Energy efficiency is the new fundamental limiter of processor performance, way beyond numbers of processors.

The exploitation of idle cycles on pervasive desktop PC systems offers the opportunity to increase the available computing power by orders of magnitude (10Â-1000Â). However, for desktop PC distributed computing to be widely accepted within the enterprise, the systems must achieve high levels of efficiency, robustness, security, scalability, manageability, unobtrusiveness, and openness/ ease of application integration.We describe the Entropia distributed computing system as a case study, detailing its internal architecture and philosophy in attacking these key problems. Key aspects of the Entropia system include the use of: (1) binary sandboxing technology for security and unobtrusiveness, (2) a layered architecture for efficiency, robustness, scalability and manageability, and (3) an open integration model to allow applications from many sources to be incorporated.Typical applications for the Entropia System includes molecular docking, sequence analysis, chemical structure modeling, ;and risk management. The applications come from a diverse set of domains including virtual screening for drug discovery, genomics for drug targeting, material property prediction, and portfolio management. In all cases, these applications scale to many thousands of nodes and have no dependences between tasks. We present representative performance results from several applications that illustrate the high performance, linear scaling, and overall capability presented by the Entropia system. r

Advances in networking technologies will soon make it possible to use the global information infrastructure in a qualitatively different way—as a computational as well as an information resource. As described in the recent book The Grid: Blueprint for a New Computing Infrastructure, this Grid will connect the nation’s computers, databases, instruments, and people in a seamless web of computing and distributed intelligence, which can be used in an on-demand fashion as a problem-solving resource in many fields of human endeavor—and, in particular, science and engineering. The availability of grid resources will give rise to dramatically new classes of applications, in which computing resources are no longer localized but, rather, distributed, heterogeneous, and dynamic; computation is increasingly sophisticated and multidisciplinary; and computation is integrated into our daily lives and, hence, subject to stricter time constraints than at present. The impact of these new applications will be pervasive, ranging from new systems for scientific inquiry, through computing support for crisis management, to the use of ambient computing to enhance personal mobile computing environments. To realize this vision, significant scientific and technical obstacles must be overcome. Principal among these is usability. The goal of the Grid Application Development Software (GrADS) project is to simplify distributed heterogeneous computing in the same way that the World Wide Web simplified information sharing over the Internet. To that end, the project is exploring the scientific and technical problems that must be solved to make it easier for ordinary scientific users to develop, execute, and tune applications on the Grid. In this paper, the authors describe the vision and strategies underlying the GrADS project, including the base software architecture for grid execution and performance monitoring, strategies and tools for construction of applications from libraries of grid-aware components, and development of innovative new science and engineering applications that can exploit these new technologies to run effectively in grid environments.

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Desktop resources are attractive for running compute-intensive distributed applications. Several systems that aggregate these resources in desktop grids have been developed. While these systems have been successfully used for a wide variety of high throughput applications there has been little insight into the detailed temporal structure of CPU availability of desktop grid resources. Yet, this structure is critical to characterize the utility of desktop grid platforms for both task parallel and even data parallel applications.We address the following questions: (i) What are the temporal characteristics of desktop CPU availability in an enterprise setting? (ii) How do these characteristics affect the utility of desktop grids? (iii) Based on these characteristics, can we construct a model of server "equivalents" for the desktop grids, which can be used to predict application performance? We present measurements of an enterprise desktop grid with over 220 hosts running the Entropia commercial desktop grid software. We utilize these measurements to characterize CPU availability and develop a performance model for desktop grid applications for various task granularities, showing that there is an optimal task size. We then introduce a new metric, cluster equivalence, which we use to quantify the utility of the desktop grid relative to that of a dedicated cluster.