H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 47 Citations 7,682 157 World Ranking 3319 National Ranking 1722

Research.com Recognitions

Awards & Achievements

2021 - IEEE Fellow For leadership in the design and use of large-scale computing systems

2016 - ACM Senior Member

2006 - ACM Gordon Bell Prize Large-scale Electronic Structure Calculations of High-Z Metals on the BlueGene/L Platform

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Design, Modeling, and Evaluation of a Scalable Multi-level Checkpointing System

Adam Moody;Greg Bronevetsky;Kathryn Mohror;Bronis R. de Supinski.
ieee international conference on high performance computing data and analytics (2010)

711 Citations

Adagio: making DVS practical for complex HPC applications

Barry Rountree;David K. Lownenthal;Bronis R. de Supinski;Martin Schulz.
international conference on supercomputing (2009)

364 Citations

Methods of inference and learning for performance modeling of parallel applications

Benjamin C. Lee;David M. Brooks;Bronis R. de Supinski;Martin Schulz.
acm sigplan symposium on principles and practice of parallel programming (2007)

248 Citations

Dynamic Software Testing of MPI Applications with Umpire

Jeffrey S. Vetter;Bronis R. de Supinski.
conference on high performance computing (supercomputing) (2000)

242 Citations

Prediction models for multi-dimensional power-performance optimization on many cores

Matthew Curtis-Maury;Ankur Shah;Filip Blagojevic;Dimitrios S. Nikolopoulos.
international conference on parallel architectures and compilation techniques (2008)

231 Citations

An approach to performance prediction for parallel applications

Engin Ipek;Bronis R. de Supinski;Martin Schulz;Sally A. McKee.
european conference on parallel processing (2005)

217 Citations

Soft error vulnerability of iterative linear algebra methods

Greg Bronevetsky;Bronis de Supinski.
international conference on supercomputing (2008)

214 Citations

Bounding energy consumption in large-scale MPI programs

Barry Rountree;David K. Lowenthal;Shelby Funk;Vincent W. Freeh.
conference on high performance computing (supercomputing) (2007)

213 Citations

A regression-based approach to scalability prediction

Bradley J. Barnes;Barry Rountree;David K. Lowenthal;Jaxk Reeves.
international conference on supercomputing (2008)

213 Citations

Exploring hardware overprovisioning in power-constrained, high performance computing

Tapasya Patki;David K. Lowenthal;Barry Rountree;Martin Schulz.
international conference on supercomputing (2013)

190 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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