Section: Overall Objectives

Introduction

Parallel computing has spread into all fields of applications, from classical simulation of mechanical systems or weather forecast to databases, video-on-demand servers or search tools like Google. From the architectural point of view, parallel machines have evolved from large homogeneous machines to clusters of PCs (with sometimes boards of several processors sharing a common memory, these boards being connected by high speed networks like Myrinet). However, the need of computing or storage resources has continued to grow leading to the need of resource aggregation through Local Area Networks (LAN) or even Wide Area Networks (WAN). The recent progress of network technology has enabled the use of highly distributed platforms as a single parallel resource. This has been called Metacomputing or more recently Grid Computing [82] . An enormous amount of financing has recently been put into this important subject, leading to an exponential growth of the number of projects, most of them focusing on low level software detail. We believe that many of these projects failed to study fundamental issues such as the computational complexity of problems and algorithms and heuristics for scheduling problems. Also they usually have not validated their theoretical results on available software platforms.

From the architectural point of view, Grid Computing has different scales but is always highly heterogeneous and hierarchical. At a very large scale, tens of thousands of PCs connected through the Internet are aggregated to solve very large applications. This form of the Grid, usually called a Peer-to-Peer (P2P) system, has several incarnations, such as SETI@home, Gnutella or XtremWeb [91] . It is already used to solve large problems (or to share files) on PCs across the world. However, as today's network capacity is still low, the applications supported by such systems are usually embarrassingly parallel. Another large-scale example is TeraGRID which connects several supercomputing centers in the USA and reaches a peak performance of over 100 Teraflops. At a smaller scale but with a high bandwidth, one can mention the Grid'5000 project, which connects PC clusters spread in nine French university research centers. Many such projects exist over the world that connect a small set of machines through a fast network. Finally, at a research laboratory level, one can build an heterogeneous platform by connecting several clusters using a fast network such as Myrinet.

The common problem of all these platforms is not the hardware (these machines are already connected to the Internet) but the software (from the operating system to the algorithmic design). Indeed, the computers connected are usually highly heterogeneous (from clusters of SMPs to the Grid).

There are two main challenges for the widespread use of Grid platforms: the development of environments that will ease the use of the Grid (in a seamless way) and the design and evaluation of new algorithmic approaches for applications using such platforms. Environments used on the Grid include operating systems, languages, libraries, and middlewares [80] , [82] , [84] . Today's environments are based either on the adaptation of “classical” parallel environments or on the development of toolboxes based on Web Services.

Aims of the Graal project.

In the Graal project we work on the following research topics:

• algorithms and scheduling strategies for heterogeneous and distributed platforms,

• environments and tools for the deployment of applications over service oriented platforms.

The main keywords of the Graal project:

Algorithmic Design + Middleware/Libraries + Applications

over Heterogeneous and Distributed Architectures