Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence

Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

Download Full-text

The New Technologies in the Pandemic Era

JOURNAL OF BIOENGINEERING AND TECHNOLOGY APPLIED TO HEALTH ◽

10.34178/jbth.v3i2.122 ◽

2020 ◽

Vol 3 (2) ◽

pp. 134-164

Author(s):

Erick Giovani Sperandio Nascimento ◽

Adhvan Novais Furtado ◽

Roberto Badaró ◽

Luciana Knop

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

High Performance Computing ◽

High Performance ◽

New Technologies ◽

World Health ◽

Human Beings ◽

Health Technologies ◽

Novel Coronavirus ◽

Performance Computing

The pandemic of the new coronavirus affected people’s lives by an unprecedented scale. Due to the need for isolation and the treatments, drugs, and vaccines, the pandemic amplified the digital health technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), Blockchain, Telecommunication Technology (TT) as well as High-Performance Computing (HPC) and other technologies, to historic levels. These technologies are being used to mitigate, facilitate pandemic strategies, and find treatments and vaccines. This paper aims to reach articles about new technologies applied to COVID-19 published in the main database (PubMed/Medline, Elsevier Science Direct, Scopus, Isi Web of Science, Embase, Excerpta Medica, UptoDate, Lilacs, Novel Coronavirus Resource Directory from Elsevier), in the high-impact international scientific Journals (Scimago Journal and Country Rank - SJR - and Journal Citation Reports - JCR), such as The Lancet, Science, Nature, The New England Journal of Medicine, Physiological Reviews, Journal of the American Medical Association, Plos One, Journal of Clinical Investigation, and in the data from Center for Disease Control (CDC), National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID) and World Health Organization (WHO). We prior selected meta-analysis, systematic reviews, article reviews, and original articles in this order. We reviewed 252 articles and used 140 from March to June 2020, using the terms coronavirus, SARS-CoV-2, novel coronavirus, Wuhan coronavirus, severe acute respiratory syndrome, 2019-nCoV, 2019 novel coronavirus, n-CoV-2, covid, n-SARS-2, COVID-19, corona virus, coronaviruses, New Technologies, Artificial Intelligence, Telemedicine, Telecommunication Technologies, AI, Big Data, BDA, TT, High-Performance Computing, Deep Learning, Neural Network, Blockchain, with the tools MeSH (Medical Subject Headings), AND, OR, and the characters [,“,; /., to ensure the best review topics. We concluded that this pandemic lastly consolidates the new technologies era and will change the whole way of the social life of human beings. Also, a big jump in medicine will happen on procedures, protocols, drug designs, attendances, encompassing all health areas, as well as in social and business behaviors.

Download Full-text

Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure

10.21203/rs.3.rs-36973/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Eliu Huerta ◽

Asad Khan ◽

Edward Davis ◽

Colleen Bushell ◽

William Gropp ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

High Performance Computing ◽

High Performance ◽

Large Scale ◽

Social Patterns ◽

Computing Paradigm ◽

Recent Developments ◽

Optimization Schemes ◽

Performance Computing

Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R\&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for AI applications that aim to provide novel solutions for big-data challenges posed by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC), which is critical to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and discuss avenues to accelerate and streamline the use of HPC platforms to design accelerated AI algorithms.

Download Full-text

PCJ Java library as a solution to integrate HPC, Big Data and Artificial Intelligence workloads

Journal Of Big Data ◽

10.1186/s40537-021-00454-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Marek Nowicki ◽

Łukasz Górski ◽

Piotr Bała

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

High Performance Computing ◽

High Performance ◽

Large Scale ◽

And Performance ◽

Performance Results ◽

Computational Systems ◽

Performance Computing ◽

Java Library

AbstractWith the development of peta- and exascale size computational systems there is growing interest in running Big Data and Artificial Intelligence (AI) applications on them. Big Data and AI applications are implemented in Java, Scala, Python and other languages that are not widely used in High-Performance Computing (HPC) which is still dominated by C and Fortran. Moreover, they are based on dedicated environments such as Hadoop or Spark which are difficult to integrate with the traditional HPC management systems. We have developed the Parallel Computing in Java (PCJ) library, a tool for scalable high-performance computing and Big Data processing in Java. In this paper, we present the basic functionality of the PCJ library with examples of highly scalable applications running on the large resources. The performance results are presented for different classes of applications including traditional computational intensive (HPC) workloads (e.g. stencil), as well as communication-intensive algorithms such as Fast Fourier Transform (FFT). We present implementation details and performance results for Big Data type processing running on petascale size systems. The examples of large scale AI workloads parallelized using PCJ are presented.

Download Full-text