There is a current need for scheduling policies that can leverage the performance variability of resources on multiuser clusters. We develop one solution to this problem called stochastic scheduling that utilizes a distribution of application execution performance on the target resources to determine a performance-efficient schedule. In this paper, we define a stochastic scheduling policy based on time-balancing for data parallel applications whose execution behavior can be represented as a normal distribution. Using three distributed applications on two contended platforms, we demonstrate that a stochastic scheduling policy can achieve good and predictable performance for the application as evaluated by several performance measures.

In this paper, we consider the problem of allocating a large number of independent, equalsized tasks to a heterogenerous "grid" computing platform. Such problems arise in collaborative computing eorts like SETI@home. We use a tree to model a grid, where resources can have dierent speeds of computation and communication, as well as dierent overlap capabilities. We dene a base model, and show how to determine the maximum steady-state throughput of a node in the base model, assuming we already know the throughput of the subtrees rooted at the node's children. Thus, a bottom-up traversal of the tree determines the rate at which tasks can be processed in the full tree. The best allocation is bandwidth-centric: if enough bandwidth is available, then all nodes are kept busy; if bandwidth is limited, then tasks should be allocated only to the children which have suciently small communication times, regardless of their computation power. We then show how nodes with other capabilities ones that allow more or less overlapping of computation and communication than the base model can be transformed to equivalent nodes in the base model. We also show how to handle a more general communication model. Finally, we present simulation results of several demand-driven task allocation policies that show that our bandwidth-centric method obtains better results than allocating tasks to all processors on a rst-come, rst serve basis. Key words: heterogeneous computer, allocation, scheduling, grid, metacomputing. Corresponding author: Jeanne Ferrante The work of Larry Carter and Jeanne Ferrante was performed while visiting LIP. 1 1

As Data Grids become more commonplace, large data sets are being replicated and distributed to multiple sites, leading to the problem of determining which replica can be accessed most efficiently. The answer to this question can depend on many factors, including physical characteristics of the resources and the load behavior on the CPUs, networks, and storage devices that are part of the end-to-end path linking possible sources and sinks.

Data-intensive, interactive applications are an important class of metacomputing (Grid) applications. They are characterized by large dataflows between data providers and consumers, like scientific simulations and remote visualization clients of simulation output. Such dataflows vary at runtime, due to changes in consumers' data needs, changes in the nature of the data being transmitted, or changes in the availability of computing resources used by flows. The topic

this paper, we address the issue of implementing matrix multiplication on heterogeneous platforms. We target two different classes of heterogeneous computing resources: heterogeneous networks of workstations and collections of heterogeneous clusters. Intuitively, the problem is to load balance the work with different speed resources while minimizing the communication volume. We formally state this problem in a geometric framework and prove its NP-completeness. Next, we introduce a (polynomial) column-based heuristic, which turns out to be very satisfactory: We derive a theoretical performance guarantee for the heuristic and we assess its practical usefulness through MPI experiments

The increasingly common practice of replicating datasets and using resources as distributed data stores in Grid environments has led to the problem of determining which replica can be accessed most efficiently. Because of diverse performance characteristics and load variations of several components in the end-to-end path linking these various locations, selecting a replica from among many requires accurate prediction information of the data transfer times between the sources and sinks. In this paper we present a prediction system that is based on combining end-to-end application throughput observations and network load variations, capturing wholesystem performance and variations in load patterns, respectively. We develop a set of regression models to derive predictions that characterize the effect of network load variations on file transfer times. We apply these techniques to the GridFTP data movement tool, part of the Globus Toolkit™, and observe performance gains of up to 10 % in prediction accuracy when compared with approaches based on past system behavior in isolation.

. Networks of workstations offer inexpensive and highly available high performance computing environments. A critical issue for achieving good performance in any parallel system is load balancing, even more so in workstation environments where the machines might be shared among many users. In this paper, we present and evaluate a system that combines compiler and run-time support to achieve load balancing dynamically on software distributed shared memory programs. We use information provided by the compiler to help the run-time system distribute the work of the parallel loops, not only according to the relative power of the processors, but also in such a way as to minimize communication and page sharing. 1 Introduction Clusters of workstations, whether uniprocessors or symmetric multiprocessors (SMPs), offer cost-effective and highly available parallel computing environments. Software distributed shared memory (SDSM) provides a shared memory abstraction on a distributed memory machine...

The paper presents a new advanced version of the mpC parallel language. The language was designed specially for programming high-performance parallel computations on heterogeneous networks of computers. The advanced version allows the programmer to define at runtime all the main features of the underlying parallel algorithm, which have an impact on the application execution performance. The mpC programming system uses this information along with the information about the performance of the executing network to map the processes of the parallel program to this network so as to achieve better execution time.

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This paper discusses some algorithmic issues when computing with a heterogeneous network of workstations (the typical poor man's parallel computer). Dealing with processors of dierent speeds requires to use more involved strategies than block-cyclic data distributions. Dynamic data distribution is a rst possibility but may prove impractical and not scalable due to communication and control overhead.