributed networks is addressed here through a new modified tasks allocation policy for distributed networks. The model, presented in this paper allocates the tasks or modules to the processor to increase the performance and to reduce the execution time. This paper reduces the problem of allocation of tasks where number of processors is less than the number of tasks. The example mentioned in the paper has three tasks and solved it in such a way that the task t 1 processed with minimum time, the task t 2 with minimum cost while the task t 3 with maximum reliability. In this problem, the tasks are fused (or clubbed) with another task(s) on the basis of minimum communication cost to form a balanced allocation.

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“…Some approaches, e.g., [7,8], work at the pre-compiler level and provide adaptivity around programmed breakpoints by use of locks and barriers in parallel codes. Others, e.g., [13], specify adaptivity decisions in terms of operations on intermediate representations [2], also relying on compiler technology to help abstract distributed program behavior. The adaptive programming literature views adaptivity as a crosscutting concern, akin to aspect-oriented programming [20], and aims to provide flexible join points between modules, supporting remapping and manipulation of function invocations.…”

Section: Introductionmentioning

confidence: 99%

The Adaptive Code Kitchen: Flexible Tools for Dynamic Application Composition

Kang

Heffner

Mukherjee

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

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Driven by the increasing componentization of scientific codes, the deployment of high-end system infrastructures such as the Grid, and the desire to support high level problem solving primitives, application composition systems have become prevalent in computational science practice. We present the adaptive code kitchen which, as the name connotes, is a loose collection of capabilities to help realize complex adaptive composition scenarios. These include function interception, continuation modification, dynamic process checkpointing and rollback, and runtime recommendation. Using these broad primitives, a computational scientist can specify many 'recipes' of adaptivity as complete control systems around native object codes. Runtime systems support then enables loading and linking of native code components, monitoring of performance indicators, consulting a recommender system for algorithmic decisions, and dynamically updating application components in response to the recommendations. We present the architecture of the adaptive code kitchen and the key enabling technologies with brief mention of the applications that will be investigated henceforth during the course of the project.

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Program Control Language: a programming language for adaptive distributed applications

Cited by 29 publications

References 41 publications

Modular, Fine-Grained Adaptation of Parallel Programs

Modular, Fine-Grained Adaptation of Parallel Programs

Performance Upgradation through Task Allocation of Distributed Networks

The Adaptive Code Kitchen: Flexible Tools for Dynamic Application Composition

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