High-Performance Computing and Geography: Developments, Issues, and Case Studies

1998 ◽  
Vol 30 (10) ◽  
pp. 1839-1856 ◽  
Author(s):  
I Turton ◽  
S Openshaw

In this paper we outline some of the results that were obtained by the application of a Cray T3D parallel supercomputer to human geography problems. We emphasise the fundamental importance of high-performance computing (HPC) as a future relevant paradigm for doing geography. We offer an introduction to recent developments and illustrate how new computational intelligence technologies can start to be used to make use of opportunities created by data riches from geographic information systems, artificial intelligence tools, and HPC in geography.

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
E. A. Huerta ◽  
Asad Khan ◽  
Edward Davis ◽  
Colleen Bushell ◽  
William D. Gropp ◽  
...  

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.


Author(s):  
Eliu Huerta ◽  
Asad Khan ◽  
Edward Davis ◽  
Colleen Bushell ◽  
William Gropp ◽  
...  

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.


AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 75 ◽  
Author(s):  
Christopher Barrett ◽  
Keith Bisset ◽  
Jonathan Leidig ◽  
Achla Marathe ◽  
Madhav V. Marathe

We discuss an interaction-based approach to study the coevolution between socio-technical networks, individual behaviors, and contagion processes on these networks. We use epidemics in human population as an example of this phenomenon. The methods consist of developing synthetic yet realistic national-scale networks using a first principles approach. Unlike simple random graph techniques, these methods combine real world data sources with behavioral and social theories to synthesize detailed social contact (proximity) networks. Individual-based models of within-host disease progression and inter-host transmission are then used to model the contagion process. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics – a general purpose modeling environment to support pandemic planning and response. Simdemics is designed specifically to be scalable to networks with 300 million agents – the underlying algorithms and methods in Simdemics are all high-performance computing oriented methods. New advances in network science, machine learning, high performance computing, data mining and behavioral modeling were necessary to develop Simdemics. Simdemics is combined with two other environments, Simfrastructure and Didactic, to form an integrated cyberenvironment. The integrated cyber-environment provides the end-user flexible and seamless Internet based access to Simdemics. Service-oriented architectures play a critical role in delivering the desired services to the end user. Simdemics, in conjunction with the integrated cyber-environment, has been used in over a dozen user defined case studies. These case studies were done to support specific policy questions that arose in the context of planning the response to pandemics (e.g., H1N1, H5N1) and human initiated bio-terrorism events. These studies played a crucial role in the continual development and improvement of the cyber-environment.


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