What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Software
Instrumentation, Parallel computing, Compiler, Application software and Programming language are his primary areas of study.
His Instrumentation research integrates issues from Embedded system and Systems engineering.
The study incorporates disciplines such as Data mining, Data collection and Search algorithm in addition to Parallel computing.
His Application software research includes themes of Computer architecture, Data structure and Programmer.
His work investigates the relationship between Programming language and topics such as Code that intersect with problems in Alpha, Software metric, x86 and Message passing.
His research integrates issues of Class, Feature, Function and SIMPLE in his study of Operating system.
His most cited work include:
- The Paradyn parallel performance measurement tool (788 citations)
- Instrumentation and measurement (598 citations)
- An API for Runtime Code Patching (542 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Parallel computing, Distributed computing, Instrumentation, Operating system and Computer engineering.
Jeffrey K. Hollingsworth has researched Parallel computing in several fields, including Scalability, Profiling, Code, Computation and Server.
Jeffrey K. Hollingsworth has included themes like Scheduling and Real-time computing in his Distributed computing study.
His Real-time computing research is multidisciplinary, relying on both Performance tuning, Software and Process.
His Instrumentation research includes elements of Computer architecture, Overhead and Feature.
Data collection is closely connected to Embedded system in his research, which is encompassed under the umbrella topic of Operating system.
He most often published in these fields:
- Parallel computing (18.55%)
- Distributed computing (18.55%)
- Instrumentation (13.71%)
What were the highlights of his more recent work (between 2011-2019)?
- Parallel computing (18.55%)
- Computation (7.26%)
- Code (9.68%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Parallel computing, Computation, Code, Computer architecture and Database-centric architecture.
The Parallel computing study combines topics in areas such as Key and Reduction.
Jeffrey K. Hollingsworth works mostly in the field of Code, limiting it down to concerns involving Parameter space and, occasionally, Scalability and Asynchronous communication.
His study looks at the relationship between Computer architecture and topics such as Data structure, which overlap with Profiling, Single node, SIMPLE and Computer hardware.
The various areas that he examines in his Floating point study include Program analysis and Instrumentation.
He undertakes multidisciplinary investigations into Instrumentation and Gaussian elimination in his work.
Between 2011 and 2019, his most popular works were:
- Automatically adapting programs for mixed-precision floating-point computation (72 citations)
- Dynamic floating-point cancellation detection (45 citations)
- Autotuning in High-Performance Computing Applications (37 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Software
His primary areas of study are Parallel computing, Computation, Extended precision, Floating point and Code.
His Parallel computing study combines topics in areas such as Key and Reduction.
His Computation research includes themes of Petascale computing, Supercomputer and Computer architecture.
His Extended precision research includes elements of Program analysis, Instrumentation and Memory bandwidth.
His study on Code also encompasses disciplines like
- Parameter space which connect with Cache, Asynchronous communication, Loop tiling and Scalability,
- Fast Fourier transform and related Decomposition.
The Speedup study combines topics in areas such as Search algorithm and Source code.
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