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Parallel Computing and Optimization Techniques

Parallel Computing and Optimization Techniques is a research topic within Hardware and Architecture. Science Explorer counts 79k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on parallel computing, performance optimization, and various aspects of multicore and heterogeneous computing. It covers topics such as GPU computing, memory systems, benchmarking, power management, simulation platforms, and high-performance computing.

  • Parallel Computing
  • Performance Optimization
  • GPU Computing
  • Multicore Architectures
  • Memory Systems
  • Benchmarking
  • Power Management
  • Simulation Platforms
  • Heterogeneous Computing
  • High-Performance Computing
Research works
79k
fractional, since 1950
In the world top 10%
7.8k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
-2%
the tick is no change
Largest producer
United States
32% of the 10 largest, 2022-now
Parallel Computing and Optimization Techniques

Which countries lead Parallel Computing and Optimization Techniques research?

By volume, the United States and China publish the most (2.4k and 1.9k works in 2022-now).

By volume, 2022-now

  1. 1 United States 2.4k works
  2. 2 China 1.9k works
  3. 3 India 723 works
  4. 4 Germany 571 works
  5. 5 South Korea 344 works
  6. 6 France 325 works
  7. 7 United Kingdom 325 works
  8. 8 Japan 287 works
  9. 9 Italy 229 works
  10. 10 Spain 221 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 32.3%China: 26.3%India: 9.9%Germany: 7.8%6 others listed: 23.7%32%largest
United States2,363 · 32.3%China1,918 · 26.3%India723 · 9.9%Germany571 · 7.8%6 others listed1,731 · 23.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Parallel Computing and Optimization Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Parallel Computing and Optimization Techniques research, followed by Centre National de la Recherche Scientifique and National University of Defense Technology.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 147 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 104 works
  3. 3 National University of Defense TechnologyChina 102 works
  4. 4 Tsinghua UniversityChina 100 works
  5. 5 Intel (United States)United States 76 works
  6. 6 Shanghai Jiao Tong UniversityChina 69 works
  7. 7 Helmholtz Association of German Research CentresGermany 66 works
  8. 8 Georgia Institute of TechnologyUnited States 62 works
  9. 9 Peking UniversityChina 55 works
  10. 10 ETH ZurichSwitzerland 52 works

Who are the leading researchers in Parallel Computing and Optimization Techniques?

The most-cited researchers publishing on Parallel Computing and Optimization Techniques include Yoshua Bengio, Rob Knight and Xiangyu Zhang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Rob Knight United States 468k citations
  3. 3 Xiangyu Zhang 327k citations
  4. 4 Wei Huang China 267k citations
  5. 5 M. Weber Israel 267k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Parallel Computing and Optimization Techniques?

The most cited paper in Parallel Computing and Optimization Techniques is "Fast Parallel Algorithms for Short-Range Molecular Dynamics" (Steven J. Plimpton, 1995), with 45,350 citations. Next come "fastp: an ultra-fast all-in-one FASTQ preprocessor" (31,650) and "MapReduce" (18,671).

Most cited papers
#PaperReleasedCitations
1 Fast Parallel Algorithms for Short-Range Molecular Dynamics Steven J. Plimpton · Journal of Computational Physics Mar 1995 45,350
2 fastp: an ultra-fast all-in-one FASTQ preprocessor Shifu Chen, Yanqing Zhou, Yaru Chen et al. · Bioinformatics Open access 7 Jul 2018 31,650
3 MapReduce Jay B. Dean, Sanjay Ghemawat · Communications of the ACM Open access 2008 18,671
4 LINCS: A linear constraint solver for molecular simulations Berk Hess, Henk Bekker, Herman J. C. Berendsen et al. · Journal of Computational Chemistry Sep 1997 17,504
5 Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct Romain Pereira, M. N. Martin, Adrien Roussel et al. · Lecture notes in computer science Conference paperOpen access 2023 12,967
All 94 most cited papers, with filters and sorting

Where is Parallel Computing and Optimization Techniques research done?

The largest centres of Parallel Computing and Optimization Techniques research in 2022-now are Beijing (China), Seoul (South Korea), Shanghai (China) and Changsha (China). Among places with at least 20 works in it, it is an unusually large share of all research in Santa Clara and Lemont.

Largest cities, 2022-now

  1. 1 BeijingChina 526 works
  2. 2 SeoulSouth Korea 214 works
  3. 3 ShanghaiChina 198 works
  4. 4 ChangshaChina 145 works
  5. 5 TokyoJapan 115 works
  6. 6 NanjingChina 95 works
  7. 7 Santa ClaraUnited States 86 works
  8. 8 BengaluruIndia 86 works
  9. 9 WuhanChina 84 works
  10. 10 GuangzhouChina 80 works

Where it is the local speciality

  1. Santa ClaraUS · 86.5 works37×
  2. LemontUS · 32.9 works19×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Parallel Computing and Optimization Techniques than the world average.

See Parallel Computing and Optimization Techniques on the map

Where is the best place to study Parallel Computing and Optimization Techniques?

Among universities, judged by research, ETH Zurich, Tsinghua University and Georgia Institute of Technology score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%30%mean 20.38%fractional works in this field (log) →share of work in the world top 10% →Korea Advanced Insti…ETH ZurichGeorgia Institute of…Tsinghua University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1ETH ZurichSwitzerland 70.319.8%9.3×52 -7.2%
2Tsinghua UniversityChina 69.317.4%5.4×100 +82.7%
3Georgia Institute of TechnologyUnited States 66.918.9%12.5×62 -8.1%
4Korea Advanced Institute of Science and TechnologySouth Korea 66.624.9%9.7×37 +28.5%
5Cornell UniversityUnited States 64.225.0%2.0×21 +30.8%
6Chinese University of Hong KongChina 64.025.1%3.1×18 +39.3%
7University of Wisconsin–MadisonUnited States 63.927.0%3.2×24 -8.6%
8Hong Kong University of Science and TechnologyChina 63.812.8%8.1×27 +121.3%
9Peking UniversityChina 63.616.7%3.4×55 +127.7%
10University of California, BerkeleyUnited States 61.716.2%3.3×26 +12.1%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Parallel Computing and Optimization Techniques, country by country

Is Parallel Computing and Optimization Techniques research growing?

Output in 2022-2025 was 2% lower than in 2018-2021, peaking in 2002.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Parallel Computing and Optimization Techniques as a percentage of every work published that year, 0.037% in 2024.

still counting0.10%0.20%0.30%Parallel Computing and Optimization Techniques0.043% in 202519801990200020102020

Fractional works of Parallel Computing and Optimization Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Parallel Computing and Optimization Techniques9.7% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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