4,166 research outputs found
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q
The complexity and cost of managing high-performance computing
infrastructures are on the rise. Automating management and repair through
predictive models to minimize human interventions is an attempt to increase
system availability and contain these costs. Building predictive models that
are accurate enough to be useful in automatic management cannot be based on
restricted log data from subsystems but requires a holistic approach to data
analysis from disparate sources. Here we provide a detailed multi-scale
characterization study based on four datasets reporting power consumption,
temperature, workload, and hardware/software events for an IBM Blue Gene/Q
installation. We show that the system runs a rich parallel workload, with low
correlation among its components in terms of temperature and power, but higher
correlation in terms of events. As expected, power and temperature correlate
strongly, while events display negative correlations with load and power. Power
and workload show moderate correlations, and only at the scale of components.
The aim of the study is a systematic, integrated characterization of the
computing infrastructure and discovery of correlation sources and levels to
serve as basis for future predictive modeling efforts.Comment: 12 pages, 7 Figure
Control of a train of high purity distillation columns for efficient production of 13C isotopes
It is well-known that high-purity distillation columns are difficult to control due to their ill-conditioned and strongly nonlinear behaviour. The fact that these processes are operated over a wide range of feed compositions and flow rates makes the control design even more challenging. This paper proposes the most suitable control strategies applicable to a series of cascaded distillation column processes. The conditions for control and input-output relations are discusssed in view of the global control strategy. The increase in complexity with increased number of series cascaded distillation column processes is tackled. Uncertainty in the model parameters is discussed with respect to the dynamics of the global train distillation process. The main outcome of this work is insight into the possible control methodologies for this particular class of distillation processes
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