High-performance computing and machine learning applied in thermal systems analysis

2021 ◽  
Author(s):  
Mostafa Safdari Shadloo ◽  
Amin Rahmat ◽  
Larry K. B. Li ◽  
Omid Mahian ◽  
Avinash Alagumalai
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
Systems Analysis ◽  
High Performance ◽  
Thermal Systems ◽  
Performance Computing

Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms

2015 ◽  
Vol 11 ◽  
pp. 69-81 ◽  
Author(s):  
Emad Elsebakhi ◽  
Frank Lee ◽  
Eric Schendel ◽  
Anwar Haque ◽  
Nagarajan Kathireason ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Functional Networks ◽  
Computing Platforms ◽  
Performance Computing

scholarly journals Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

2019 ◽  
Vol 16 (2) ◽  
pp. 541-564
Author(s):  
Mathias Longo ◽  
Ana Rodriguez ◽  
Cristian Mateos ◽  
Alejandro Zunino
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
Computer Simulations ◽  
High Performance ◽  
Machine Learning Algorithms ◽  
Energy Usage ◽  
Energy Aware ◽  
Scientific Application ◽  
Performance Computing ◽  
Reducing Energy

In-silico research has grown considerably. Today?s scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.

scholarly journals Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

Joule ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 1410-1420 ◽  
Author(s):  
Juan-Pablo Correa-Baena ◽  
Kedar Hippalgaonkar ◽  
Jeroen van Duren ◽  
Shaffiq Jaffer ◽  
Vijay R. Chandrasekhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
High Performance ◽  
Materials Development ◽  
Performance Computing

A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks

2022 ◽  
pp. 98-118
Author(s):  
Nina Ihde ◽  
Paula Marten ◽  
Ahmed Eleliemy ◽  
Gabrielle Poerwawinata ◽  
Pedro Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Performance Computing

scholarly journals Applications of Machine Learning and High-Performance Computing in the Era of COVID-19

2021 ◽  
Vol 4 (3) ◽  
pp. 40
Author(s):  
Abdul Majeed
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
High Performance ◽  
Low Cost ◽  
General Purpose ◽  
Prototype System ◽  
Successful Implementation ◽  
Privacy And Security ◽  
Applications Of Machine Learning ◽  
Performance Computing

During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.

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