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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.


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01 Jan 2000
TL;DR: The purpose of this paper is to introduce AMR++ as an object- oriented library which forms a part of the OVERTURE framework, a much larger object-oriented numerical framework developed and supported at Los Alamos National Laboratory and distributed on the Web for the last several years.
Abstract: Adaptive mesh refinement computations are complicated by their dynamic nature. In the serial environment they require substantial infrastructures to support the regridding processes, intergrid operations, and local bookkeeping of positions of grids relative to one another. In the parallel environment the dynamic behavior is more problematic because it requires dynamic distribution support and load balancing. Parallel AMR is further complicated by the substantial task parallelism, in addition to the obvious data parallelism, this task parallelism requires additional infrastructure to support efficiently [6]. The degree of parallelism is typically dependent upon the algorithms in use and the equations being solved. Different algorithms have significant compromises between computation and communication. Substantial research work is often required to define efficient methods and suitable infrastructure. The purpose of this paper is to introduce AMR++ as an object-oriented library which forms a part of the OVERTURE framework, a much larger object-oriented numerical framework developed and supported at Los Alamos National Laboratory and distributed on the Web for the last several years.

6 citations

Book ChapterDOI
11 Dec 2007
TL;DR: This paper presents a technique, called ParaSolve, that exploits the sparsity structure of the web graph matrix to improve on the degree of parallelism in a number of distributed approaches for computating PageRank.
Abstract: This paper presents a technique we call ParaSolve that exploits the sparsity structure of the web graph matrix to improve on the degree of parallelism in a number of distributed approaches for computating PageRank. Specifically, a typical algorithm (such as power iteration or GMRES) for solving the linear system corresponding to PageRank, call it LinearSolve, may be converted to a distributed algorithm, Distrib( LinearSolve), by partitioning the problem and applying a standard technique (i.e., Distrib). By reducing the number of inter-partition multiplications, we may greatly increase the degree of parallelism, while achieving a similar degree of accuracy. This should lead to increasingly better performance as we utilize more processors. For example, using GeoSolve (a variant of Jacobi iteration) as our linear solver and the 2001 web graph from Stanford's WebBase project, on 12 processors Para-Solve(GeoSolve) outperforms Distrib(GeoSolve) by a factor of 1.4, while on 32 processors the performance ratio improves to 2.8.

6 citations

Proceedings ArticleDOI
27 Jun 2005
TL;DR: A new scheduling algorithm is introduced, which is based on using an objective function to guide the search for a near optimal solution, which includes different criteria such as real-time deadlines, reliability, and quantitative measures of the communication, degree of parallelism and processing power fragmentation.
Abstract: Improper scheduling of real-time applications on a cluster may lead to missing required deadlines and offset the gain of using the system and software parallelism. Most existing scheduling algorithms do not consider factors such as real-time deadlines, system reliability, processing power fragmentation, inter-task communication and degree of parallelism on performance. In this paper we introduce a new scheduling algorithm, which is based on using an objective function to guide the search for a near optimal solution. This objective function includes different criteria such as real-time deadlines, reliability, and quantitative measures of the communication, degree of parallelism and processing power fragmentation. The presence of different criteria may affect the overall acceptance rate of the applications. We also investigate the effect of reliability on the overall acceptance rate.

6 citations

Proceedings ArticleDOI
27 Oct 2010
TL;DR: This paper dynamically restricts the number of active threads within a parallel region without violating the OpenMP specification.
Abstract: While OpenMP conceptually allows to vary the degree of parallelism from one parallel region to the next in order to adapt to the system load, this might still be too coarse-grained in certain scenarios. Especially applications designed for parallelism may stay within one parallel region for a long time. This may lead either to an oversubscribed system where individual applications are not restricted in their degree of parallelism, or to an underutilized system, because individual applications are restricted to a too small degree of parallelism. In this paper, we tackle both problems by dynamically restricting the number of active threads within a parallel region without violating the OpenMP specification.

6 citations

DOI
01 Jan 2017
TL;DR: The objective of this Thesis is to provide a complete framework to analyze, evaluate and refactor DDF applications expressed using the RVC-CAL language, which relies on a systematic design space exploration (DSE) examining different design alternatives to optimize the chosen objective function while satisfying the constraints.
Abstract: The limitations of clock frequency and power dissipation of deep sub-micron CMOS technology have led to the development of massively parallel computing platforms. They consist of dozens or hundreds of processing units and offer a high degree of parallelism. Taking advantage of that parallelism and transforming it into high program performances requires the usage of appropriate parallel programming models and paradigms. Currently, a common practice is to develop parallel applications using methods evolving directly from sequential programming models. However, they lack the abstractions to properly express the concurrency of the processes. An alternative approach is to implement dataflow applications, where the algorithms are described in terms of streams and operators thus their parallelism is directly exposed. Since algorithms are described in an abstract way, they can be easily ported to different types of platforms. Several dataflow models of computation (MoCs) have been formalized so far. They differ in terms of their expressiveness (ability to handle dynamic behavior) and complexity of analysis. So far, most of the research efforts have focused on the simpler cases of static dataflow MoCs, where many analyses are possible at compile-time and several optimization problems are greatly simplified. At the same time, for the most expressive and the most difficult to analyze dynamic dataflow (DDF), there is still a dearth of tools supporting a systematic and automated analysis minimizing the programming efforts of the designer. The objective of this Thesis is to provide a complete framework to analyze, evaluate and refactor DDF applications expressed using the RVC-CAL language. The methodology relies on a systematic design space exploration (DSE) examining different design alternatives in order to optimize the chosen objective function while satisfying the constraints. The research contributions start from a rigorous DSE problem formulation. This provides a basis for the definition of a complete and novel analysis methodology enabling systematic performance improvements of DDF applications. Different stages of the methodology include exploration heuristics, performance estimation and identification of refactoring directions. All of the stages are implemented as appropriate software tools. The contributions are substantiated by several experiments performed with complex dynamic applications on different types of physical platforms.

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
202147
202048
201952
201870
201775