2023 - Research.com Computer Science in Germany Leader Award
2006 - ACM Gordon Bell Prize Large-scale Electronic Structure Calculations of High-Z Metals on the BlueGene/L Platform
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.
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.
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.
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.
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Adagio: making DVS practical for complex HPC applications
Barry Rountree;David K. Lownenthal;Bronis R. de Supinski;Martin Schulz.
international conference on supercomputing (2009)
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)
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)
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)
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)
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)
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)
A regression-based approach to scalability prediction
Bradley J. Barnes;Barry Rountree;David K. Lowenthal;Jaxk Reeves.
international conference on supercomputing (2008)
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)
Exploring hardware overprovisioning in power-constrained, high performance computing
Tapasya Patki;David K. Lowenthal;Barry Rountree;Martin Schulz.
international conference on supercomputing (2013)
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