Degree of parallelism

About: Degree of parallelism is a research topic. Over the lifetime, 1515 publications have been published within this topic receiving 25546 citations.

On the energy efficiency of synchronization primitives for shared-memory single-chip multiprocessors

Abstract: Applications running on Multiprocessor Systems-on-Chips (MP-SoCs) exhibit complex interaction patterns, resulting in significant amounts of time spent while synchronizing for mutually exclusive access to shared resources. Such an overhead is expected to increase with the degree of parallelism and with the mutual correlation of concurrent tasks, thus becoming in a severe obstacle to the full exploitation of a system potential. Although the topic has been extensively studied in the literature, in MPSoC architectures, which exhibit different tradeoffs with respect to traditional multi-processors, the available results may not be valid or hold only partially. Furthermore, the strict energy budget of MPSoCs requires also the evaluation of the energy efficiency of such synchronization primitives. In this work we survey various state-of-the-art implementations of synchronization primitives, in order to assess their impact on performance and on energy consumption. The results of our analysis show that some commonly accepted intuitions in the multiprocessor domain do not hold in the context of MPSoCs.

Experiments in Solving Mixed Integer Programming Problems on a Small Array of Transputers

Abstract: The time-consuming process of solving large-scale Mixed Integer Programming problems using the branch-and-bound technique can be speeded up by introducing a degree of parallelism into the basic algorithm. This paper describes the development and implementation of a parallel branch-and-bound algorithm created by adapting a commercial MIP solver. Inherent in the design of this software are certain ad hoc methods, the use of which are necessary in the effective solution of real problems. The extent to which these ad hoc methods can successfully be transferred to a parallel environment, in this case an array of at most nine transputers, is discussed. Computational results on a variety of real integer programming problems are reported.

A compile time partitioning method for DOALL loops on distributed memory systems

Abstract: The loop partitioning problem on modern distributed memory systems is no longer fully communication bound primarily due to a significantly lower ratio of communication/computation speeds. The useful parallelism may be exploited on these systems to an extent that the communication balances the parallelism and does not produce a very high overhead to nullify all the gains due to the parallelism. We describe a compile time partitioning and scheduling approach based on the above motivation for DOALL loops where communication without data replication is inevitable. First, the code partitioning phase analyzes the references in the body of the DOALL loop nest and determines a set of directions for reducing a larger degree of communication by trading a lesser degree of parallelism. Next, the data distribution phase uses a new larger partition owns rule to achieve computation and communication load balance. The granularity adjustment phase attempts to further eliminate communication through merging partitions to reduce the completion time. Finally, the load balancing phase attempts to reduce the number of processors without degrading the completion time and the mapping phase schedules the partitions on available processors. Relevant theory and algorithms are developed along with a performance evaluation on Cray T3D.

Scientific Computing on the Grid

Abstract: Computer simulations are becoming increasingly important as the only means for studying and interpreting the complex processes of nature. Yet the scope and accuracy of these simulations are severely limited by available computational power, even using today's most powerful supercomputers. As we endeavor to simulate the true complexity of nature, we will require much larger scale calculations than are possible at present. Such dynamic and large scale applications will require computational grids and grids require development of new latency tolerant algorithms, and sophisticated code frameworks like Cactus to carry out more complex and high fidelity simulations with a massive degree of parallelism.

Parallel Distributed Genetic Programming Applied to the Evolution of Natural Language Recognisers

Abstract: This paper describes an application of Parallel Distributed Genetic Programming (PDGP) to the problem of inducing recognisers for natural language from positive and negative examples. PDGP is a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an efficient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. PDGP allows the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, finite state automata, Recursive Transition Networks (RTNs), etc. The paper describes the representations, the operators and the interpreters used in PDGP, and describes how these can be tailored to evolve RTN-based recognisers.