Grid computing presents two major challenges for deploying large scale applications across wide area networks gathering volunteers PC and clusters/parallel computers as computational resources: security and fault tolerance. This paper presents a lightweight Grid solution for the deployment of multi-parameters applications on a set of clusters protected by firewalls. The system uses a hierarchical design based on Condor for managing each cluster locally and XtremWeb for enabling resource sharing among the clusters. We discuss the security and fault tolerance mechanisms used for this design and demonstrate the usefulness of the approach measuring the performances of a multi-parameters bio-chemistry application deployed on two sites: University of Wisconsin/Madison and Paris South University. This experiment shows that we can efficiently and safely harness the computational power of about 200 PC distributed on two geographic sites.

Global Computing platforms, large scale clusters and future TeraGRID systems gather thousands of nodes for computing parallel scientific applications. At this scale, node failures or disconnections are frequent events. This Volatility reduces the MTBF of the whole system in the range of hours or minutes. We present MPICH-V, an automatic Volatility tolerant MPI environment based on uncoordinated checkpoint/roll-back and distributed message logging. MPICH-V architecture relies on Channel Memories, Checkpoint servers and theoretically proven protocols to execute existing or new, SPMD and Master-Worker MPI applications on volatile nodes. To evaluate its capabilities, we run MPICH-V within a framework for which the number of nodes, Channels Memories and Checkpoint Servers can be completely configured as well as the node Volatility. We present a detailed performance evaluation of every component of MPICH-V and its global performance for non-trivial parallel applications. Experimental results demonstrate good scalability and high tolerance to node volatility.

Global computing achieves high throughput computing by harvesting a very large number of unused computing resources connected to the Internet. This parallel computing model targets a parallel architecture defined by a very high number of nodes, poor communication performance and continuously varying resources. The unprecedented scale of the global computing architecture paradigm requires us to revisit many basic issues related to parallel architecture programming models, performance models, and class of applications or algorithms suitable for this architecture. XtremWeb is an experimental global computing platform dedicated to provide a tool for such studies. The paper presents the design of XtremWeb. Two essential features of this design are multi-applications and high-performance. Accepting multiple applications allows institutions or enterprises to set up their own global computing applications or experiments. High-performance is ensured by scalability, fault tolerance, efficient scheduling and a large base of volunteer PCs. We also present an implementation of the first global application running on XtremWeb.

Mismatches between on-chip high performance CPU and data access times is the basic reason for the increasing gap between peak and sustained performance in distributed memory parallel computers. We propose the concept of balanced architectures, based on a network with a dynamic topology and communication patterns determined at compile time. The corresponding processing element is a cacheless CPU, which can achieve a 1 FLOP/clock cycle rate. Network and PE features are presented. An example shows that balanced architectures keep efficiency when scaling.