. This paper presents cache performance data for blocked programs and evaluates several optimizations to improve this performance. The data is obtained by a theoretical model of data conflicts in the cache, which has been validated by large amounts of simulation. We show that the degree of cache interference

statistical modelling techniques. The size of the input block must be large (a few kilobytes) to achieve good compression.

simultaneously on multi-ple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation pro-gresses to make efficient use of the available resources. Dryad is designed to scale from powerful multi-core sin

channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes hut also for any linear binary systematic block code. The iteration is controlled by a

It is always desirable to use acquired data in the most effective way possible. This paper explores acquiring several contiguous blocks of data and then processing the data in various ways. The first method will be the traditional approach of utilizing each contigu-ous block, in whole, as one RMS

these techniques have been previously studied in the literature, they have usually been compared with the performance of small block Reed Solomon codes. This paper presents a novel comparison between the per-formance of LDPC-LDGM codes and Raptor codes as alternatives for packet level FEC when large codifica-tion

-ior of an arbitrary section of execution in a program. To this end we examine the use of Basic Block Vectors. We quantify the effectiveness of Basic Block Vectors in capturing program behavior across several different architectural met-rics, explore the large scale behavior of several programs, and develop a set

Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new

We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling

This paper presents Scribe, a scalable application-level multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peer-to-peer object location and routing substrate overlayed on the Internet