Advanced Scientific Computing Research Exascale Requirements Review. An Office of Science review sponsored by Advanced Scientific Computing Research, September 27-29, 2016, Rockville, Maryland

Almgren, Ann; DeMar, Phil; Vetter, Jeffrey S.; Riley, Katherine; Antypas, Katie; Bard, Deborah; Coffey, Richard; Dart, Eli; Dosanjh, Sudip S.; Gerber, R.; Hack, James J.; Monga, Inder; Papka, Michael E.; Rotman, Lauren; Straatsma, Tjerk P.; Wells, Jack; Bernholdt, David E.; Bethel, Wes; Bosilca, George; Cappello, Frank; Gamblin, Todd; Habib, Salman; Judy, Hill,; Hollingsworth, Jeffrey K.; McInnes, Lois Curfman; Mohror, Kathryn; Moore, Shirley; Moreland, Ken; Roser, R.; Shende, Sameer; Shipman, Galen; Williams, Samuel

doi:10.2172/1375638

Cited by 6 publications

(4 citation statements)

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“…Extreme-scale architectures provide unprecedented resources for scientific discovery. At the same time, the computational science and engineering (CSE) community faces daunting productivity and sustainability challenges for parallel application development [1,12,13,21]. Difficulties include increasing complexity of algorithms and computer science techniques required by coupled multiscale and multiphysics applications.…”

Section: Software Challenges For Extreme-scale Sciencementioning

confidence: 99%

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

Bartlett

Demeshko

Gamblin

et al. 2017

JSFI

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Extreme-scale computational science increasingly demands multiscale and multiphysics formulations. Combining software developed by independent groups is imperative: no single team has resources for all predictive science and decision support capabilities. Scientific libraries provide high-quality, reusable software components for constructing applications with improved robustness and portability. However, without coordination, many libraries cannot be easily composed. Namespace collisions, inconsistent arguments, lack of third-party software versioning, and additional difficulties make composition costly. The Extreme-scale Scientific Software Development Kit (xSDK) defines community policies to improve code quality and compatibility across independently developed packages (hypre, PETSc, SuperLU, Trilinos, and Alquimia) and provides a foundation for addressing broader issues in software interoperability, performance portability, and sustainability. The xSDK provides turnkey installation of member software and seamless combination of aggregate capabilities, and it marks first steps toward extreme-scale scientific software ecosystems from which future applications can be composed rapidly with assured quality and scalability.

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Section: Software Challenges For Extreme-scale Sciencementioning

confidence: 99%

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

Bartlett

Demeshko

Gamblin

et al. 2017

JSFI

View full text Add to dashboard Cite

show abstract

“…Extreme-scale architectures provide unprecedented resources for scientific discovery. At the same time, the computational science and engineering (CSE) community faces daunting productivity and sustainability challenges for parallel application development [1,14,15,27]. Difficulties include increasing complexity of algorithms and computer science techniques required by coupled multiscale and multiphysics applications.…”

Section: Software Challenges For Extreme-scale Sciencementioning

confidence: 99%

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

Bartlett¹,

Demeshko²,

Gamblin³

et al. 2017

Preprint

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Extreme-scale computational science increasingly demands multiscale and multiphysics formulations. Combining software developed by independent groups is imperative: no single team has resources for all predictive science and decision support capabilities. Scientific libraries provide high-quality, reusable software components for constructing applications with improved robustness and portability. However, without coordination, many libraries cannot be easily composed. Namespace collisions, inconsistent arguments, lack of third-party software versioning, and additional difficulties make composition costly.The Extreme-scale Scientific Software Development Kit (xSDK) defines community policies to improve code quality and compatibility across independently developed packages (hypre, PETSc, Su-perLU, Trilinos, and Alquimia) and provides a foundation for addressing broader issues in software interoperability, performance portability, and sustainability. The xSDK provides turnkey installation of member software and seamless combination of aggregate capabilities, and it marks first steps toward extreme-scale scientific software ecosystems from which future applications can be composed rapidly with assured quality and scalability.

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“…While most techniques focus on analyzing "big data" sets, which are comprised of redundant information, collecting smaller but information-rich data sets has become equally important. Brute-force data collection leads to tremendous inefficiencies in the utilization of experimental facilities and instruments, in data analysis and data storage; large experimental facilities around the globe are running at 10 to 20 percent utilization and are still spending millions of dollars each year to keep up with the increase in the amount of data storage needed [16,14,1,35]. In addition, conventional experiments require scientists to prepare samples and directly control experiments, which leads to highly-trained researchers spending significant effort on micromanaging experimental tasks rather than thinking about scientific meaning.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels

Noack¹,

Doerk²,

Li³

et al. 2020

Preprint

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A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously steering experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

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Advanced Scientific Computing Research Exascale Requirements Review. An Office of Science review sponsored by Advanced Scientific Computing Research, September 27-29, 2016, Rockville, Maryland

Cited by 6 publications

References 0 publications

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels

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