eScience is rapidly changing the way we do research. As a result, many research labs now need non-trivial computational power. Grid and voluntary computing are well-established solutions for this need. However, not all labs can effectively benefit from these technologies. In particular, small and medium research labs (which are the majority of the labs in the world) have a hard time using these technologies as they demand high visibility projects and/or high-qualified computer personnel. This paper describes OurGrid, a system designed to fill this gap. OurGrid is an open, free-to-join, cooperative Grid in which labs donate their idle computational resources in exchange for accessing other labs' idle resources when needed. It relies on an incentive mechanism that makes it in the best interest of participants to collaborate with the system, employs a novel application scheduling technique that demands very little information, and uses virtual machines to isolate applications and thus provide security. The vision is that OurGrid enables labs to combine their resources in a massive worldwide computing platform. OurGrid is in production since December 2004. Any lab can join it by downloading its software from http://www.ourgrid.org.

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Large, real-world graphs are famously difficult to process efficiently. Not only they have a large memory footprint but most graph processing algorithms entail memory access patterns with poor locality, data-dependent parallelism, and a low compute-tomemory access ratio. Additionally, most real-world graphs have a low diameter and a highly heterogeneous node degree distribution. Partitioning these graphs and simultaneously achieve access locality and load-balancing is difficult if not impossible. This paper demonstrates the feasibility of graph processing on heterogeneous (i.e., including both CPUs and GPUs) platforms as a cost-effective approach towards addressing the graph processing challenges above. To this end, this work (i) presents and evaluates a performance model that estimates the achievable performance on heterogeneous platforms; (ii) introduces TOTEM -a processing engine based on the Bulk Synchronous Parallel (BSP) model that offers a convenient environment to simplify the implementation of graph algorithms on heterogeneous platforms; and, (iii) demonstrates TOTEM'S efficiency by implementing and evaluating two graph algorithms (PageRank and breadth-first search). TOTEM achieves speedups close to the model's prediction, and applies a number of optimizations that enable linear speedups with respect to the share of the graph offloaded for processing to accelerators.

This paper investigates the power, energy, and performance characteristics of large-scale graph processing on hybrid (i.e., CPU and GPU) single-node systems. Graph processing can be accelerated on hybrid systems by properly mapping the graphlayout to processing units, such that the algorithmic tasks exercise each of the units where they perform best. However, the GPUs have much higher Thermal Design Power (TDP), thus their impact on the overall energy consumption is unclear. Our evaluation using large real-world graphs and synthetic graphs as large as 1 billion vertices and 16 billion edges shows that a hybrid system is efficient in terms of both time-to-solution and energy.

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-This paper evaluates the potential gains a workflow-aware storage system can bring. Two observations make us believe such storage system is crucial to efficiently support workflow-based applications: First, workflows generate irregular and application-dependent data access patterns. These patterns render existing storage systems unable to harness all optimization opportunities as this often requires conflicting optimization options or even conflicting design decision at the level of the storage system. Second, when scheduling, workflow runtime engines make suboptimal decisions as they lack detailed data location information. This paper discusses the feasibility, and evaluates the potential performance benefits brought by, building a workflow-aware storage system that supports per-file access optimizations and exposes data location. To this end, this paper presents approaches to determine the application-specific data access patterns, and evaluates experimentally the performance gains of a workflowaware storage approach. Our evaluation using synthetic benchmarks shows that a workflow-aware storage system can bring significant performance gains: up to 7x performance gain compared to the distributed storage system -MosaStore and up to 16x compared to a central, well provisioned, NFS server.

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