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About 10,000 papers for “Parallel computing”

Pervasive parallel computing

Andrew A. Chien · 2007

Parallel programming has been the subject of deep research for decades -- and renowned in the software community as a difficult challenge to the degree that many companies have teams of parallelism and concurrency experts. Further, many ISV's explicitly design their software architectures so as to ensure that the majority of the development effort, including of course debug and test, can be done without consideration of parallelism. What makes parallelism so difficult, are the knotty and coupled problems of correctness, performance -- particularly data locality, and software modularity.

Cited by 4

parallel: A command for parallel computing

George G. Vega Yon, Brian Quistorff · The Stata Journal Promoting communications on statistics and Stata · 2019

The parallel package allows parallel processing of tasks that are not interdependent. This allows all flavors of Stata to take advantage of multiprocessor machines. Even Stata/MP users can benefit because many community-contributed programs are not automatically parallelized but could be under our framework.

Cited by 17Open access

Parallel computing Using ECLIPSE Parallel Contributors:

Paul A. Crumpton, Principal Software Engineer, Paul A. Fjerstad et al. · 2008

This paper documents that model simulations up to 10 million cells can be performed using parallel computing, allowing geologists and reservoir engineers to include more realistic geological detail for improved, more reliable production optimization. The parallelization of the simulator’s linear solver is critical to achieve calculation speed as more processors are used for a given model. Various approaches for solving the solution matrix in conjunction with various coloring and partitioning strategies are discussed, along with computing issues that arise when applying parallel processing to complex reservoir simulation models.

Cited by 0

Parallel computing in India

David K. Kahaner · IEEE Parallel & Distributed Technology Systems & Applications · 1996

India has made significant strides in the development of high performance parallel computers. The author summarizes the status of some parallel computing projects initiated at the Center for Development of Advanced Computing in India, and assesses the viability of these efforts both in India and as part of the worldwide picture of advanced computing products.

Cited by 1

Edinburgh Parallel Computing Centre,

Rochan Upadhyay, Gavin Pringle, George Beckett et al. · 2010

ABSTRACT: FireGrid is a modern concept that aims to leverage a number of modern technologies to aid fire emergency response. In this paper we provide a brief introduction to the FireGrid project. A number of different technologies such as wireless sensor networks, grid-enabled High Performance Computing (HPC) implementation of fire models, and artificial intelligence tools need to be integrated to build up a modern fire emergency response system. We propose a system architecture that provides the framework for integration of the various technologies. We describe the components of the generic FireGrid system architecture in detail. Finally we present a small-scale demonstration experiment which has been completed to highlight the concept and application of the FireGrid system to an actual fire. Although our proposed system architecture provides a versatile framework for integration, a number of new and interesting research problems need to be solved before actual deployment of the system. We outline some of the challenges involved which require significant interdisciplinary collaborations.

Cited by 0

Parallel Computing with Dask

Hayek, Wolfgang · Figshare · 2020

Parallel computing has become a necessity for a wide range of modern scientific computing problems, including data-oriented computing at large scale to achieve reasonable processing times. Implementing parallel computing can be challenging and time-consuming - APIs such as the Message Passing Interface (MPI) are powerful but can be hard to learn and implement. <br><br>Dask is a popular toolkit for the Python programming language that addresses this issue. While requiring very little programming effort, it offers a variety of parallelisation paradigms, including work sharing via parallel function evaluation, task graphs, and direct integration with packages such as NumPy, Pandas, and Scikit-Learn. Dask can be used interactively and as a batch processing tool. The Dask-MPI package adds MPI as a parallelisation backend, enabling scalability and high throughput on high-performance computing (HPC) systems. <br><br>In this presentation, I will introduce Dask, discuss some of its parallelisation mechanisms, and demonstrate how to use the MPI backend for batch processing. <br><br>[Note: This presentation should precede Maxime Rio’s demo of using Dask with SciKit-learn in Jupyter notebooks as it will cover off the basics of Dask.] <br><b>ABOUT THE AUTHOR(S)</b>Wolfgang Hayek is a research software engineer at NeSI and NIWA, and group manager of NIWA’s scientific programming group, with many years of experience in scientific computing and HPC. <b><br></b><br>Maxime is a data scientist at NeSI and NIWA. He enjoys helping researchers to analyse their data, from visualisation to probabilistic modelling. <br><br>Chris Scott is a Research Software Engineer at NeSI with a background in scientific computing and HPC.<br>

Cited by 0Open access

Distributed and Parallel Computing

Monir H. Sharker, Hassan A. Karimi · Big Data · 2024

Geoinformatics researchers and practitioners have been facing challenges in geospatial data processing ever since the debut of digital mapping, the predecessor term for today’s geospatial information systems. Over time, due to the advancement in data collection technologies (such as satellite imagery and Global Positioning System (GPS), challenges of geospatial data processing have become widespread. Today, every individual or organization that either routinely or sporadically must handle geospatial data has a better understanding and much appreciation for the complex nature of geospatial data processing. Of several characteristics of geospatial phenomena that make geospatial data complex for processing, data-intensive computing, one of the main characteristics of geospatial big data that can be addressed by employing special computing hardware and advanced computing techniques, is discussed in this chapter. To that end, since geospatial big data is defined and different approaches for addressing challenges of this emergent paradigm are discussed in all other chapters of this book, in this chapter high-performance computing (HPC), commonly considered as one main approach for handling data-intensive problems, is focused.

Cited by 0

Trends in Parallel Computing

Oliver A. McBryan · 1990

Abstract : A general consensus is developing that highly parallel computers are the only practical near-term route to the teraflops of computing power required by many scientific and engineering problems. While current conventional supercomputers already utilize low parallelism, highly parallel computers are still in the experimental stage, although they are increasingly visible at the front line of grand challenge computations. While it has been clearly demonstrated that such systems can be built, and are reliable, there has been only limited success with developing appropriate user-level software. The authors survey recent and upcoming developments in the area of parallel supercomputing, focusing on the underlying trends that have proved successful. The paper begins with discussions of how parallel computers are classified; recent or current parallel computer projects worldwide; node design, including the individual computational processor, along with its associated communications hardware and local memory; interprocessor communication facilities; and software for currently available parallel computers. The next section examines in more depth the parallel systems that are currently in development: INTEL iPSC, CMU iWarp, NCUBE2, GF-11, Connection Machine CM-1, AMT DAP, MasPar MP-1, Myrias SPS-2, and the SUPRENUM. The final section discusses the likely next-generation computing environment: a heterogeneous ensemble of scalar, vector, parallel, and graphics computers along with disk farms and network interfaces to users.

Cited by 2