H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 58 Citations 8,863 263 World Ranking 1784 National Ranking 72

Research.com Recognitions

Awards & Achievements

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
  • Central processing unit

The scientist’s investigation covers issues in Scalability, Distributed computing, Parallel computing, Supercomputer and Message passing. His Scalability research integrates issues from Process, Debugging, Data mining and Code. The concepts of his Distributed computing study are interwoven with issues in Plug-in, System software and Semantics.

His work in the fields of Parallel computing, such as Multi-core processor, intersects with other areas such as Energy. His research integrates issues of Key, Speedup, Computational science and Provisioning in his study of Supercomputer. In Message passing, he works on issues like Programming paradigm, which are connected to Message Passing Interface.

His most cited work include:

  • Efficiently exploring architectural design spaces via predictive modeling (299 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?

Martin Schulz spends much of his time researching Distributed computing, Scalability, Parallel computing, Supercomputer and Message passing. His study explores the link between Distributed computing and topics such as Shared memory that cross with problems in Uniform memory access. His work deals with themes such as Debugging, Overhead and Task, which intersect with Scalability.

His work on Multi-core processor is typically connected to Multigrid method as part of general Parallel computing study, connecting several disciplines of science. His study on Message passing is mostly dedicated to connecting different topics, such as Programming paradigm. His Message Passing Interface research is multidisciplinary, incorporating elements of Interface, Profiling and Implementation.

He most often published in these fields:

  • Distributed computing (33.13%)
  • Scalability (23.49%)
  • Parallel computing (23.49%)

What were the highlights of his more recent work (between 2016-2021)?

  • Distributed computing (33.13%)
  • Supercomputer (16.27%)
  • Scalability (23.49%)

In recent papers he was focusing on the following fields of study:

Martin Schulz mainly focuses on Distributed computing, Supercomputer, Scalability, Profiling and Parallel computing. His Distributed computing study focuses on Runtime system in particular. The Supercomputer study combines topics in areas such as Compiler, Embedded system and Resilience.

His Scalability research is multidisciplinary, relying on both Plug-in, Data store, Data science and Modular design. His Profiling research includes elements of Data modeling, Message Passing Interface and Data model. In the field of Parallel computing, his study on Task and Cache overlaps with subjects such as Memory systems and Exponential function.

Between 2016 and 2021, his most popular works were:

  • A Survey of MPI Usage in the US Exascale Computing Project (28 citations)
  • A Survey of MPI Usage in the US Exascale Computing Project (28 citations)
  • REFINE: realistic fault injection via compiler-based instrumentation for accuracy, portability and speed (21 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Programming language
  • Central processing unit

His main research concerns Supercomputer, Distributed computing, Scalability, Resilience and Exascale computing. Parallel computing covers Martin Schulz research in Supercomputer. The study incorporates disciplines such as Dram, Data mining and Search engine in addition to Parallel computing.

Martin Schulz works mostly in the field of Distributed computing, limiting it down to topics relating to Computation and, in certain cases, Runtime system, Scheduling, Exploit and Programming paradigm. His research investigates the link between Scalability and topics such as Modular design that cross with problems in Layer and Distributed data store. His studies deal with areas such as Instrumentation, Fault injection, Compiler and Computer architecture as well as Resilience.

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

Efficiently exploring architectural design spaces via predictive modeling

Engin Ïpek;Sally A. McKee;Rich Caruana;Bronis R. de Supinski.
architectural support for programming languages and operating systems (2006)

394 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

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

Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application

Ian Karlin;Abhinav Bhatele;Jeff Keasler;Bradford L. Chamberlain.
international parallel and distributed processing symposium (2013)

216 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

Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound

Barry Rountree;Dong H. Ahn;Bronis R. de Supinski;David K. Lowenthal.
international parallel and distributed processing symposium (2012)

189 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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