Energy-performance optimized design of silicon photonic interconnection networks for high-performance computing

2017 ◽  
Author(s):  
Meisam Bahadori ◽  
Sebastien Rumley ◽  
Robert Polster ◽  
Alexander Gazman ◽  
Matt Traverso ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Interconnection Networks ◽  
High Performance ◽  
Energy Performance ◽  
Optimized Design ◽  
Silicon Photonic ◽  
Photonic Interconnection ◽  
Performance Computing

A Silicon Photonic Chip-Scale AWGR Switch for High Performance Computing Systems

CLEO: 2014 ◽  
2014 ◽  
Author(s):  
Runxiang Yu ◽  
Stanley Cheung ◽  
Yuliang Li ◽  
Katsunari Okamoto ◽  
Roberto Proietti ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Systems ◽  
Silicon Photonic ◽  
Performance Computing

Ultralow-power silicon photonic interconnect for high-performance computing systems

2010 ◽  
Author(s):  
GuoLiang Li ◽  
Xuezhe Zheng ◽  
Jon Lexau ◽  
Ying Luo ◽  
Hiren Thacker ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Systems ◽  
Silicon Photonic ◽  
Ultralow Power ◽  
Performance Computing

scholarly journals Utilizing High Performance Computing to Improve the Application of Machine Learning for Time-Efficient Prediction of Buildings’ Daylighting Performance

2021 ◽  
Vol 2069 (1) ◽  
pp. 012153
Author(s):  
Rania Labib
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
High Performance ◽  
Learning Algorithm ◽  
Energy Performance ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Validation Data ◽  
Time Required ◽  
Performance Computing

Abstract Architects often investigate the daylighting performance of hundreds of design solutions and configurations to ensure an energy-efficient solution for their designs. To shorten the time required for daylighting simulations, architects usually reduce the number of variables or parameters of the building and facade design. This practice usually results in the elimination of design variables that could contribute to an energy-optimized design configuration. Therefore, recent research has focused on incorporating machine learning algorithms that require the execution of only a relatively small subset of the simulations to predict the daylighting and energy performance of buildings. Although machine learning has been shown to be accurate, it still becomes a time-consuming process due to the time required to execute a set of simulations to be used as training and validation data. Furthermore, to save time, designers often decide to use a small simulation subset, which leads to a poorly designed machine learning algorithm that produces inaccurate results. Therefore, this study aims to introduce an automated framework that utilizes high performance computing (HPC) to execute the simulations necessary for the machine learning algorithm while saving time and effort. High performance computing facilitates the execution of thousands of tasks simultaneously for a time-efficient simulation process, therefore allowing designers to increase the size of the simulation’s subset. Pairing high performance computing with machine learning allows for accurate and nearly instantaneous building performance predictions.

scholarly journals A scalable silicon photonic chip-scale optical switch for high performance computing systems

2013 ◽  
Vol 21 (26) ◽  
pp. 32655 ◽  
Author(s):  
Runxiang Yu ◽  
Stanley Cheung ◽  
Yuliang Li ◽  
Katsunari Okamoto ◽  
Roberto Proietti ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Optical Switch ◽  
Computing Systems ◽  
Silicon Photonic ◽  
Performance Computing
Sign in / Sign up

Export Citation Format

Share Document