What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Parallel computing
His scientific interests lie mostly in Parallel computing, Scalability, Distributed computing, Supercomputer and Performance prediction.
His work on Multi-core processor as part of general Parallel computing research is often related to Energy, thus linking different fields of science.
Bronis R. de Supinski has researched Scalability in several fields, including Critical path method, Data mining and File system.
His work on Software fault tolerance as part of his general Distributed computing study is frequently connected to Context, thereby bridging the divide between different branches of science.
His study in Supercomputer is interdisciplinary in nature, drawing from both Provisioning and Computational science.
His Performance prediction study integrates concerns from other disciplines, such as Artificial neural network, Machine learning, Artificial intelligence, Architectural model and Parallel algorithm.
His most cited work include:
- Design, Modeling, and Evaluation of a Scalable Multi-level Checkpointing System (404 citations)
- Adagio: making DVS practical for complex HPC applications (246 citations)
- Methods of inference and learning for performance modeling of parallel applications (172 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, Scalability, Supercomputer and Operating system.
The concepts of his Parallel computing study are interwoven with issues in Scheduling, Software and Programming paradigm.
Bronis R. de Supinski has included themes like Correctness and Interface in his Distributed computing study.
In his research on the topic of Scalability, Computer network is strongly related with File system.
He merges Supercomputer with Energy in his study.
His Shared memory research incorporates themes from Programming language and Thread.
He most often published in these fields:
- Parallel computing (49.74%)
- Distributed computing (26.46%)
- Scalability (22.75%)
What were the highlights of his more recent work (between 2014-2020)?
- Parallel computing (49.74%)
- Supercomputer (16.40%)
- Distributed computing (26.46%)
In recent papers he was focusing on the following fields of study:
Bronis R. de Supinski mainly focuses on Parallel computing, Supercomputer, Distributed computing, Programming language and Programming paradigm.
His Parallel computing study combines topics from a wide range of disciplines, such as Computer architecture and Software portability.
The Supercomputer study combines topics in areas such as Workload and Provisioning.
His studies in Distributed computing integrate themes in fields like Scalability and Computation.
His Programming language research includes themes of Task and Interoperability.
The various areas that Bronis R. de Supinski examines in his Programming paradigm study include Field-programmable gate array, Software, Task and Center of excellence.
Between 2014 and 2020, his most popular works were:
- Practical Resource Management in Power-Constrained, High Performance Computing (60 citations)
- Another Trip to the Wall: How Much Will Stacked DRAM Benefit HPC? (32 citations)
- HpMC: An Energy-aware Management System of Multi-level Memory Architectures (25 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Parallel computing
His primary areas of study are Distributed computing, Parallel computing, Supercomputer, CUDA and Scheduling.
He studies Distributed computing, focusing on Fault tolerance in particular.
His study looks at the relationship between Parallel computing and fields such as Programming paradigm, as well as how they intersect with chemical problems.
His work investigates the relationship between Supercomputer and topics such as Provisioning that intersect with problems in Computer hardware and Power management.
He combines subjects such as Virtual function, Pipeline, Memory bandwidth, Cyclomatic complexity and Speedup with his study of CUDA.
His work often combines Power budget and Scalability studies.
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