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About 10,000 papers for “Parallel processing”

Parallel Processing with TreeClust

I Taylor McKechnie · Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · 2017

Clustering data is one of the most common statistical and machine learning techniques for analyzing big data. Clustering can be particularly difficult when the data sets include categorical, missing, or noise variables. The tree clustering algorithm developed by Samuel Buttrey and Lyn Whitaker, as described in the December 2015 issue of The R Journal, seems to provide a solution to these problems, but it requires a large set of overhead computations. This issue is intensified when working with high-dimensional data because the extent of treeClust’s overhead computations are based on the dimensions of the data. High performance computing (HPC) and parallel processing present a solution to this overhead computation burden, but treeClust’s existing parallel processing method does not work on the Naval Postgraduate School’s HPC, the Hamming Supercomputer (HSC). Furthermore, correctly determining what HPC resources to use can be a difficult task. In this thesis, we present a new HSC-specific method for parallel processing data using the treeClust R package developed by Buttrey and Whitaker. Based on the results of our experiments, our method approximates the optimal resource HPC request, so that users realize the best run time when using treeClust on the HSC.

Cited by 0

Parallel graph processing on graphics processing units

Jianlong Zhong · 2014

users, and throughput is an important metric for performance and total ownership cost. Particularly, we propose Kernelet as the runtime back end system to optimize the throughput of concurrent kernel executions on the GPU. Kernelet embraces transparent memory and PCI-e data transfer management techniques, as well as dynamic slicing and scheduling techniques to support efficient concurrent kernel executions. With slicing, Kernelet divides a GPU kernel into multiple sub-kernels (namely slices). Each slice has tunable occupancy to allow co-scheduling with other slices for high GPU utilization. We develop a novel Markov chain-based performance model to guide the scheduling decision. Our experimental results demonstrate up to 31% and 23% performance improvement on NVIDIA Tesla C2050 and GTX680 GPUs for our benchmark kernels, respectively. Kernelet improves the throughput of concurrent Medusa applications by 29% on C2050.

Cited by 0Open access

Autonomous processing in parallel distributed processing networks

Michael R. W. Dawson, Don Schopflocher · Philosophical Psychology · 1992

Abstract This paper critically examines the claim that parallel distributed processing (PDP) networks are autonomous learning systems. A PDP model of a simple distributed associative memory is considered. It is shown that the ‘generic’ PDP architecture cannot implement the computations required by this memory system without the aid of external control. In other words, the model is not autonomous. Two specific problems are highlighted: (i) simultaneous learning and recall are not permitted to occur as would be required of an autonomous system; (ii) connections between processing units cannot simultaneously represent current and previous network activation as would be required if learning is to occur. Similar problems exist for more sophisticated networks constructed from the generic PDP architecture. We argue that this is because these models are not adequately constrained by the properties of the functional architecture assumed by PDP modelers. It is also argued that without such constraints, PDP researchers cannot claim to have developed an architecture radically different from that proposed by the Classical approach in cognitive science.

Cited by 37

PARALLEL PROCESSING MODELS FOR IMAGE PROCESSING PROBLEMS

Shaharuddin Salleh, Bahrom Sanugi · 2002

This report is the compilation of our research work under IRPA Vot no. 72179. The work consists of four main problems of study. First, we look at the stochastic task scheduling problem using the reconfigurable mesh network as the computing platform. Through our model called the Dynamic Simulator on Reconfigurable Mesh (DSRM) which maps a randomly generated number of tasks onto the processors at discrete time, some reasonably good load balancing results were obtained. The second problem is the application of the first model in the edge detection problem using the Laplacian convolution method on the same parallel computing network. In the third problem, we extend the scope to include a strategy for the single-row routing of pins and vias in VLSI design, using our model called the Enhanced Simulated annealing for Single-row Routing (ESSR). This model is the parallel implementation of the simulated annealing method, and it generates optimum solutions to the problem. The fourth problem is the extension of the single-row routing problem, where a model has been developed to transform a complete graph into its single-row representation. This last problem has some significant contributions in applications such as scheduling and channel assignment problem in cellular telephone systems.

Cited by 0

Quantum Parallel Processing Framework for Image Processing Applications

Juan Carlos Salcedo, Khaled Ahmed · 2025

Quantum computing stands at a transformative threshold, with error-correcting quantum processors expected to emerge within the next five years. This technological advancement will be crucial for maintaining competitive advantage in fields such as cryptography, optimization, molecular medicine, and image processing, with mainstream quantum computing projected by 2030. This study introduces a novel Quantum Parallel Framework (QPF) designed to prepare for the integration of quantum computing in industrial applications. The research focuses on developing and implementing parallel quantum algorithms for practical commercial applications, particularly in image processing. The QPF provides a comprehensive methodology for organizations to build expertise in quantum computing integration with classical systems. It includes a Qiskit interface for managing quantum algorithms and communications with Quantum Processing Units (QPUs), along with a C++ and OpenGL-based front end. The framework's capabilities are demonstrated through a case study implementing a Quantum Hadamard Edge Detector (QHED) in parallel execution. The study evaluates various performance parameters, including QHED threads, processors, Non-Uniform Memory Allocation (NUMA) strategy, thread priority, and image resolution, to optimize processing efficiency. The framework provides a practical pathway for organizations to develop and mature their quantum computing capabilities, focusing on real-world applications and system integration. This work contributes to the growing field of quantum computing by offering a methodology that can be adapted for various quantum algorithms and applications, particularly in preparation for more powerful self-error correcting QPUs.

Cited by 0

Application of parallel processing in natural language processing

Peng Xu · DR-NTU (Nanyang Technological University) · 1998

Natural Language Processing (NLP)is an important research direction, since it addresses the needs of building a next-generation multi-language-processing tool for fast information exchange and acquisition. Machine Translation is an important branch in NLP. However, approaches used in NLP suffer the drawbacks of requiring huge computational resources, both in terms of computation time and memory. Therefore, applying parallelization techniques to NLP would be a valuable attempt in both research fields (that is, parallel processing and natural language processing).

Cited by 0

Bit-serial RISC processing element for parallel processing

Faisal A Haidar · OakTrust (Texas A&M University Libraries) · 1994

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.

Cited by 0

Optical, processing-less massively parallel processing interconnection networks

E. Schenfeld, John H. Reif, S.H. Lee et al. · 1994

The authors attempt to form a coherent picture that may suggest possible future directions of using the benefits optics has to offer, yet avoiding its drawbacks for optical interconnection networks. Possible questions addressed include: What is the nature of (some) parallel applications that may be used to avoid optical processing in the network? How can reconfiguration be used in an efficient way to permit using processing-less optical communication? What are the devices issues that need to be addressed? What opto-electronic devices can be used? What is the expected performance of such devices (density, bandwidth)? What are the connection needs to the electronic processing elements? What are the aspects of packaging? Will a distributed layout be better than a centralized one (i.e., using one central device vs. multiple devices)? How will such a reconfigurable system be used in a real environment (multi-users, partitioning of jobs, distribution of global data)? What applications can benefit from such an architecture and how general purpose can it be (business applications: databases, transaction processing; scientific applications; engineering applications; future applications)?.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 0

Skew in Parallel Query Processing

Paul Beame, Paraschos Koutris, Dan Suciu · arXiv (Cornell University) · 2014

We study the problem of computing a conjunctive query q in parallel, using p of servers, on a large database. We consider algorithms with one round of communication, and study the complexity of the communication. We are especially interested in the case where the data is skewed, which is a major challenge for scalable parallel query processing. We establish a tight connection between the fractional edge packings of the query and the amount of communication, in two cases. First, in the case when the only statistics on the database are the cardinalities of the input relations, and the data is skew-free, we provide matching upper and lower bounds (up to a poly log p factor) expressed in terms of fractional edge packings of the query q. Second, in the case when the relations are skewed and the heavy hitters and their frequencies are known, we provide upper and lower bounds (up to a poly log p factor) expressed in terms of packings of residual queries obtained by specializing the query to a heavy hitter. All our lower bounds are expressed in the strongest form, as number of bits needed to be communicated between processors with unlimited computational power. Our results generalizes some prior results on uniform databases (where each relation is a matching) [4], and other lower bounds for the MapReduce model [1].

Cited by 11Open access

Parallel processing for scientific computations

Hasan S. Alkhatib · NASA Technical Reports Server (NASA) · 1991

The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience.

Cited by 2Open access