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Ratings for products and services are increasingly important on the Internet, as they allow users to harvest the wisdom of the community in making decisions. However, the difficulty with ratings is that little is known about the people providing them. Interpreting ratings well requires that the reputations of raters be factored into the scores computed for rated objects, even though these reputations are not explicitly available. Taking advantage of the insight that reputation can be computed implicitly from ratings, this paper addresses the reputation problem for raters and its application to evaluating rated objects. We develop a general method to automatically compute reputations for raters based on the ratings they and others give to objects, and incorporate these reputations to generate value-added information about rated objects. We evaluate our mechanisms by performing experiments on data from major rating sites, and show that they have the desired properties of a good reputation system. In the process, we analyze some key characteristics of different types of Internet ratings. To our knowledge, this is the first investigation into automatically computing raters' reputations and applying these reputations to better evaluate rated objects.

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We present parallel versions of a representative N-body application that uses Greengard and Rokhlin's adaptive Fast Multipole Method (FMMJ While parallel implementations of the umform FMM are straightforward and have been developed on alfferent architectures, the aalzptive version complicates the task of obtaining eflective parallel peflormance owing to the nonunz~onn and dynamically changing nature of the problem &mains to which it is applied.We propose and evaluate two techniques for providing load balancing and data locality, both of which take advantage of key insights into the method and its typical applications.Using the better of these techm"ques, we demonstrate 45-fold speedups on galactic sinudations on a 48-processor Stanford DASH machine, a state-of-the-art shared address space multiprocessor even for relatively small problems.We also show good speedups on a 2-ring Kendall Square Research KSR-I. Finally we summarize some key architectural implications of this important computational method.

The performance of page-based software shared virtual memory (SVM) is still far from that achieved on hardware-coherent distributed shared memory (DSM) systems. The interrupt cost for asynchronous protocol processing has been found to be a key source of performance loss and complexity.This paper shows that by providing simple and general support for asynchronous message handling in a commodity network interface (NI), and by altering SVM protocols appropriately, protocol activity can be decoupled from asynchronous message handling and the need for interrupts or polling can be eliminated. The NI mechanisms needed are generic, not SVM-dependent. They also require neither visibility into the node memory system nor code instrumentation to identify memory operations. We prototype the mechanisms and such a synchronous home-based LRC protocol, called GeNIMA (GEneral-purpose Network Interface support in a shared Memory Abstraction), on a cluster of SMPs with a programmable NI, though the mechanisms are simple and do not require programmability.We find that the performance improvements are substantial, bringing performance on a small-scale SMP cluster much closer to that of hardware-coherent shared memory for many applications, and we show the value of each of the mechanisms in different applications. Application performance improves by about 37% on average for reasonably well performing applications, even on our relatively slow programmable NI, and more for others. We discuss the key remaining bottlenecks at the protocol level and use a firmware performance monitor in the NI to understand the interactions with and the implications for the communication layer.