What is he best known for?
The fields of study he is best known for:
- Operating system
- Central processing unit
- Programming language
Peter M. Kogge mainly focuses on Parallel computing, SIMD, MIMD, Computer hardware and Distributed memory.
His study in Parallel computing is interdisciplinary in nature, drawing from both Microprocessor, Bridging and Very-large-scale integration.
His work in SIMD tackles topics such as Parallel processing which are related to areas like Set, Network interface, Block, Logic synthesis and Computer architecture.
His biological study spans a wide range of topics, including Systolic array, MISD, Program counter and Instruction register.
Peter M. Kogge studied Computer hardware and Parallel array that intersect with Multiprocessing, Array processing and Sparse array.
His Distributed memory research incorporates elements of Vector processor, Supercomputer, Chip and Massively parallel.
His most cited work include:
- A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations (1044 citations)
- The Architecture of Pipelined Computers (466 citations)
- Advanced parallel array processor (APAP) (322 citations)
What are the main themes of his work throughout his whole career to date?
Peter M. Kogge focuses on Parallel computing, Computer architecture, SIMD, Computer hardware and MIMD.
His work carried out in the field of Parallel computing brings together such families of science as Multithreading, Thread and Uniform memory access.
His Computer architecture research includes elements of Parallel processing, Chip, Memory rank, Multi-core processor and Massively parallel.
His work is dedicated to discovering how Chip, Dram are connected with Logic gate and other disciplines.
The SIMD study combines topics in areas such as Parallel array and Array processing.
His MIMD study frequently draws parallels with other fields, such as Vector processor.
He most often published in these fields:
- Parallel computing (40.41%)
- Computer architecture (18.13%)
- SIMD (10.88%)
What were the highlights of his more recent work (between 2010-2020)?
- Parallel computing (40.41%)
- Computer architecture (18.13%)
- Scalability (5.18%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Parallel computing, Computer architecture, Scalability, Multithreading and Distributed computing.
His studies deal with areas such as Partitioned global address space and Programming paradigm as well as Parallel computing.
The study incorporates disciplines such as TOP500, Microarchitecture and Massively parallel in addition to Computer architecture.
Peter M. Kogge combines subjects such as Algorithm and Instruction set with his study of Multithreading.
His Distributed computing research focuses on subjects like Big data, which are linked to Speedup, Parameterized complexity, Resource, Variety and Leverage.
His research investigates the link between Thread and topics such as Multi-core processor that cross with problems in Energy consumption and Efficient energy use.
Between 2010 and 2020, his most popular works were:
- DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges (75 citations)
- Exascale Computing Trends: Adjusting to the "New Normal"' for Computer Architecture (61 citations)
- The tops in flops (50 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Programming language
The scientist’s investigation covers issues in Supercomputer, Parallel computing, Computer architecture, Electronic engineering and Concurrency.
His research integrates issues of Moore's law, Data science and FLOPS in his study of Supercomputer.
His Parallel computing study focuses on Multi-core processor in particular.
His work deals with themes such as Uniform memory access and Cache-only memory architecture, which intersect with Computer architecture.
His study in the fields of Logic gate under the domain of Electronic engineering overlaps with other disciplines such as Throughput.
His Concurrency course of study focuses on Engineering management and Big data, Energy consumption and Exascale computing.
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