scholarly journals Machine Learning and High-Performance Computing Hybrid Systems, a New Way of Performance Acceleration in Engineering and Scientific Applications

2021 ◽  
Author(s):  
Pawel Gepner
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
Hybrid Systems ◽  
High Performance ◽  
Scientific Applications ◽  
Performance Computing

scholarly journals An Architecture for Developing Cyber Environments using Multiple HPC Infrastructures for e-Science Applications

2015 ◽  
Author(s):  
Felipe Maciel ◽  
Carina Oliveira ◽  
Renato Juaçaba Neto ◽  
João Alencar ◽  
Paulo Rego ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Scientific Applications ◽  
Computational Resources ◽  
Performance Computing

In this paper, we propose a novel architecture to allow the implementation of a cyber environment composed of different High Performance Computing (HPC) infrastructures (i.e., clusters, grids and clouds). To access this cyber environment, scientific researchers do not have to become computer experts. In particular, we assume that scientific researchers provide a description of the problem as an input to the cyber environment and then get their results without being responsible for managing the computational resources. We provide a prototype of the architecture and introduce an evaluation which studies a real workload of scientific applications executions. The results show the advantages of the proposed architecture. Besides, we highlight this work provides guidelines for developing cyber environments focused on e-Science.

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

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.

scholarly journals Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources

2021 ◽  
Author(s):  
Mariza Ferro ◽  
Vinicius P. Klôh ◽  
Matheus Gritz ◽  
Vitor de Sá ◽  
Bruno Schulze
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
High Performance ◽  
Performance Metrics ◽  
Scientific Applications ◽  
Computing Systems ◽  
Computational Resources ◽  
Performance Computing

Understanding the computational impact of scientific applications on computational architectures through runtime should guide the use of computational resources in high-performance computing systems. In this work, we propose an analysis of Machine Learning (ML) algorithms to gather knowledge about the performance of these applications through hardware events and derived performance metrics. Nine NAS benchmarks were executed and the hardware events were collected. These experimental results were used to train a Neural Network, a Decision Tree Regressor and a Linear Regression focusing on predicting the runtime of scientific applications according to the performance metrics.

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