2021년 2월 17일 (수) 08:06에 Pythagoras0님의 편집

2021-02-17T08:06:05Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:16:24Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T11:02:43Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q232661 Q232661]
===말뭉치===
# Parallel computing has long played a vital role in addressing the performance demands of high-end engineering and scientific applications.<ref name="ref_8e516425">[https://www.clear.rice.edu/comp422/ 534 Parallel Computing: Home Page]</ref>
# Over the last decade, parallel computing has become important to a much broader audience as the regular increases in clock speed that previously fueled performance increases became infeasible.<ref name="ref_8e516425" />
# Almost all increases in the performance of recent processors comes from more parallelism rather than increases in clock speed.<ref name="ref_8e516425" />
# Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.<ref name="ref_de1ba16e">[https://www.cambridge.org/core/journals/communications-in-computational-physics/article/survey-on-parallel-computing-and-its-applications-in-dataparallel-problems-using-gpu-architectures/879D964A36478175DEED99FB00C8D811 A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures]</ref>
# The evolution of computer architectures (multi-core and many-core) towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm.<ref name="ref_de1ba16e" />
# In the last decade, the graphics processing unit, or GPU, has gained an important place in the field of high performance computing (HPC) because of its low cost and massive parallel processing power.<ref name="ref_de1ba16e" />
# In this paper, we survey the concept of parallel computing and especially GPU computing.<ref name="ref_de1ba16e" />
# Definition - What does Parallel Computing mean?<ref name="ref_483fb7cf">[https://www.techopedia.com/definition/8777/parallel-computing What is Parallel Computing?]</ref>
# Parallel computing is a form of computation in which two or more processors are used to solve a problem or task.<ref name="ref_d06ded77">[https://www.teldat.com/blog/en/parallel-computing-bit-instruction-task-level-parallelism-multicore-computers/ Parallel computing allows increases processing frequency]</ref>
# It’s mainly for this reason that parallel computing became established as the dominant paradigm.<ref name="ref_d06ded77" />
# Instruction-level parallelism : a program’s instructions are reordered and grouped for parallel execution.<ref name="ref_d06ded77" />
# Parallel computing is defined as ‘Involving the concurrent or simultaneous performance of certain operations’.<ref name="ref_c1c0315f">[https://www.futurelearn.com/info/courses/supercomputing/0/steps/24033 Parallel Computing]</ref>
# Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems.<ref name="ref_50b1145c">[https://www.scimagojr.com/journalsearch.php?q=26138&tip=sid Parallel Computing]</ref>
# If parallel computing has a central tenet, that might be it.<ref name="ref_bf81d7a4">[https://builtin.com/hardware/parallel-processing-example 9 Parallel Processing Examples & Applications]</ref>
# Parallel Processing Applications Parallel processing refers to the speeding up a computational task by dividing it into smaller jobs across multiple processors.<ref name="ref_bf81d7a4" />
# When you tap the Weather Channel app on your phone to check the day’s forecast, thank parallel processing.<ref name="ref_bf81d7a4" />
# Parallel computing is the backbone of other scientific studies, too, including astrophysic simulations, seismic surveying, quantum chromodynamics and more.<ref name="ref_bf81d7a4" />
# Increasingly, parallel processing is being seen as the only cost-effective method for the fast solution of computationally large and data-intensive problems.<ref name="ref_c1f239e6">[https://dl.acm.org/citation.cfm?id=600009 Introduction to Parallel Computing]</ref>
# The Wolfram Language provides a uniquely integrated and automated environment for parallel computing.<ref name="ref_ae84d7db">[https://reference.wolfram.com/language/guide/ParallelComputing.html Parallel Computing—Wolfram Language Documentation]</ref>
# The popularization and evolution of parallel computing in the 21st century came in response to processor frequency scaling hitting the power wall.<ref name="ref_fd6f81f2">[https://www.omnisci.com/technical-glossary/parallel-computing What is Parallel Computing? Definition and FAQs]</ref>
# The importance of parallel computing continues to grow with the increasing usage of multicore processors and GPUs.<ref name="ref_fd6f81f2" />
# Parallel computer architecture exists in a wide variety of parallel computers, classified according to the level at which the hardware supports parallelism.<ref name="ref_fd6f81f2" />
# There is much overlap in distributed and parallel computing and the terms are sometimes used interchangeably.<ref name="ref_fd6f81f2" />
# Bit-level parallelism: It is the form of parallel computing which is based on the increasing processor’s size.<ref name="ref_ce7f3f00">[https://www.geeksforgeeks.org/introduction-to-parallel-computing/ Introduction to Parallel Computing]</ref>
# Instruction-level parallelism: A processor can only address less than one instruction for each clock cycle phase.<ref name="ref_ce7f3f00" />
# This is called instruction-level parallelism.<ref name="ref_ce7f3f00" />
# Task parallelism employs the decomposition of a task into subtasks and then allocating each of the subtasks for execution.<ref name="ref_ce7f3f00" />
# As stated above, there are two ways to achieve parallelism in computing.<ref name="ref_2d21c59b">[https://www2.cisl.ucar.edu/user-support/parallel-computing-concepts Parallel computing concepts]</ref>
# most programmers have little or no experience with parallel computing, and there are few parallel programs to use off-the shelf or even good examples to copy from.<ref name="ref_b826f13d">[https://web.eecs.umich.edu/~qstout/parallel.html Parallel Computing: Overview, Definitions, Examples and Explanations]</ref>
# Hence people often have to reinvent the parallel wheel (see "Parallelism needs Classes for the Masses").<ref name="ref_b826f13d" />
# Yet other computers exploit graphics processor units, GPUs, to achieve parallelism.<ref name="ref_b826f13d" />
# Overall, this means that there is a massive need to make use of the parallelism in multi-core chips for almost any problem, and to use many of these combined together for large problems.<ref name="ref_b826f13d" />
# If you’re at all involved in tech, chances are you’ve heard about parallel computing.<ref name="ref_6447d3fb">[https://store.hp.com/us/en/tech-takes/parallel-computing-and-its-modern-uses Parallel Computing and Its Modern Uses]</ref>
# Parallel computing uses multiple computer cores to attack several operations at once.<ref name="ref_6447d3fb" />
# Without parallel computing, performing digital tasks would be tedious, to say the least.<ref name="ref_6447d3fb" />
# The advantages of parallel computing are that computers can execute code more efficiently, which can save time and money by sorting through “big data” faster than ever.<ref name="ref_6447d3fb" />
# It is intended to provide only a brief overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it.<ref name="ref_881be057">[https://hpc.llnl.gov/training/tutorials/introduction-parallel-computing-tutorial Introduction to Parallel Computing Tutorial]</ref>
# The tutorial begins with a discussion on parallel computing - what it is and how it's used, followed by a discussion on concepts and terminology associated with parallel computing.<ref name="ref_881be057" />
# It soon becomes obvious that there are limits to the scalability of parallelism.<ref name="ref_881be057" />
# Can be very easy and simple to use - provides for "incremental parallelism".<ref name="ref_881be057" />
# Parallel computing, on the other hand, uses multiple processing elements simultaneously to solve a problem.<ref name="ref_62895f24">[https://en.wikipedia.org/wiki/Parallel_computing Parallel computing]</ref>
# Multi-core processors have brought parallel computing to desktop computers.<ref name="ref_62895f24" />
# Without instruction-level parallelism, a processor can only issue less than one instruction per clock cycle (IPC < 1).<ref name="ref_62895f24" />
# This is known as instruction-level parallelism.<ref name="ref_62895f24" />
# In this introductory chapter some views regarding state-of-the-art and trends in parallelism are given, accompanied by a summary of individual chapters.<ref name="ref_6af47195">[https://link.springer.com/chapter/10.1007/978-1-84882-409-6_1 Overview – Parallel Computing: Numerics, Applications, and Trends]</ref>
# These days, many computational libraries have built-in parallelism that can be used behind the scenes.<ref name="ref_1c879b17">[https://bookdown.org/rdpeng/rprogdatascience/parallel-computation.html R Programming for Data Science]</ref>
# Usually, this kind of “hidden parallelism” will generally not affect you and will improve you computational efficiency.<ref name="ref_1c879b17" />
# Parallel computing is the execution of a computer program utilizing multiple computer processors (CPU) concurrently instead of using one processor exclusively.<ref name="ref_e575a3bd">[https://mathworld.wolfram.com/ParallelComputing.html Parallel Computing -- from Wolfram MathWorld]</ref>
===소스===
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 * ID :  [https://www.wikidata.org/wiki/Q232661 Q232661]

← 이전 판		2020년 12월 26일 (토) 12:16 판
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		+	* ID : [https://www.wikidata.org/wiki/Q232661 Q232661]

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2021년 2월 17일 (수) 08:06에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단