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.
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