What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Compiler
His main research concerns Parallel computing, Compiler, Programming language, Automatic parallelization and Optimizing compiler.
His Parallel computing research includes themes of Program transformation and Operating system.
His work carried out in the field of Compiler brings together such families of science as Supercomputer, Automatic programming, Speedup, Algorithm and Programming paradigm.
His work in the fields of Fortran overlaps with other areas such as Polaris.
In his work, Code and Program parallelization is strongly intertwined with Dependence analysis, which is a subfield of Automatic parallelization.
His Optimizing compiler research is multidisciplinary, incorporating elements of Multiprocessing, Orchestration, Pentium and Code generation.
His most cited work include:
- OpenMP to GPGPU: a compiler framework for automatic translation and optimization (400 citations)
- Parallel programming with Polaris (304 citations)
- Parallel programming with Polaris (304 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Parallel computing, Compiler, Programming language, Automatic parallelization and Distributed computing.
His studies deal with areas such as Spec# and Programming paradigm as well as Parallel computing.
His biological study spans a wide range of topics, including Thread and Code generation.
His study in Fortran and Compiler construction falls within the category of Programming language.
His study in Automatic parallelization is interdisciplinary in nature, drawing from both Symbolic data analysis, Automatic programming and Dependence analysis.
His work focuses on many connections between Distributed computing and other disciplines, such as Resource, that overlap with his field of interest in Host, The Internet and Resource management.
He most often published in these fields:
- Parallel computing (59.30%)
- Compiler (49.75%)
- Programming language (28.14%)
What were the highlights of his more recent work (between 2007-2021)?
- Parallel computing (59.30%)
- Compiler (49.75%)
- Distributed computing (12.06%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Parallel computing, Compiler, Distributed computing, Automatic parallelization and Optimizing compiler.
Rudolf Eigenmann frequently studies issues relating to Programming paradigm and Parallel computing.
The study incorporates disciplines such as Computer architecture, Set, Shared memory and Code generation in addition to Compiler.
He has researched Distributed computing in several fields, including Scalability, Instruction cycle, Application software, Resource and Host.
His Automatic parallelization study combines topics from a wide range of disciplines, such as Program transformation, Automatic programming, Multi-core processor and Parallel processing.
His Optimizing compiler research is classified as research in Programming language.
Between 2007 and 2021, his most popular works were:
- OpenMP to GPGPU: a compiler framework for automatic translation and optimization (400 citations)
- OpenMPC: Extended OpenMP Programming and Tuning for GPUs (207 citations)
- Cetus: A Source-to-Source Compiler Infrastructure for Multicores (154 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Compiler
His primary areas of investigation include Parallel computing, Compiler, Optimizing compiler, Code generation and Automatic parallelization.
His Parallel computing research is multidisciplinary, incorporating perspectives in Operating system and Computational science.
Rudolf Eigenmann has included themes like Scheme and Data-flow analysis in his Compiler study.
His Optimizing compiler research focuses on Automatic programming and how it relates to Java, Parallel processing, Multiprocessing and High-level programming language.
His Code generation study incorporates themes from Computer architecture, CUDA, Programming paradigm and General-purpose computing on graphics processing units.
His Automatic parallelization research is multidisciplinary, relying on both Program transformation and Multi-core processor.
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