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International audienceP2PDC is an environment for high performance peer to peer computing that allows direct communication between peers. This environment is based on P2PSAP, a self adaptive communication protocol. P2PDC is suited to the solution of large scale numerical simulation problems via distributed iterative methods. dPerf is a performance prediction environment for parallel and distributed applications, with primary interest in programs written in C, C++, Fortran for P2PDC. The dPerf performance prediction tool makes use of static and dynamic analyses combined with trace-based simulation. In this paper, we present a decentralized version of P2PDC and show how dPerf predicts performance for the P2PDC environment. We present new features of P2PDC aimed at making it more scalable and robust. Through experiments with P2PDC and dPerf, we show how to properly choose a peer to peer computing system which can match the computing power of a cluster

International audienceRecently, a new environment for high performance peer-to-peer distributed computing was proposed. This environment , named P2PDC, addresses stable or volatile systems communicating in a decentralized manner using the self-adaptive protocol P2PSAP. P2PDC is devoted to task parallel applications like numerical simulation problems or optimization problems solved via parallel or distributed iterative algorithms. For distributed applications meant to run with P2PDC, a performance prediction tool named dPerf was proposed. dPerf combines static and dynamic analysis with trace-based simulation to provide scientist with information about the execution of their large scale numerical simulation applications. dPerf addresses real parallel and distributed numerical simulation and optimisation applications written in C, C++ or Fortran for P2PDC. This paper introduces an enhancement of the dPerf tool which provides scalable performance prediction results. Scaling is done with respect to (i) network configuration and (ii) number of peers. Scaling predictions based on network configuration is achieved through trace-based simulation, where various architectures can be studied. Scaling predictions based on the number of peers implies analyzing the communication topology and modifying trace files prior to simulation. We present experimental results obtained for the obstacle problem, a C/P2PDC implementation of the code used in mechanics and finance. Prediction for this application is computed under real conditions, with a reduced slowdown and by providing user with scalable results

Abstract-The distributed iterative solution of numerical simulation problems on Infiniband or Ethernet Clusters via the P2PDC environment is studied. The P2PDC decentralized environment is dedicated to task parallel applications. It has been designed for the solution of large scale numerical simulation problems via distributed iterative algorithms. The P2PDC environment is based on the P2PSAP self-adaptive communication protocol. New functionalities of the P2PSAP communication protocol aimed at using Infiniband clusters are presented. A series of computational results is presented and analyzed.

This paper deals with the numerical solution of financial applications, more specifically the computation of American and European options derivatives modelled by boundary values problems. In such applications we have to solve large-scale algebraic linear systems. We concentrate on synchronous and asynchronous parallel iterative algorithms carried out on peer-to-peer networks. The properties of the operators arising in the discretized problem ensure the convergence of the parallel iterative synchronous and asynchronous algorithms. Computational experiments performed on peer-to-peer networks are presented and analyzed.

A new tool and web portal are presented for deployment of High Performance Computing applications on distributed heterogeneous computing platforms. This tool relies on the decentralized environment P2PDC and the OMF and OML multithreaded control, instrumentation and measurement libraries. Deployment on PlanetLab of a numerical simulation application is studied. A first series of computational results is displayed and analyzed.