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Parallel Computing and Optimization Techniques
TopicLeading institutions, researchers & key papers
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.
51
Works
IDs:OpenAlex
How has Parallel Computing and Optimization Techniques's publication output changed over time?
ScholarIQpublication output · 2003–2025
Output grew0% over the shown period — from 1 works in 2003 to 1 in 2025.
1
1
1
1
2
1
2
1
1
1
2003200420062008200920102011201420172025
What are the most-cited papers on Parallel Computing and Optimization Techniques?
ScholarIQmost cited works
The Jalapeño virtual machine
Bowen Alpern, C. R. Attanasio, John Barton, Michael Burke, Pau-Chen Cheng, Jong-Min Choi, Anthony Cocchi, Stephen J. Fink, David Grove, Michael Hind, Susan Flynn Hummel, Derek Lieber, Vassily Litvinov, Mark Mergen, T. Ngo, James R. Russell, Vivek Sarkar, Maurício Serrano, Janice C. Shepherd, S. E. Smith, Vugranam C. Sreedhar, Harini Srinivasan, John Whaley
S112676551. 2000579 Citations
Accelerating Compute-Intensive Applications with GPUs and FPGAs
Shuai Che, Jie Li, Jeremy W. Sheaffer, Kevin Skadron, John Lach
2008318 Citations
Interprocedural dependence analysis and parallelization
Michael Burke, Ron K. Cytron
S148324379. 2004219 Citations
An overview of the PTRAN analysis system for multiprocessing
Frances Allen, Michael Burke, Philippe Charles, Ron K. Cytron, Jeanne Ferrante
2014184 Citations
C-Pack: A High-Performance Microprocessor Cache Compression Algorithm
Xi Chen, Lei Yang, Robert P. Dick, Shang Li, Haris Lekatsas
S37538908. 2009162 Citations
Where is Parallel Computing and Optimization Techniques research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
S112676551579
S148324379219
S37538908162
S166774750138
S14852165076
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
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How much of the research on Parallel Computing and Optimization Techniques is open access?
ScholarIQopen access share
27%OPEN ACCESS
Gold
0%
Green
13%
Hybrid
13%
Bronze
0%
Closed
73%
Related on ScholarIQ
The Jalapeño virtual machine
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Score-P: A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir
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Accelerating Compute-Intensive Applications with GPUs and FPGAs
Paper
The Jalapeño dynamic optimizing compiler for Java
Paper
GraphP: Reducing Communication for PIM-Based Graph Processing with Efficient Data Partition
Paper
Interprocedural dependence analysis and parallelization
Paper