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.

Sampling from a Gibbs Measure with Pair Interaction by Means of PCA

Abstract: We consider the problem of approximate sampling from the finite volume Gibbs measure with a general pair interaction. We exhibit a parallel dynamics (Probabilistic Cellular Automaton) which efficiently implements the sampling. In this dynamics the product measure that gives the new configuration in each site contains a term that tends to favour the original value of each spin. This is the main ingredient that allows one to prove that the stationary distribution of the PCA is close in total variation to the Gibbs measure. The presence of the parameter that drives the “inertial” term mentioned above gives the possibility to control the degree of parallelism of the numerical implementation of the dynamics.

Data transport in Wang's partition method

Abstract: The partition method of Wang, for the solution of tridiagonal linear systems, is analysed with regard to data transport between the processors of a parallel (local memory) computer. We propose a modification which reduces the amount of data transport considerably, without affecting the computational complexity and which has about the same degree of parallelism as the original version. We will also discuss the effects of this modification to a generalized version for banded systems. The parallel solution of a bidiagonal system is considered.

Efficient data layout, scheduling and playout control in MARS

Abstract: Large-scale on-demand multimedia servers thatcan provide independent and interactive access to a vastamount of multimedia information to a large number ofconcurrent clients will be required for a widespread deployment of exciting multimedia applications. Our project, calledMassively-parallel And Real-time Storage (MARS) is aimedat prototype development of such a large-scale server. Thispaper primarily focuses on the distributed data layout andscheduling techniques developed as a part of this project.These techniques support a high degree of parallelism andconcurrency, and efficiently implement various playout control operations, such as fast forward, rewind, pause, resume,frame advance and random access.

A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems

Abstract: This paper presents a new efficient multi-objective evolutionary algorithm for solving computationally-intensive optimization problems. To support a high degree of parallelism, the algorithm is based on a steady-state design. For improved efficiency the algorithm utilizes a surrogate to identify promising candidate solutions and filter out poor ones. To handle the uncertainties associated with the approximative surrogate evaluations, a new method for multi-objective optimization is described which is generally applicable to all surrogate techniques. In this method, basically, surrogate objective values assigned to offspring are adjusted to consider the error of the surrogate. The algorithm is evaluated on the ZDT benchmark functions and on a real-world problem of manufacturing optimization. In assessing the performance of the algorithm, a new performance metric is suggested that combines convergence and diversity into one single measure. Results from both the benchmark experiments and the real-world test case indicate the potential of the proposed algorithm.

Identifying energy-efficient concurrency levels using machine learning

Abstract: Multicore microprocessors have been largely motivated by the diminishing returns in performance and the increased power consumption of single-threaded ILP microprocessors. With the industry already shifting from multicore to many-core microprocessors, software developers must extract more thread-level parallelism from applications. Unfortunately, low power-efficiency and diminishing returns in performance remain major obstacles with many cores. Poor interaction between software and hardware, and bottlenecks in shared hardware structures often prevent scaling to many cores, even in applications where a high degree of parallelism is potentially available. In some cases, throwing additional cores at a problem may actually harm performance and increase power consumption. Better use of otherwise limitedly beneficial cores by software components such as hypervisors and operating systems can improve system-wide performance and reliability, even in cases where power consumption is not a main concern. In response to these observations, we evaluate an approach to throttle concurrency in parallel programs dynamically. We throttle concurrency to levels with higher predicted efficiency from both performance and energy standpoints, and we do so via machine learning, specifically artificial neural networks (ANNs). One advantage of using ANNs over similar techniques previously explored is that the training phase is greatly simplified, thereby reducing the burden on the end user. Using machine learning in the context of concurrency throttling is novel. We show that ANNs are effective for identifying energy-efficient concurrency levels in multithreaded scientific applications, and we do so using physical experimentation on a state-of-the-art quad-core Xeon platform.