This paper presents an adaptive scheduling method, which can be used for parallel applications whose total workload is unknown a priori. This method can deal with the unpredictable execution conditions commonly encountered on grids. To address this scheduling problem, parameters which quantify the dynamic nature of the execution conditions had to be defined. The proposed scheduling method is based on an online algorithm so as to be adaptable to the varying execution conditions, but avoids the idle periods inherent to this on-line algorithm.

In this paper, we present a runtime method for scheduling parallel applications on dynamic and heterogeneous platforms. It can be used to schedule parallel applications whose total workload is unknown a priori. It can also handle the heterogeneous and dynamic conditions of execution that are typical of grids. The method delivers the workload through multiple rounds in order to improve communication/computation overlap on an existing on-line algorithm.

In this paper, we present an adaptive method for scheduling parallel applications on unspecified distributed memory platforms. The presented method can be used to schedule parallel applications when the total workload and the execution parameters (communication speed, available computing power...) are unspecified. When used to schedule divisible load applications according to a masterworker model, this method delivers the workload through multiple rounds. In order to maximize the throughput of each worker, it can prevent both idleness in the use of workers and contentions in the use of the links between master and workers. Before focusing on the proposed scheduling method, the paper recalls the underlying methods on which its development relies. The paper then gives a theoretical analysis of the method before presenting results of simulations obtained with the SimGrid framework on a limited distributed memory platform.

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The grid usage is facing the problem that consists in running existing sequential code for parallel execution transparently (ie. using source code without modification). AIPE is a middleware that deals with this problem. MPIPP is the software component we have developed to allow the generation of MPI derived datatypes from C datatype definitions automatically. The goal of this new tool is to make the building of complex messages easier for the end-user. Moreover, this paper shows that MPIPP goes farther in the complexity level of C datatypes that can be taken into account than any other similar tools have ever gone to.

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