What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Artificial intelligence
Satoshi Matsuoka mainly investigates Parallel computing, Distributed computing, CUDA, Operating system and Supercomputer.
His Parallel computing research is multidisciplinary, relying on both Fast Fourier transform, Scalability, Kernel and General-purpose computing on graphics processing units.
His Distributed computing study incorporates themes from Scheduling, Computation and Bottleneck.
His biological study spans a wide range of topics, including Computational science, Solver, Memory bandwidth and FLOPS.
His Grid computing research extends to the thematically linked field of Operating system.
Satoshi Matsuoka has researched Supercomputer in several fields, including Graph, Multi-core processor and Benchmark.
His most cited work include:
- Teddy: a sketching interface for 3D freeform design (1138 citations)
- The International Exascale Software Project roadmap (580 citations)
- ECOOP'97 - Object-Oriented Programming (475 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Parallel computing, Distributed computing, Supercomputer, Operating system and Grid computing.
His Parallel computing study combines topics in areas such as Stencil and General-purpose computing on graphics processing units.
His study of Stencil code is a part of Stencil.
His Blocking research extends to Stencil code, which is thematically connected.
His studies in Distributed computing integrate themes in fields like Scheduling and Computer network.
His Supercomputer research includes themes of Scalability and Benchmark.
He most often published in these fields:
- Parallel computing (27.96%)
- Distributed computing (20.58%)
- Supercomputer (16.11%)
What were the highlights of his more recent work (between 2017-2021)?
- Parallel computing (27.96%)
- Artificial intelligence (7.61%)
- Supercomputer (16.11%)
In recent papers he was focusing on the following fields of study:
Satoshi Matsuoka focuses on Parallel computing, Artificial intelligence, Supercomputer, CUDA and Optoelectronics.
His Parallel computing research includes elements of Matrix, Sparse matrix and Stencil.
His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Recall and Scheduling.
His Supercomputer research incorporates themes from Proxy and Double-precision floating-point format.
His study explores the link between CUDA and topics such as Computation that cross with problems in Computational science and Kernel.
His research in Optoelectronics intersects with topics in Transistor, Thin-film transistor and Modulation.
Between 2017 and 2021, his most popular works were:
- Big data and extreme-scale computing: Pathways to Convergence-Toward a shaping strategy for a future software and data ecosystem for scientific inquiry (53 citations)
- Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL (50 citations)
- Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks (25 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Artificial intelligence
His primary areas of investigation include Parallel computing, Supercomputer, Artificial intelligence, Convolutional neural network and Field-programmable gate array.
Satoshi Matsuoka works in the field of Parallel computing, focusing on Xeon Phi in particular.
Xeon Phi is a subfield of Operating system that Satoshi Matsuoka studies.
His Supercomputer study combines topics from a wide range of disciplines, such as Machine learning, Scheduling and Recall.
His studies deal with areas such as Context and SIMPLE as well as Artificial intelligence.
His work carried out in the field of Field-programmable gate array brings together such families of science as Kernel, Software, System on a chip and Xeon.
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