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High Performance Computing Lab
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A lab that explores high-performance computing technology using GPUs and parallel computing.
AboutAbout the lab
Making computation faster with parallel computing and GPUs.
The High Performance Computing Lab studies technologies for performing large-scale computations quickly.
It works on high-performance computing with GPUs and on parallel computing, in which multiple computers are connected and compute together.
Associate Professor Satoshi Ohshima lists high-performance computing with GPUs as a research theme, and Associate Professor Takeshi Nanri lists topics such as optimizing communication in parallel computing.
The faculty members belong to the Division of Advanced Computational Science of the Research Institute for Information Technology.
Key terms
- Parallel computing
- A method of dividing a computation among multiple computers. The efficiency of communication between the computers determines performance.
- RT core
- A mechanism built into GPUs that accelerates ray tracing (computation that follows the paths of rays).
- Low-rank approximation
- A method of approximating and representing a large matrix with less information.
ResearchResearch themes
The lab studies technologies for performing large-scale computations quickly in the field of high-performance computing. Depending on the faculty member, topics include high-performance computing with GPUs and optimization of communication on parallel computers.
*Illustrative imageUsing GPU RT cores for computational science
This research uses the RT cores built into GPUs in computational science. On GPUs equipped with hardware that accelerates ray tracing, the lab is also working on implementing and benchmarking calculations of radio wave propagation loss.
*Illustrative imageLow-rank approximation matrix computation on GPUs
This research aims to make computation fast on GPUs where large matrices are represented compactly using block-wise low-rank approximation.
*Illustrative imageFoundational technologies for highly scalable parallel computing
The lab studies foundational technologies for improving scalability in parallel computing, that is, the ability of performance to keep growing as the scale increases.
*Illustrative imageDynamic optimization of communication libraries
To make communication between computers efficient, the lab studies dynamic optimization, which adjusts the internal algorithms and parameters of a communication library using information obtained while a program is running, such as process placement, load conditions, and communication performance.
MembersFaculty
- Associate ProfessorSatoshi Ohshima
- Associate ProfessorTakeshi Nanri
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “IBM Blue Gene P supercomputer” Argonne National Laboratory's Flickr page / CC BY-SA 2.0 (cropped)
- Using GPU RT cores for computational science: “EVGA GeForce GTX 590” Intel Free Press / CC BY 2.0 (cropped)
- Low-rank approximation matrix computation on GPUs: “Alexandre Augusto Martins Rodrigues - Algebra linear e geometria euclidiana” digitalização / CC0 (cropped)
- Foundational technologies for highly scalable parallel computing: “Dutch national supercomputer "Huygens"” Dennis van Zuijlekom / CC BY-SA 2.0 (cropped)
- Dynamic optimization of communication libraries: “Optical fiber connectors-optical patch cable-01ASD” Asurnipal / CC BY-SA 4.0 (cropped)
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