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.

Efficient mapping reductions using iso-planes on the polytope model

Abstract: This paper presents a new technique for mapping algorithms onto regular (systolic) arrays. The technique integrates the associativity and commutativity of computations into space-time transformations on the polytope model and involves three categories of transformations: ( 1) iso-planes - forming iso-planes of computations for algorithm representation in contrast to the conventional technique using the data dependence graph; ( 2) increase in dimensionality -mapping a low dimensional algorithm representation into a higher dimensional version with a higher degree of parallelism; and (3) pipestructures - generating and choosing a particular partial order of computations on iso-planes for moving data around the regular array. Three operations for generating pipestructures are introduced: permutation, rotation and reversal. The method presented here increases the available degree of parallelism and thus improves the time complexity of systolic computations. Examples for developing 2-D arrays for 1-D c...

Towards a streaming model for nested data parallelism

Abstract: The language-integrated cost semantics for nested data parallelism pioneered by NESL provides an intuitive, high-level model for predicting performance and scalability of parallel algorithms with reasonable accuracy. However, this predictability, obtained through a uniform, parallelism-flattening execution strategy, comes at the price of potentially prohibitive space usage in the common case of computations with an excess of available parallelism, such as dense-matrix multiplication.We present a simple nested data-parallel functional language and associated cost semantics that retains NESL's intuitive work--depth model for time complexity, but also allows highly parallel computations to be expressed in a space-efficient way, in the sense that memory usage on a single (or a few) processors is of the same order as for a sequential formulation of the algorithm, and in general scales smoothly with the actually realized degree of parallelism, not the potential parallelism.The refined semantics is based on distinguishing formally between fully materialized (i.e., explicitly allocated in memory all at once) "vectors" and potentially ephemeral "sequences" of values, with the latter being bulk-processable in a streaming fashion. This semantics is directly compatible with previously proposed piecewise execution models for nested data parallelism, but allows the expected space usage to be reasoned about directly at the source-language level.The language definition and implementation are still very much work in progress, but we do present some preliminary examples and timings, suggesting that the streaming model has practical potential.

A parallel algorithm for text inference

Abstract: In this paper, we describe a highly parallel method for extracting inferences from text. The method is based on a marker-propagation algorithm that establishes semantic paths between knowledge base concepts. The paper presents the structure of the system, the marker-propagation algorithm, and results that show a large degree of parallelism.

Cluster-driven hardware/software partitioning and scheduling approach for a reconfigurable computer system

Abstract: To achieve a good performance when implementing applications in codesign systems, partitioning and scheduling are important steps In this paper, a two-phase clustering algorithm is introduced as a preprocessing step to an existing hardware/software partitioning and scheduling system This preprocessing step increases the granularity in the partition design, resulting in a higher degree of parallelism and a better mapping to the reconfigurable resource This cluster-driven approach shows improvements in both the makespan of the implementation, and the CPU runtime

Policy-Aware Service Composition: Predicting Parallel Execution Performance of Composite Services

Abstract: With the increasing volume of data to be analysed, one of the challenges in Service Oriented Architecture (SOA) is to make web services efficient in processing large-scale data. Parallel execution and cloud technologies are the keys to speed-up the service invocation. In SOA, service providers typically employ policies to limit parallel execution of the services based on arbitrary decisions. In order to attain optimal performance improvement, users need to adapt to the services policies. A composite service is a combination of several atomic services provided by various providers. To use parallel execution for greater composite service efficiency, the degree of parallelism (DOP) of the composite services need to be optimized by considering the policies of all atomic services. We propose a model that embeds service policies into formulae to calculate composite service performance. From the calculation, we predict the optimal DOP for the composite service, where it attains the best performance. Extensive experiments are conducted on real-world translation services. We use several measures such as mean prediction error (MPE), mean absolute deviation (MAD) and tracking signal (TS) to evaluate our model. The analysis results show that our proposed model has good prediction accuracy in identifying optimal DOPs for composite services.