What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Programming language
His main research concerns Parallel computing, Computer network, Supercomputer, Distributed computing and Graphics processing unit.
His research investigates the connection between Parallel computing and topics such as General-purpose computing on graphics processing units that intersect with problems in Symmetric multiprocessor system.
His study in Supercomputer is interdisciplinary in nature, drawing from both Green computing, Embedded system and Mobile computing.
The study incorporates disciplines such as Scheduling, Reference data, Total cost of ownership and Computer maintenance in addition to Distributed computing.
While the research belongs to areas of Graphics processing unit, he spends his time largely on the problem of Central processing unit, intersecting his research to questions surrounding Multi-core processor.
His CUDA research includes elements of Programming paradigm and Graphics.
His most cited work include:
- The design, implementation, and evaluation of mpiBLAST (365 citations)
- A Power-Aware Run-Time System for High-Performance Computing (341 citations)
- The Quadrics network: high-performance clustering technology (317 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Parallel computing, Distributed computing, Computer network, Supercomputer and CUDA.
He has researched Parallel computing in several fields, including Scalability, Programming paradigm and General-purpose computing on graphics processing units.
As part of one scientific family, Wu-chun Feng deals mainly with the area of Distributed computing, narrowing it down to issues related to the The Internet, and often Communications protocol.
The subject of his Supercomputer research is within the realm of Operating system.
His CUDA study combines topics in areas such as Central processing unit, Kernel and Computational science.
His Graphics processing unit research incorporates elements of Coprocessor, Massively parallel, Instruction set and Graphics.
He most often published in these fields:
- Parallel computing (34.88%)
- Distributed computing (26.16%)
- Computer network (18.60%)
What were the highlights of his more recent work (between 2015-2021)?
- Parallel computing (34.88%)
- Speedup (11.34%)
- Supercomputer (14.83%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Parallel computing, Speedup, Supercomputer, Field-programmable gate array and Scalability.
His work carried out in the field of Parallel computing brings together such families of science as Scheduling, Data structure and Kernel.
His Speedup study incorporates themes from Control reconfiguration, x86, Heuristic and Macro.
His work deals with themes such as Visualization, Programming paradigm and Unconventional computing, which intersect with Supercomputer.
Wu-chun Feng has included themes like Computer architecture, Software portability and Compiler in his Field-programmable gate array study.
His research integrates issues of Distributed computing, Deep learning, Artificial intelligence, Bottleneck and Implementation in his study of Scalability.
Between 2015 and 2021, his most popular works were:
- Fast Detection of Transformed Data Leaks (42 citations)
- Fast segmented sort on GPUs (40 citations)
- Parallel Transposition of Sparse Data Structures (33 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Programming language
Parallel computing, Scalability, Speedup, Programming paradigm and Supercomputer are his primary areas of study.
His study of CUDA is a part of Parallel computing.
His study focuses on the intersection of Scalability and fields such as Bottleneck with connections in the field of Deep learning and Data access.
Wu-chun Feng works mostly in the field of Speedup, limiting it down to topics relating to x86 and, in certain cases, Code, Vectorization, Multi-core processor and SIMD.
In his study, which falls under the umbrella issue of Programming paradigm, Software portability, Symmetric multiprocessor system, Profiling and Hardware architecture is strongly linked to Field-programmable gate array.
His Supercomputer research is multidisciplinary, incorporating elements of Ensemble learning, Frequency scaling and Decision tree.
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