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. 18.7% 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%
- 15k per year above
- Top-10% rate
- 18.7% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- -24% the tick is no change
Which countries lead Parallel Computing and Optimization Techniques research?
By volume, the United States and China publish the most (2k and 1.5k works in 2022–2025).
By volume, 2022–2025
- 1 United States 2k works
- 2 China 1.5k works
- 3 India 609 works
- 4 Germany 499 works
- 5 South Korea 285 works
- 6 France 284 works
- 7 United Kingdom 280 works
- 8 Japan 254 works
- 9 Italy 205 works
- 10 Spain 196 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.
Shares of the rows listed above, not of the whole node.
Which institutions lead Parallel Computing and Optimization Techniques research?
By volume in 2022–2025, National University of Defense Technology publishes the most Parallel Computing and Optimization Techniques research, followed by Tsinghua University and Shanghai Jiao Tong University.
By volume, 2022–2025
- 1 National University of Defense Technology China 90 works
- 2 Tsinghua University China 83 works
- 3 Shanghai Jiao Tong University China 57 works
- 4 Georgia Institute of Technology United States 51 works
- 5 University of Science and Technology of China China 46 works
- 6 Peking University China 46 works
- 7 ETH Zurich Switzerland 45 works
- 8 Intel (United States) United States 44 works
- 9 Oak Ridge National Laboratory United States 43 works
- 10 University of Illinois Urbana-Champaign United States 42 works
Who are the leading researchers in Parallel Computing and Optimization Techniques?
The most-cited researchers publishing on Parallel Computing and Optimization Techniques include Steven J. Plimpton and Ion Stoica.
- 1 Steven J. Plimpton 10k citations
- 2 Ion Stoica 9.6k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Parallel Computing and Optimization Techniques research done?
The largest centres of Parallel Computing and Optimization Techniques research in 2022–2025 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, 2022–2025
Where it is the local speciality
- Santa ClaraUS · 77.8 works39×
- LemontUS · 31.3 works21×
Location quotient: how much more of its research is in Parallel Computing and Optimization Techniques than the world average.
Where is the best place to study Parallel Computing and Optimization Techniques?
Among universities, judged by research, Carnegie Mellon University, ETH Zurich 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.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Carnegie Mellon University United States | 63.9 | 27.9% | 10.2× | 32 | +46.4% |
| 2 | ETH Zurich Switzerland | 63.7 | 27.0% | 8.7× | 45 | +1.3% |
| 3 | Georgia Institute of Technology United States | 61.7 | 25.4% | 10.6× | 51 | -19.4% |
| 4 | Korea Advanced Institute of Science and Technology South Korea | 55.0 | 28.3% | 8.4× | 30 | -24.2% |
| 5 | Universitat Politècnica de Catalunya Spain | 54.9 | 14.1% | 14.9× | 36 | -5.7% |
| 6 | National University of Defense Technology China | 52.4 | 9.4% | 14.9× | 90 | -32.7% |
| 7 | Hong Kong University of Science and Technology Hong Kong | 52.3 | 19.6% | 7.0× | 21 | +39.1% |
| 8 | Tsinghua University China | 50.7 | 22.6% | 5.4× | 83 | -20.8% |
| 9 | National University of Singapore Singapore | 48.2 | 36.7% | 3.0× | 22 | -18.5% |
| 10 | KTH Royal Institute of Technology Sweden | 48.0 | 24.6% | 6.5× | 19 | -5.1% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Parallel Computing and Optimization Techniques research growing?
Output in 2018–2022 was 24% lower than in 2013–2017, peaking in 2002. The fastest-growing topics are Parallel Computing and Optimization Techniques.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
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.