Grid computing is a very important research area with a great practical impact. By nature, the topics of Grid computing cover many types of systems. Speaking about metacomputing implies to have built a metacomputer, which can be defined as a collection of elements providing a variety of functionalities (networking, computation, graphics) that can be used to run multiple tasks with several computational requirements. The key point is that, to the individual user, it should look and act like a single computer. Of course, all these capabilities can be a achieved by a set of high-performance machines interconnected by a fast dedicated local-area network. But a metacomputer may also be made of geographically distributed computational and information resources spread over the Internet, since emerging high-performance networks promise to enable a wide range of emerging application concepts such as remote computing, distributed supercomputing, tele-immersion, smart instruments, and data mining.
On such a metacomputing environment, all well known standard services such as authentication, resource location, resource allocation, configuration, communication, file access, fault detection, and executable management… are becoming harder to deploy, to maintain or simply to access. Thus, applications in this kind of environment may achieve performance by exploiting the adequacy between the problem to solve and the different platforms or paradigms available. The challenge for the computer science community is to provide a solid, integrated middleware foundation on which to build wide-area applications. A metacomputing system must support the illusion of a single machine by transparently scheduling application components on processors, managing data migration, caching, transfer and masking of data-format differences between systems; detecting and managing faults and ensuring that users' data and physical resources are protected.
Unlike dedicated parallel computer systems, local networks are inherently heterogeneous. They consist of diverse computers of different performances interconnected via mixed network equipment providing communication links of different speeds and bandwidths. Traditional parallel algorithms and tools are aimed at homogeneous multiprocessors and cannot be efficiently used for parallel computing on heterogeneous clusters. New ideas, dedicated algorithms and tools are needed to efficiently use this most common but most irregular parallel architecture.
The first and second International Workshops on Metacomputing Systems and Applications (MSA'2000 and MSA'2001) as well as the first International Workshop on Algorithms and Tools for Parallel Computing on Heterogeneous Clusters (HeteroPar'2001) were recently held in conjunction with ICPP '2000 and PDPTA'2001 respectively. Building on the success of these workshops, this special issue of Parallel and Distributed Computing Practice features papers that explore algorithms, programming languages, systems, tools and theoretical models aimed at high performance computing on heterogeneous networks of computers.
Frédéric Desprez, LIP ENS Lyon, France
Eric Fleury, CITI INSA-Lyon, France
Alexey Kalinov, Institute for System Programming, Moscow, Russia
Alexey Lastovetsky, Dept of Computer Science, University College Dublin, Ireland