Publications

A linear algebraic approach to graph algorithms that exploits the sparse adjacency matrix representation of graphs can provide a variety of benefits. These benefits include syntactic simplicity, easier implementation, and higher performance. Selected examples are presented illustrating these benefits. These examples are drawn from the remainder of the book in...

Graph algorithms are used for numerous database applications such as analysis of financial transactions, social networking patterns, and internet data. While graph algorithms can work well with moderate size databases, processors often have difficulty providing sufficient throughput when the databases are large. This is because the processor architectures are poorly...

Matlab is one of the most commonly used languages for scientific computing with approximately one million users worldwide. Many of the programs written in matlab can benefit from the increased performance offered by multicore processors and parallel computing clusters. The Lincoln pMatlab library (http://www.ll.mit.edu.ezproxyberklee.flo.org/pMatlab) allows high performance parallel programs to...

An analytical theory of power law graphs is presented basedon the Kronecker graph generation technique. The analysisuses Kronecker exponentials of complete bipartite graphsto formulate the sub-structure of such graphs. This allows various high level quantities (e.g. degree distribution,betweenness centrality, diameter, eigenvalues, and isoparametric ratio) to be computed directly from the...

This chapter presents that high performance embedded computing (HPEC) software architectures and evaluation metrics. A canonical HPEC application is used to illustrate basic concepts. The chapter discusses different types of parallelism are reviewed, and performance analysis techniques. It presents a typical programmable multicomputer and explores the performance trade-offs of different...

This chapter focuses on the computational complexity of the front-end of the surface moving-target indication (SMTI) radar application. SMTI radars can require over one trillion operations per second of computation for wideband systems. The adaptive beamforming performed in SMTI radars is one of the major computational complexity drivers. The goal...

This chapter discusses parallel and distributed programming technologies for high performance embedded systems. Computational or memory constraints can be overcome with parallel processing. The primary goal of parallel processing is to improve performance by distributing computation across multiple processors or increasing dataset sizes by distributing data across multiple processors’ memory...

High Performance Computing has seen extraordinary growth in peak performance which has been accompanied by a significant increase in the difficulty of using these systems. High Productivity Computing Systems (HPCS) seek to address this gap by producing petascale computers that are usable by a broader range of scientists and engineers...

Evaluation of High Performance Computing (HPC) systems should take into account software development time productivity in addition to hardware performance, cost, and other factors. We propose a new metric for HPC software development time productivity, defined as the ratio of relative runtime performance to relative programmer effort. This formula has...

Examples are advances in ground moving target indicator (GMTI) processing, space-time adaptive processing (STAP), target discrimination, and electronic counter-countermeasures (ECCM). All these advances have improved the capabilities of radar sensors. Major improvements expected in the next several years will come from exploiting collaborative network-centric architectures to leverage synergies among individual...