Tlib—a library to support programming with hierarchical multi-processor tasks

Rauber, Thomas; Rünger, Gudula

doi:10.1016/j.jpdc.2004.10.006

Cited by 30 publications

(16 citation statements)

References 25 publications

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“…Examples for existing hierarchical programming models are the TwoL [85] and the Tlib [86] approaches, which are both defined on top of MPI and allow a flexible and hierarchical grouping of processes into groups each of which can execute multi-processor tasks (M-tasks). The Mtasks are the basic execution units and each M-task can be executed by an arbitrary number of processing cores.…”

Section: Hierarchical Programming Modelsmentioning

confidence: 99%

Energy-efficient Algorithms for Ultrascale Systems

Carretero

Distefano

Petcu

et al. 2015

JSFI

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The chances to reach Exascale or Ultrascale Computing are strongly connected with the problem of the energy consumption for processing applications. For physical and economical reasons, the energy consumption has to be reduced significantly to make Ultrascale Computing possible. The research efforts towards energy-saving mechanisms of the hardware have already made energy-aware hardware systems available. However, to achieve a strong energy reduction, hardware mechanisms must be complemented with new energy-efficient software that can exploit them so that the foreseen energy savings actually result. In the software area, there also exist a multitude of research approaches towards energy saving, often concentrating either on the system software level or the application organization level, reflecting the expertise of the corresponding research group. The challenge of reducing the energy consumption dramatically to make Ultrascale Computing possible is so ambitious that a concerted action combining research efforts through all the software levels seems reasonable. In this article, we discuss the current research efforts and results related to energy efficiency in the diverse areas of software. We conclude with open problems and questions concerning energy-related techniques with an emphasis on the application or algorithmic side.

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Section: Hierarchical Programming Modelsmentioning

confidence: 99%

Energy-efficient Algorithms for Ultrascale Systems

Carretero

Distefano

Petcu

et al. 2015

JSFI

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“…Using multi-processor tasks can improve the performance of parallel programs due to a reduced communication overhead [9,13]. In previous work we have proposed a concept how to execute M-tasks on heterogenous systems and grid environments [12] and we have presented an approach to provide efficient scheduling strategies for each M-task [14].…”

Section: The Tgrid Runtime System In Detailmentioning

confidence: 99%

“…All components can be arbitrary single-processor or multiprocessor tasks. However, the main design goal of TGrid is the support of hierarchically-structured multi-processor tasks preferably based on TLib [13].…”

Section: The Tgrid Runtime System In Detailmentioning

confidence: 99%

“…Examples of such applications include environmental models combining atmospheric, surface water and water models, or aircraft simulations combining models for fluid dynamics, structural dynamics and surface heating. Coding such applications with hierarchically structured multiprocessor tasks (M-tasks) has been shown to be successful for parallel platforms with a distributed address space [13] , since the corresponding group-based execution of communication operations can help to reduce the communication overhead. This effect is most significant if collective communication operations like broadcast or gather are involved and can be exploited to obtain message-passing programs that are scalable for a larger number of processors.…”

Section: Introductionmentioning

confidence: 99%

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TGrid - Grid runtime support for hierarchically structured task-parallel programs

Hunold

Rauber

Rünger

2006

2006 IEEE International Conference on Cluster Computing

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In this article we introduce a grid runtime system called TGrid which is designed to run hierarchically structured task-parallel programs on heterogenous environments and can also be used for common component-based grid programming. TGrid is built on top of a location-aware communication layer which enables the runtime system to cluster grid nodes. As a result, the component scheduler assigns a multi-processor task to a set of processors taking into account the spatial locality within the available processors. The multi-processor task directly benefits from having less network overhead and thus, the overall runtime of a gridenabled multi-processor program is reduced.

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“…the task graph can be seen in the program structure, or the task graph can be more implicit when iterative or recursive module structures are allowed [10]. A suitable library for M-task programming is Tlib [17].…”

Section: Programming Modelmentioning

confidence: 99%

Anticipated distributed task scheduling for grid environments

Rauber¹,

Runger²

2006

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

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Heterogeneous distributed environments or grid environments provide large computing resources for the execution of large scientific applications. The effective use of those platforms requires a suitable representation of the application algorithm which makes a distribution of parts of the application across the distributed environment possible. A representation of an application algorithm in form of interacting tasks has been shown to be a suitable programming model for those distributed environments, where tasks can be shipped to remote computing resources for execution. The efficient execution of an application also depends on the time for sending tasks and data to remote resoures, which adds an additional overhead to the distributed execution time. In this paper, we propose a method to overlap the execution of current tasks with the shipping time for tasks to be executed later. The efficient overlapping is achieved by an anticipated scheduling algorithm for the placement of future task executions.

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Tlib—a library to support programming with hierarchical multi-processor tasks

Cited by 30 publications

References 25 publications

Energy-efficient Algorithms for Ultrascale Systems

Energy-efficient Algorithms for Ultrascale Systems

TGrid - Grid runtime support for hierarchically structured task-parallel programs

Anticipated distributed task scheduling for grid environments

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