High-Performance Computing for Theoretical Study of Nanoscale and Molecular Interconnects

2012 ◽  
pp. 78-97
Author(s):  
Rasit O. Topaloglu ◽  
Swati R. Manjari ◽  
Saroj K. Nayak
Keyword(s):  
Integrated Circuits ◽  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Quantum Effects ◽  
Accurate Analysis ◽  
Graphical Processing Units ◽  
Graphical Processing ◽  
Silicon Based ◽  
Performance Computing

Interconnects in semiconductor integrated circuits have shrunk to nanoscale sizes. This size reduction requires accurate analysis of the quantum effects. Furthermore, improved low-resistance interconnects need to be discovered that can integrate with biological and nanoelectronic systems. Accurate system-scale simulation of these quantum effects is possible with high-performance computing (HPC), while high cost and poor feasibility of experiments also suggest the application of simulation and HPC. This chapter introduces computational nanoelectronics, presenting real-world applications for the simulation and analysis of nanoscale and molecular interconnects, which may provide the connection between molecules and silicon-based devices. We survey computational nanoelectronics of interconnects and analyze four real-world case studies: 1) using graphical processing units (GPUs) for nanoelectronic simulations; 2) HPC simulations of current flow in nanotubes; 3) resistance analysis of molecular interconnects; and 4) electron transport improvement in graphene interconnects. In conclusion, HPC simulations are promising vehicles to advance interconnects and study their interactions with molecular/biological structures in support of traditional experimentation.

High-Performance Computing for Theoretical Study of Nanoscale and Molecular Interconnects

2014 ◽  
pp. 513-532
Author(s):  
Rasit O. Topaloglu ◽  
Swati R. Manjari ◽  
Saroj K. Nayak
Keyword(s):  
Integrated Circuits ◽  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Quantum Effects ◽  
Accurate Analysis ◽  
Graphical Processing Units ◽  
Graphical Processing ◽  
Silicon Based ◽  
Performance Computing

Interconnects in semiconductor integrated circuits have shrunk to nanoscale sizes. This size reduction requires accurate analysis of the quantum effects. Furthermore, improved low-resistance interconnects need to be discovered that can integrate with biological and nanoelectronic systems. Accurate system-scale simulation of these quantum effects is possible with high-performance computing (HPC), while high cost and poor feasibility of experiments also suggest the application of simulation and HPC. This chapter introduces computational nanoelectronics, presenting real-world applications for the simulation and analysis of nanoscale and molecular interconnects, which may provide the connection between molecules and silicon-based devices. We survey computational nanoelectronics of interconnects and analyze four real-world case studies: 1) using graphical processing units (GPUs) for nanoelectronic simulations; 2) HPC simulations of current flow in nanotubes; 3) resistance analysis of molecular interconnects; and 4) electron transport improvement in graphene interconnects. In conclusion, HPC simulations are promising vehicles to advance interconnects and study their interactions with molecular/biological structures in support of traditional experimentation.

scholarly journals Improving speed of models for improved real-world decision-making

2021 ◽  
Author(s):  
Jason Thompson ◽  
Haifeng Zhao ◽  
Sachith Seneviratne ◽  
Rohan Byrne ◽  
Rajith Vidanaarachichi ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Time Pressure ◽  
Practical Importance ◽  
Model Development ◽  
Sudden Onset ◽  
Health Crisis ◽  
Performance Improvements ◽  
Performance Computing

The sudden onset of the COVID-19 global health crisis and as-sociated economic and social fall-out has highlighted the im-portance of speed in modeling emergency scenarios so that ro-bust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement. Popular and ac-cessible agent-based modeling platforms such as NetLogo can be fast to develop, but slow to run when exploring broad param-eter spaces on individual workstations. However, while deploy-ment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require ad-ditional support for deploying their models on such infrastruc-ture. We show that model run-time speed improvements of be-tween 200x and 400x over desktop machines are possible using 1) a benchmark ‘wolf-sheep predation’ model in addition to 2) an example drawn from our own work modeling the spread of COVID-19 in Victoria, Australia. We describe how a focus on improving model speed is non-trivial for model development and discuss its practical importance for improved policy and de-cision-making in the real world. We provide all associated doc-umentation in a linked git repository.

Super Leaders

2015 ◽  
pp. 351-377
Author(s):  
Kim Grover-Haskin
Keyword(s):  
High Performance Computing ◽  
Baby Boomers ◽  
Real World ◽  
High Performance ◽  
Leadership Studies ◽  
Talent Pool ◽  
Real World Problem ◽  
The Future ◽  
It Leadership ◽  
Performance Computing

Present day and projected labor demands forecast a need for minds to comprehend in algorithm in order to leverage computing developments for real world problem resolutions. This chapter focuses not so much on solutions to the preparation of the learners and the scientists, but on the future leadership that will advocate and open doors for the high performance computing community to be funded, supported, and practiced. Supercomputing's sustainable future lies in its future of leadership. Studies over the last ten years identify a shift in leadership as the Baby Boomers enter retirement. The talent pool following the Baby Boomers will shrink in numbers between 2010-2020. Women continue to be under represented in IT leadership. This chapter provides information on the talent pool for supercomputing, discusses leadership and organizational culture as influenced by gender, and explores how a mentoring community fosters leaders for the future.

scholarly journals PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators

2020 ◽  
Vol 53 (5) ◽  
pp. 1404-1413
Author(s):  
Vincent Favre-Nicolin ◽  
Gaétan Girard ◽  
Steven Leake ◽  
Jerome Carnis ◽  
Yuriy Chushkin ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Near Field ◽  
Data Sets ◽  
Imaging Data ◽  
Multiple Gpus ◽  
X Ray ◽  
X Ray Imaging ◽  
Graphical Processing ◽  
Performance Computing

The open-source PyNX toolkit has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units (GPUs) to achieve high-performance computing speeds. The toolkit can be used for coherent diffraction imaging (CDI), ptychography and wavefront propagation, in the far- or near-field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles…) can be implemented in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple GPUs, e.g. for large ptychography data sets. Command-line scripts are available for on-line CDI and ptychography analysis, either from raw beamline data sets or using the coherent X-ray imaging data format.

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