Blue Waters Enabled Advances in the Fields of Atmospheric Science, Climate, and Weather
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This document discusses how the Blue Waters supercomputer enabled advances in atmospheric science, climate, and weather modeling through high-resolution simulations and large datasets. It provides examples of research projects in these fields that were able to utilize Blue Waters' massive processing power and storage capabilities to run global models at finer scales and save more frequent output over longer time periods than previously possible. This high-performance computing resource has significantly furthered scientific understanding of phenomena like tropical cyclones, cloud microphysics, and long-term climate changes.
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"Blue Waters is one of the most powerful supercomputers in the world and is one of the fastest supercomputers on a university campus. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenging problems, from predicting the behavior of complex biological systems to simulating the evolution of the cosmos."
Watch the video: https://wp.me/p3RLHQ-kYx
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Mercator Ocean newsletter 36
Greetings all,
This month’s newsletter is devoted to data assimilation and its application to Ocean Reanalyses.
Brasseur is introducing this newsletter telling us about the history of Ocean Reanalyses, the need for such Reanalyses for
MyOcean users in particular, and the perspective of Ocean Reanalyses coupled with biogeochemistry or regional systems for
example.
Scientific articles about Ocean Reanalyses activities are then displayed as follows: First, Cabanes et al. are presenting CORA, a
new comprehensive and qualified ocean in-situ dataset from 1990 to 2008, developped at the Coriolis Data Centre at IFREMER
and used to build Ocean Reanalyses. A more comprehensive article will be devoted to the CORA dataset in our next April 2010
issue. Then, Remy at Mercator in Toulouse considers large scale decadal Ocean Reanalysis to assess the improvement due to
the variational method data assimilation and show the sensitivity of the estimate to different parameters. She uses a light
configuration system allowing running several long term reanalysis. Third, Ferry et al. present the French Global Ocean
Reanalysis (GLORYS) project which aims at producing eddy resolving global Ocean Reanalyses with different streams spanning different time periods and using different technical choices. This is a collaboration between Mercator and French research
laboratories, and is a contribution to the European MyOcean project. Then, Masina et al. at CMCC in Italy are presenting the
implementation of data assimilation techniques into global ocean circulation model in order to investigate the role of the ocean on
climate variability and predictability. Fifth, Smith et al. are presenting the Ocean Reanalyses studies at ESSC in the U.K. which
aim at reconstructing water masses variability and ocean transports. Finally, Langlais et al. are giving an example of the various
uses of ocean reanalyses: they are using the Australian BlueLink Reanalysis in order to look into details at the various Southern
Ocean fronts.
The next April 2010 newsletter will introduce a new editorial line with a common newsletter between the Mercator
Ocean Forecasting Center in Toulouse and the Coriolis Data Center at Ifremer in Brest. Some papers will be
dedicated to observations only, when others will display collaborations between the 2 aspects: Observations and
Model. The idea is to wider and complete the subjects treated in our newsletter, as well as to trigger interactions
between observations and modeling communities. This common Mercator-Coriolis Newsletter is a test for which
we will take the opportunity to ask for your feedback.
We wish you a pleasant reading!
Ocean Modelling
1. Scientific models are representations of phenomena that make them easier to understand through diagrams, physical models, or complex mathematics. The main types are visual, mathematical, and computer models.
2. Ocean circulation models represent ocean circulation, climate change, and pollutant distribution through factors like temperature, salinity, winds, and ocean features. There are mechanistic models for simplified processes and simulation models for realistic regional circulation.
3. Global climate models (GCMs) simulate climate system components but have coarse resolution. Regional climate models (RCMs) increase GCM resolution for a small area, providing more local information down to 50km. Parameterization replaces sub-grid scale processes in models.
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SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
Cost analysis Toolkit for In-situ Lunar Exploration. Presented to IAC 2006, b...
Cost analysis Toolkit for In-situ Lunar Exploration. Presented to IAC 2006, b...
Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habit...
Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habit...
Backscatter Working Group Software Inter-comparison Project Requesting and Co...
Backscatter Working Group Software Inter-comparison Project Requesting and Co...
DSD-INT 2017 High Performance Parallel Computing with iMODFLOW-MetaSWAP - Ver...
DSD-INT 2017 High Performance Parallel Computing with iMODFLOW-MetaSWAP - Ver...
INMARTECH 2018 - G.Masetti & P.Johnson - Sound Speed Management and Environme...
INMARTECH 2018 - G.Masetti & P.Johnson - Sound Speed Management and Environme...
Similar to Blue Waters Enabled Advances in the Fields of Atmospheric Science, Climate, and Weather
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute
We present a survey of computational and applied mathematical techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties.
Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.Research on Blue Waters
In this video from the HPC User Forum at Argonne, Dr. Brett Bode from NCSA presents: Research on Blue Waters.
"Blue Waters is one of the most powerful supercomputers in the world and is one of the fastest supercomputers on a university campus. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenging problems, from predicting the behavior of complex biological systems to simulating the evolution of the cosmos."
Watch the video: https://wp.me/p3RLHQ-kYx
Learn more: http://www.ncsa.illinois.edu/enabling/bluewaters
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Mercator Ocean newsletter 36
Greetings all,
This month’s newsletter is devoted to data assimilation and its application to Ocean Reanalyses.
Brasseur is introducing this newsletter telling us about the history of Ocean Reanalyses, the need for such Reanalyses for
MyOcean users in particular, and the perspective of Ocean Reanalyses coupled with biogeochemistry or regional systems for
example.
Scientific articles about Ocean Reanalyses activities are then displayed as follows: First, Cabanes et al. are presenting CORA, a
new comprehensive and qualified ocean in-situ dataset from 1990 to 2008, developped at the Coriolis Data Centre at IFREMER
and used to build Ocean Reanalyses. A more comprehensive article will be devoted to the CORA dataset in our next April 2010
issue. Then, Remy at Mercator in Toulouse considers large scale decadal Ocean Reanalysis to assess the improvement due to
the variational method data assimilation and show the sensitivity of the estimate to different parameters. She uses a light
configuration system allowing running several long term reanalysis. Third, Ferry et al. present the French Global Ocean
Reanalysis (GLORYS) project which aims at producing eddy resolving global Ocean Reanalyses with different streams spanning different time periods and using different technical choices. This is a collaboration between Mercator and French research
laboratories, and is a contribution to the European MyOcean project. Then, Masina et al. at CMCC in Italy are presenting the
implementation of data assimilation techniques into global ocean circulation model in order to investigate the role of the ocean on
climate variability and predictability. Fifth, Smith et al. are presenting the Ocean Reanalyses studies at ESSC in the U.K. which
aim at reconstructing water masses variability and ocean transports. Finally, Langlais et al. are giving an example of the various
uses of ocean reanalyses: they are using the Australian BlueLink Reanalysis in order to look into details at the various Southern
Ocean fronts.
The next April 2010 newsletter will introduce a new editorial line with a common newsletter between the Mercator
Ocean Forecasting Center in Toulouse and the Coriolis Data Center at Ifremer in Brest. Some papers will be
dedicated to observations only, when others will display collaborations between the 2 aspects: Observations and
Model. The idea is to wider and complete the subjects treated in our newsletter, as well as to trigger interactions
between observations and modeling communities. This common Mercator-Coriolis Newsletter is a test for which
we will take the opportunity to ask for your feedback.
We wish you a pleasant reading!
Ocean Modelling
1. Scientific models are representations of phenomena that make them easier to understand through diagrams, physical models, or complex mathematics. The main types are visual, mathematical, and computer models.
2. Ocean circulation models represent ocean circulation, climate change, and pollutant distribution through factors like temperature, salinity, winds, and ocean features. There are mechanistic models for simplified processes and simulation models for realistic regional circulation.
3. Global climate models (GCMs) simulate climate system components but have coarse resolution. Regional climate models (RCMs) increase GCM resolution for a small area, providing more local information down to 50km. Parameterization replaces sub-grid scale processes in models.
Cosmic rays and clouds: using open science to clear the confusion
A talk presented at the conference of Sun Climate Connections (SCC2015) in Kiel, Germany (March 18th, 2015) at the final meeting of the TOSCA group.
A video of Dr Jasa Calogovic presenting the talk live at SCC2015 is available on our YouTube channel at https://www.youtube.com/user/DrBenLaken
In it, we advocate an open science approach to address the conflicting results within the field of Sun – climate connections, particularly concerning the hypothesised role of cosmic rays on clouds. We briefly summarise some of the conflict within the field and discuss how it may arise. We then outline a project we are currently developing to enable users to isolate specific cloud types from the MODIS dataset, and examine a range of properties in a composite analysis (such as area coverage, optical depth, liquid water content). The composites make use of statistical methods we have previously outlined. More information can be found at http://www.benlaken.com
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute
This document discusses using machine learning to help develop subgrid parameterizations for climate models based on high-resolution simulations. It notes that while high-resolution models have progressed faster than parameterizations, humans still develop parameterizations, which is slow. The document explores using machine learning on comprehensive training datasets from high-resolution models to relate coarse-grid variables to subgrid-scale quantities needed by climate models. Challenges include making schemes stochastic, handling data outside the training range, and instability in global models. Past work applying neural networks to a cloud-resolving model dataset showed promise but has not been used prognostically. Overall, machine learning may help break the parameterization bottleneck if technical challenges can be overcome.Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute
Physical processes in the earth system are modeled with mathematical representations called parameterizations. This talk will describe some of the conceptual approaches and mathematics used do describe physical parameterizations focusing on cloud parameterizations. This includes tracing physical laws to discrete representations in coarse scale models. Clouds illustrate several of the complexities and techniques common to many physical parameterizations. This includes the problem of different scales, sub-grid scale variability. Discussions of mathematical methods for dealing with the sub-grid scale will be discussed. In-exactness or indeterminate problems for both weather and climate will be discussed, including the problems of indeterminate parameterizations, and inexact initial conditions. Different mathematical methods, including the use of stochastic methods, will be described and discussed, with examples from contemporary earth system models.Improving Physical Parametrizations in Climate Models using Machine Learning
This document discusses using machine learning to improve climate model physical parameterizations. It begins by introducing common issues with coarse resolution climate models, such as biases in mean climate states and variability. It then presents machine learning as a way to build flexible parameterization functions from high-resolution model data. Specifically, it explores using neural networks trained on single-column outputs from a global cloud-resolving model to provide tendencies like heating and moistening. While initial diagnostic tests showed skill, prognostic tests revealed issues with stability. The document discusses various approaches to address these issues, such as incorporating momentum tendencies and using stochastic and data assimilation methods. Overall, the work aims to develop machine learning parameterizations for climate models that are numerically stable and accurately represent sub
Toward a Global Interactive Earth Observing Cyberinfrastructure
The document discusses the need for a new generation of cyberinfrastructure to support interactive global earth observation. It outlines several prototyping projects that are building examples of systems enabling real-time control of remote instruments, remote data access and analysis. These projects are driving the development of an emerging cyber-architecture using web and grid services to link distributed data repositories and simulations.
Tolkien - Climate Middle Earth
The document describes three climate model simulations run by Radagast the Brown:
1) A simulation of the pre-industrial "Modern Earth" climate from 1800-1850 AD.
2) A simulation of the "Dinosaur Earth" climate approximately 65 million years ago during the Late Cretaceous period.
3) A simulation of the climate of "Middle Earth" from J.R.R. Tolkien's works.
The climate model is able to flexibly simulate different time periods and planetary configurations by changing boundary conditions like topography, atmospheric composition, and orbital parameters. The paper aims to demonstrate the model's capabilities and discuss the Middle Earth simulation results.
Zebra - TRIAD-ES Joint Presentation
This document discusses using high resolution site characterization tools to efficiently characterize contaminated sites. It presents an overview of tools like membrane interface probes, hydraulic profiling tools, and on-site analytical methods that can provide high density geological, hydrogeological, and contaminant distribution data. Case studies demonstrate how these tools can be used to precisely delineate non-aqueous phase liquid sources and fluxes to inform remediation decisions. The document emphasizes that high resolution data allows conceptual site models to evolve dynamically during investigations and for remedies to target the most mobile contamination.
CLIM Program: Remote Sensing Workshop, Optimization Methods in Remote Sensing...
CLIM Program: Remote Sensing Workshop, Optimization Methods in Remote Sensing...The Statistical and Applied Mathematical Sciences Institute
Statistical estimation and inference for large data sets require computationally efficient optimization methods. Remote sensing retrievals are, in fact, estimates of the underlying true state, and their optimization routines must necessarily make compromises in order to keep up with large data volumes. A sub-group of the Remote Sensing Working Group of the SAMSI Program on Mathematical and Statistical Methods for Climate and the Earth System is investigating how optimization in Bayesian-inspired retrievals and o_-line statistical methods could be made more computationally efficient. We will report on discussions held to-date and describe how progress in the theory of data systems research can positively impact optimization methodologies.Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
Talk at the EarthCube End-User Domain Workshop for Rock Deformation and Mineral Physics Research.
By Martin Kunz, Lawrence Berkeley National Laboratory
1_Buck - Wavemil Steps IGARSS-11.ppt
Wavemill is a proposed ocean surface current measurement mission concept that uses hybrid interferometry. A feasibility study generated promising results showing potential accuracies better than 10cm/s. Risk reduction activities are underway, including an airborne demonstration campaign and development of an end-to-end simulator. The next steps include a product assessment study and advancing the instrument design and mission requirements.
1_Buck - Wavemil Steps IGARSS-11.ppt
Wavemill is a proposed ocean surface current measurement mission concept that uses hybrid interferometry. A feasibility study generated promising results showing potential accuracies better than 10cm/s. Risk reduction activities are underway, including an airborne demonstration campaign and development of an end-to-end simulator. The next steps include a product assessment study and advancing the instrument design and mission requirements.
EcoTas13 BradEvans e-MAST
eMAST aims to integrate data from TERN and other sources to model ecosystems at all scales in Australia from 2013-2015. This will be done using data assimilation, model evaluation and optimization tools to further ecosystem science and help address questions about topics like carbon, water, climate change, fire, and biodiversity. Key products being delivered include high resolution climate and productivity datasets as well as tools for data analysis, interpolation and modeling. Progress includes the development and delivery of ANUClimate climate datasets and the ePiSaT model for estimating primary productivity across Australia using flux tower and satellite data.
The Emerging Cyberinfrastructure for Earth and Ocean Sciences
05.03.05
Invited Talk to the SIO Council
Title: The Emerging Cyberinfrastructure for Earth and Ocean Sciences
La Jolla, CA
DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...
Presentation by Edwin Elias, Deltares USA, Inc., USA, at the Delft3D - User Days (Day 2: Hydrodynamics), during Delft Software Days - Edition 2018. Tuesday, 13 November 2018, Delft.
TU1.L10 - Arctic Sea Ice dynamics for Global Climate Models: Results from the...
The GlobICE project produced sea ice dynamics data products for climate models from 1992 to present using SAR images. Products include ice motion, deformation, thickness, and area/volume fluxes. The data is gridded and available via FTP to evaluate for modeling and analysis of Arctic sea ice and its role in global climate. Validation found good agreement between GlobICE and in situ buoy data.
NASA Advanced Computing Environment for Science & Engineering
In this deck from the 2017 Argonne Training Program on Extreme-Scale Computing, Rupak Biswas from NASA presents: NASA Advanced Computing Environment for Science & Engineering.
""High performance computing is now integral to NASA’s portfolio of missions to pioneer the future of space exploration, accelerate scientific discovery, and enable aeronautics research. Anchored by the Pleiades supercomputer at NASA Ames Research Center, the High End Computing Capability (HECC) Project provides a fully integrated environment to satisfy NASA’s diverse modeling, simulation, and analysis needs. In addition, HECC serves as the agency’s expert source for evaluating emerging HPC technologies and maturing the most appropriate ones into the production environment. This includes investigating advanced IT technologies such as accelerators, cloud computing, collaborative environments, big data analytics, and adiabatic quantum computing. The overall goal is to provide a consolidated bleeding-edge environment to support NASA's computational and analysis requirements for science and engineering applications."
Dr. Rupak Biswas is currently the Director of Exploration Technology at NASA Ames Research Center, Moffett Field, Calif., and has held this Senior Executive Service (SES) position since January 2016. In this role, he in charge of planning, directing, and coordinating the technology development and operational activities of the organization that comprises of advanced supercomputing, human systems integration, intelligent systems, and entry systems technology. The directorate consists of approximately 700 employees with an annual budget of $160 million, and includes two of NASA’s critical and consolidated infrastructures: arc jet testing facility and supercomputing facility. He is also the Manager of the NASA-wide High End Computing Capability Project that provides a full range of advanced computational resources and services to numerous programs across the agency. In addition, he leads the emerging quantum computing effort for NASA.
Watch the video: https://wp.me/p3RLHQ-hua
Learn more: https://extremecomputingtraining.anl.gov/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Similar to Blue Waters Enabled Advances in the Fields of Atmospheric Science, Climate, and Weather (20)
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Cosmic rays and clouds: using open science to clear the confusion
Cosmic rays and clouds: using open science to clear the confusion
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Improving Physical Parametrizations in Climate Models using Machine Learning
Improving Physical Parametrizations in Climate Models using Machine Learning
Toward a Global Interactive Earth Observing Cyberinfrastructure
Toward a Global Interactive Earth Observing Cyberinfrastructure
CLIM Program: Remote Sensing Workshop, Optimization Methods in Remote Sensing...
CLIM Program: Remote Sensing Workshop, Optimization Methods in Remote Sensing...
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
Toward Real-Time Analysis of Large Data Volumes for Diffraction Studies by Ma...
The Emerging Cyberinfrastructure for Earth and Ocean Sciences
The Emerging Cyberinfrastructure for Earth and Ocean Sciences
DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...
DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...
TU1.L10 - Arctic Sea Ice dynamics for Global Climate Models: Results from the...
TU1.L10 - Arctic Sea Ice dynamics for Global Climate Models: Results from the...
NASA Advanced Computing Environment for Science & Engineering
NASA Advanced Computing Environment for Science & Engineering
More from inside-BigData.com
Major Market Shifts in IT
The document discusses the top 5 technologies that all organizations must understand: digital transformation, quantum computing, IoT, 5G, and AI/HPC. It provides an overview of each technology including opportunities and threats to organizations. The document emphasizes that understanding these emerging technologies is mandatory as the information revolution changes many aspects of life and business.
Preparing to program Aurora at Exascale - Early experiences and future direct...
In this deck from IWOCL / SYCLcon 2020, Hal Finkel from Argonne National Laboratory presents: Preparing to program Aurora at Exascale - Early experiences and future directions.
"Argonne National Laboratory’s Leadership Computing Facility will be home to Aurora, our first exascale supercomputer. Aurora promises to take scientific computing to a whole new level, and scientists and engineers from many different fields will take advantage of Aurora’s unprecedented computational capabilities to push the boundaries of human knowledge. In addition, Aurora’s support for advanced machine-learning and big-data computations will enable scientific workflows incorporating these techniques along with traditional HPC algorithms. Programming the state-of-the-art hardware in Aurora will be accomplished using state-of-the-art programming models. Some of these models, such as OpenMP, are long-established in the HPC ecosystem. Other models, such as Intel’s oneAPI, based on SYCL, are relatively-new models constructed with the benefit of significant experience. Many applications will not use these models directly, but rather, will use C++ abstraction libraries such as Kokkos or RAJA. Python will also be a common entry point to high-performance capabilities. As we look toward the future, features in the C++ standard itself will become increasingly relevant for accessing the extreme parallelism of exascale platforms.
This presentation will summarize the experiences of our team as we prepare for Aurora, exploring how to port applications to Aurora’s architecture and programming models, and distilling the challenges and best practices we’ve developed to date. oneAPI/SYCL and OpenMP are both critical models in these efforts, and while the ecosystem for Aurora has yet to mature, we’ve already had a great deal of success. Importantly, we are not passive recipients of programming models developed by others. Our team works not only with vendor-provided compilers and tools, but also develops improved open-source LLVM-based technologies that feed both open-source and vendor-provided capabilities. In addition, we actively participate in the standardization of OpenMP, SYCL, and C++. To conclude, I’ll share our thoughts on how these models can best develop in the future to support exascale-class systems."
Watch the video: https://wp.me/p3RLHQ-lPT
Learn more: https://www.iwocl.org/iwocl-2020/conference-program/
and
https://www.anl.gov/topic/aurora
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Transforming Private 5G Networks
In this deck, Greg Wahl from Advantech presents: Transforming Private 5G Networks.
Advantech Networks & Communications Group is driving innovation in next-generation network solutions with their High Performance Servers. We provide business critical hardware to the world's leading telecom and networking equipment manufacturers with both standard and customized products. Our High Performance Servers are highly configurable platforms designed to balance the best in x86 server-class processing performance with maximum I/O and offload density. The systems are cost effective, highly available and optimized to meet next generation networking and media processing needs.
“Advantech’s Networks and Communication Group has been both an innovator and trusted enabling partner in the telecommunications and network security markets for over a decade, designing and manufacturing products for OEMs that accelerate their network platform evolution and time to market.” Said Advantech Vice President of Networks & Communications Group, Ween Niu. “In the new IP Infrastructure era, we will be expanding our expertise in Software Defined Networking (SDN) and Network Function Virtualization (NFV), two of the essential conduits to 5G infrastructure agility making networks easier to install, secure, automate and manage in a cloud-based infrastructure.”
In addition to innovation in air interface technologies and architecture extensions, 5G will also need a new generation of network computing platforms to run the emerging software defined infrastructure, one that provides greater topology flexibility, essential to deliver on the promises of high availability, high coverage, low latency and high bandwidth connections. This will open up new parallel industry opportunities through dedicated 5G network slices reserved for specific industries dedicated to video traffic, augmented reality, IoT, connected cars etc. 5G unlocks many new doors and one of the keys to its enablement lies in the elasticity and flexibility of the underlying infrastructure.
Advantech’s corporate vision is to enable an intelligent planet. The company is a global leader in the fields of IoT intelligent systems and embedded platforms. To embrace the trends of IoT, big data, and artificial intelligence, Advantech promotes IoT hardware and software solutions with the Edge Intelligence WISE-PaaS core to assist business partners and clients in connecting their industrial chains. Advantech is also working with business partners to co-create business ecosystems that accelerate the goal of industrial intelligence."
Watch the video: https://wp.me/p3RLHQ-lPQ
* Company website: https://www.advantech.com/
* Solution page: https://www2.advantech.com/nc/newsletter/NCG/SKY/benefits.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The Incorporation of Machine Learning into Scientific Simulations at Lawrence...
In this deck from the Stanford HPC Conference, Katie Lewis from Lawrence Livermore National Laboratory presents: The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory.
"Scientific simulations have driven computing at Lawrence Livermore National Laboratory (LLNL) for decades. During that time, we have seen significant changes in hardware, tools, and algorithms. Today, data science, including machine learning, is one of the fastest growing areas of computing, and LLNL is investing in hardware, applications, and algorithms in this space. While the use of simulations to focus and understand experiments is well accepted in our community, machine learning brings new challenges that need to be addressed. I will explore applications for machine learning in scientific simulations that are showing promising results and further investigation that is needed to better understand its usefulness."
Watch the video: https://youtu.be/NVwmvCWpZ6Y
Learn more: https://computing.llnl.gov/research-area/machine-learning
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
How to Achieve High-Performance, Scalable and Distributed DNN Training on Mod...
In this deck from the Stanford HPC Conference, DK Panda from Ohio State University presents: How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems?
"This talk will start with an overview of challenges being faced by the AI community to achieve high-performance, scalable and distributed DNN training on Modern HPC systems with both scale-up and scale-out strategies. After that, the talk will focus on a range of solutions being carried out in my group to address these challenges. The solutions will include: 1) MPI-driven Deep Learning, 2) Co-designing Deep Learning Stacks with High-Performance MPI, 3) Out-of- core DNN training, and 4) Hybrid (Data and Model) parallelism. Case studies to accelerate DNN training with popular frameworks like TensorFlow, PyTorch, MXNet and Caffe on modern HPC systems will be presented."
Watch the video: https://youtu.be/LeUNoKZVuwQ
Learn more: http://web.cse.ohio-state.edu/~panda.2/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Evolving Cyberinfrastructure, Democratizing Data, and Scaling AI to Catalyze ...
In this deck from the Stanford HPC Conference, Nick Nystrom and Paola Buitrago provide an update from the Pittsburgh Supercomputing Center.
Nick Nystrom is Chief Scientist at the Pittsburgh Supercomputing Center (PSC). Nick is architect and PI for Bridges, PSC's flagship system that successfully pioneered the convergence of HPC, AI, and Big Data. He is also PI for the NIH Human Biomolecular Atlas Program’s HIVE Infrastructure Component and co-PI for projects that bring emerging AI technologies to research (Open Compass), apply machine learning to biomedical data for breast and lung cancer (Big Data for Better Health), and identify causal relationships in biomedical big data (the Center for Causal Discovery, an NIH Big Data to Knowledge Center of Excellence). His current research interests include hardware and software architecture, applications of machine learning to multimodal data (particularly for the life sciences) and to enhance simulation, and graph analytics.
Watch the video: https://youtu.be/LWEU1L1o7yY
Learn more: https://www.psc.edu/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
HPC Impact: EDA Telemetry Neural Networks
The document discusses using systems intelligence and artificial intelligence/neural networks to enhance semiconductor electronic design automation (EDA) workflows by collecting telemetry data from EDA jobs and infrastructure and analyzing it using complex event processing, machine learning models, and messaging substrates to provide insights that could optimize EDA pipelines and infrastructure. The approach aims to allow both internal and external augmentation of EDA processes and environments through unsupervised and incremental learning.
Biohybrid Robotic Jellyfish for Future Applications in Ocean Monitoring
In this deck from the Stanford HPC Conference, Nicole Xu from Stanford University describes how she transformed a common jellyfish into a bionic creature that is part animal and part machine.
"Animal locomotion and bioinspiration have the potential to expand the performance capabilities of robots, but current implementations are limited. Mechanical soft robots leverage engineered materials and are highly controllable, but these biomimetic robots consume more power than corresponding animal counterparts. Biological soft robots from a bottom-up approach offer advantages such as speed and controllability but are limited to survival in cell media. Instead, biohybrid robots that comprise live animals and self- contained microelectronic systems leverage the animals’ own metabolism to reduce power constraints and body as an natural scaffold with damage tolerance. We demonstrate that by integrating onboard microelectronics into live jellyfish, we can enhance propulsion up to threefold, using only 10 mW of external power input to the microelectronics and at only a twofold increase in cost of transport to the animal. This robotic system uses 10 to 1000 times less external power per mass than existing swimming robots in literature and can be used in future applications for ocean monitoring to track environmental changes."
Watch the video: https://youtu.be/HrmJFyvInj8
Learn more: https://sanfrancisco.cbslocal.com/2020/02/05/stanford-research-project-common-jellyfish-bionic-sea-creatures/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Machine Learning for Weather Forecasts
In this deck from the Stanford HPC Conference, Peter Dueben from the European Centre for Medium-Range Weather Forecasts (ECMWF) presents: Machine Learning for Weather Forecasts.
"I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future."
Peter is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud- resolving simulations with ECMWF's forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.
Watch the video: https://youtu.be/ks3fkRj8Iqc
Learn more: https://www.ecmwf.int/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
HPC AI Advisory Council Update
In this deck, Gilad Shainer from the HPC AI Advisory Council describes how this organization fosters innovation in the high performance computing community.
"The HPC-AI Advisory Council’s mission is to bridge the gap between high-performance computing (HPC) and Artificial Intelligence (AI) use and its potential, bring the beneficial capabilities of HPC and AI to new users for better research, education, innovation and product manufacturing, bring users the expertise needed to operate HPC and AI systems, provide application designers with the tools needed to enable parallel computing, and to strengthen the qualification and integration of HPC and AI system products."
Watch the video: https://wp.me/p3RLHQ-lNz
Learn more: http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Fugaku Supercomputer joins fight against COVID-19
Today RIKEN in Japan announced that the Fugaku supercomputer will be made available for research projects aimed to combat COVID-19.
"Fugaku is currently being installed and is scheduled to be available to the public in 2021. However, faced with the devastating disaster unfolding before our eyes, RIKEN and MEXT decided to make a portion of the computational resources of Fugaku available for COVID-19-related projects ahead of schedule while continuing the installation process.
Fugaku is being developed not only for the progress in science, but also to help build the society dubbed as the “Society 5.0” by the Japanese government, where all people will live safe and comfortable lives. The current initiative to fight against the novel coronavirus is driven by the philosophy behind the development of Fugaku."
Initial Projects
Exploring new drug candidates for COVID-19 by "Fugaku"
Yasushi Okuno, RIKEN / Kyoto University
Prediction of conformational dynamics of proteins on the surface of SARS-Cov-2 using Fugaku
Yuji Sugita, RIKEN
Simulation analysis of pandemic phenomena
Nobuyasu Ito, RIKEN
Fragment molecular orbital calculations for COVID-19 proteins
Yuji Mochizuki, Rikkyo University
Energy Efficient Computing using Dynamic Tuning
In this deck from the Performance Optimisation and Productivity group, Lubomir Riha from IT4Innovations presents: Energy Efficient Computing using Dynamic Tuning.
"We now live in a world of power-constrained architectures and systems and power consumption represents a significant cost factor in the overall HPC system economy. For these reasons, in recent years researchers, supercomputing centers and major vendors have developed new tools and methodologies to measure and optimize the energy consumption of large-scale high performance system installations. Due to the link between energy consumption, power consumption and execution time of an application executed by the final user, it is important for these tools and the methodology used to consider all these aspects, empowering the final user and the system administrator with the capability of finding the best configuration given different high level objectives.
This webinar focused on tools designed to improve the energy-efficiency of HPC applications using a methodology of dynamic tuning of HPC applications, developed under the H2020 READEX project. The READEX methodology has been designed for exploiting the dynamic behaviour of software. At design time, different runtime situations (RTS) are detected and optimized system configurations are determined. RTSs with the same configuration are grouped into scenarios, forming the tuning model. At runtime, the tuning model is used to switch system configurations dynamically.
The MERIC tool, that implements the READEX methodology, is presented. It supports manual or binary instrumentation of the analysed applications to simplify the analysis. This instrumentation is used to identify and annotate the significant regions in the HPC application. Automatic binary instrumentation annotates regions with significant runtime. Manual instrumentation, which can be combined with automatic, allows code developer to annotate regions of particular interest."
Watch the video: https://wp.me/p3RLHQ-lJP
Learn more: https://pop-coe.eu/blog/14th-pop-webinar-energy-efficient-computing-using-dynamic-tuning
and
https://code.it4i.cz/vys0053/meric
Sign up for our insideHPC Newsletter: http://insidehpc.com/newslett
HPC at Scale Enabled by DDN A3i and NVIDIA SuperPOD
The document discusses how DDN A3I storage solutions and Nvidia's SuperPOD platform can enable HPC at scale. It provides details on DDN's A3I appliances that are optimized for AI and deep learning workloads and validated for Nvidia's DGX-2 SuperPOD reference architecture. The solutions are said to deliver the fastest performance, effortless scaling, reliability and flexibility for data-intensive workloads.
State of ARM-based HPC
In this deck, Paul Isaacs from Linaro presents: State of ARM-based HPC. This talk provides an overview of applications and infrastructure services successfully ported to Aarch64 and benefiting from scale.
"With its debut on the TOP500, the 125,000-core Astra supercomputer at New Mexico’s Sandia Labs uses Cavium ThunderX2 chips to mark Arm’s entry into the petascale world. In Japan, the Fujitsu A64FX Arm-based CPU in the pending Fugaku supercomputer has been optimized to achieve high-level, real-world application performance, anticipating up to one hundred times the application execution performance of the K computer. K was the first computer to top 10 petaflops in 2011."
Watch the video: https://wp.me/p3RLHQ-lIT
Learn more: https://www.linaro.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Versal Premium ACAP for Network and Cloud Acceleration
Today Xilinx announced Versal Premium, the third series in the Versal ACAP portfolio. The Versal Premium series features highly integrated, networked and power-optimized cores and the industry’s highest bandwidth and compute density on an adaptable platform. Versal Premium is designed for the highest bandwidth networks operating in thermally and spatially constrained environments, as well as for cloud providers who need scalable, adaptable application acceleration.
Versal is the industry’s first adaptive compute acceleration platform (ACAP), a revolutionary new category of heterogeneous compute devices with capabilities that far exceed those of conventional silicon architectures. Developed on TSMC’s 7-nanometer process technology, Versal Premium combines software programmability with dynamically configurable hardware acceleration and pre-engineered connectivity and security features to enable a faster time-to- market. The Versal Premium series delivers up to 3X higher throughput compared to current generation FPGAs, with built-in Ethernet, Interlaken, and cryptographic engines that enable fast and secure networks. The series doubles the compute density of currently deployed mainstream FPGAs and provides the adaptability to keep pace with increasingly diverse and evolving cloud and networking workloads.
Learn more: https://insidehpc.com/2020/03/xilinx-announces-versal-premium-acap-for-network-and-cloud-acceleration/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Zettar: Moving Massive Amounts of Data across Any Distance Efficiently
In this video from the Rice Oil & Gas Conference, Chin Fang from Zettar presents: Moving Massive Amounts of Data across Any Distance Efficiently.
The objective of this talk is to present two on-going projects aiming at improving and ensuring highly efficient bulk transferring or streaming of massive amounts of data over digital connections across any distance. It examines the current state of the art, a few very common misconceptions, the differences among the three major type of data movement solutions, a current initiative attempting to improve the data movement efficiency from the ground up, and another multi-stage project that shows how to conduct long distance large scale data movement at speed and scale internationally. Both projects have real world motivations, e.g. the ambitious data transfer requirements of Linac Coherent Light Source II (LCLS-II) [1], a premier preparation project of the U.S. DOE Exascale Computing Initiative (ECI) [2]. Their immediate goals are described and explained, together with the solution used for each. Findings and early results are reported. Possible future works are outlined.
Watch the video: https://wp.me/p3RLHQ-lBX
Learn more: https://www.zettar.com/
and
https://rice2020oghpc.rice.edu/program-2/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Scaling TCO in a Post Moore's Era
In this deck from the Rice Oil & Gas Conference, Bradley McCredie from AMD presents: Scaling TCO in a Post Moore's Law Era.
"While foundries bravely drive forward to overcome the technical and economic challenges posed by scaling to 5nm and beyond, Moore’s law alone can provide only a fraction of the performance / watt and performance / dollar gains needed to satisfy the demands of today’s high performance computing and artificial intelligence applications. To close the gap, multiple strategies are required. First, new levels of innovation and design efficiency will supplement technology gains to continue to deliver meaningful improvements in SoC performance. Second, heterogenous compute architectures will create x-factor increases of performance efficiency for the most critical applications. Finally, open software frameworks, APIs, and toolsets will enable broad ecosystems of application level innovation."
Watch the video:
Learn more: http://amd.com
and
https://rice2020oghpc.rice.edu/program-2/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
CUDA-Python and RAPIDS for blazing fast scientific computing
In this deck from the ECSS Symposium, Abe Stern from NVIDIA presents: CUDA-Python and RAPIDS for blazing fast scientific computing.
"We will introduce Numba and RAPIDS for GPU programming in Python. Numba allows us to write just-in-time compiled CUDA code in Python, giving us easy access to the power of GPUs from a powerful high-level language. RAPIDS is a suite of tools with a Python interface for machine learning and dataframe operations. Together, Numba and RAPIDS represent a potent set of tools for rapid prototyping, development, and analysis for scientific computing. We will cover the basics of each library and go over simple examples to get users started. Finally, we will briefly highlight several other relevant libraries for GPU programming."
Watch the video: https://wp.me/p3RLHQ-lvu
Learn more: https://developer.nvidia.com/rapids
and
https://www.xsede.org/for-users/ecss/ecss-symposium
Sign up for our insideHPC Newsletter: http://insidehp.com/newsletter
Introducing HPC with a Raspberry Pi Cluster
In this deck from FOSDEM 2020, Colin Sauze from Aberystwyth University describes the development of a RaspberryPi cluster for teaching an introduction to HPC.
"The motivation for this was to overcome four key problems faced by new HPC users:
* The availability of a real HPC system and the effect running training courses can have on the real system, conversely the availability of spare resources on the real system can cause problems for the training course.
* A fear of using a large and expensive HPC system for the first time and worries that doing something wrong might damage the system.
* That HPC systems are very abstract systems sitting in data centres that users never see, it is difficult for them to understand exactly what it is they are using.
* That new users fail to understand resource limitations, in part because of the vast resources in modern HPC systems a lot of mistakes can be made before running out of resources. A more resource constrained system makes it easier to understand this.
The talk will also discuss some of the technical challenges in deploying an HPC environment to a Raspberry Pi and attempts to keep that environment as close to a "real" HPC as possible. The issue to trying to automate the installation process will also be covered."
Learn more: https://github.com/colinsauze/pi_cluster
and
https://fosdem.org/2020/schedule/events/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Overview of HPC Interconnects
In this deck from ATPESC 2019, Ken Raffenetti from Argonne presents an overview of HPC interconnects.
"The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two-week training on the key skills, approaches, and tools to design, implement, and execute computational science and engineering applications on current high-end computing systems and the leadership-class computing systems of the future."
Watch the video: https://wp.me/p3RLHQ-luc
Learn more: https://extremecomputingtraining.anl.gov/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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Blue Waters Enabled Advances in the Fields of Atmospheric Science, Climate, and Weather
- 1. BLUE WATERS ENABLED ADVANCES IN THE FIELDS OF ATMOSPHERIC SCIENCE, CLIMATE, AND WEATHER Susan Bates National Center for Atmospheric Research Blue Waters Users Symposium June 6, 2018 NCAR is sponsored by the National Science Foundation
- 3. •Systems of differential equations that describe fluid motion, radiative transfer, etc. •Planet divided into 3- dimensional grid to solve the equations •More grid boxes = higher resolutionhttps://www.earthmagazine.org/article/todays-weather-forecast-good-strong-chance-improvement Earth Magazine UCAR/NCAR Multimedia Gallery https://www2.ucar.edu/news/understanding-climate-change-multimedia-gallery
- 4. New CPL7 Architecture processors New Single Executable CESM1 architecture (cpl7) time CPL (regridding, merging) CAM CLM CICE Driver (controls time evolution) POP Sequential Layout processors Hybrid Sequential/Concurrent Layouts CAM CLM CICE POP Driver CPL Original Multiple Executable CCSM3 architecture (cpl6) CAM CLM CICE POP CPL time processors atmosphere coupler land sea ice ocean
- 5. FAST but COARSE CLIMATE WORKHORSE SLOW and EXPENSIVE TCs thunderstorm sea breezes tornado cloud microphysics ETCs
- 7. Present Day 1985-2005 Present Day 1985-2005 (modified dust) Future RCP8.5 2070-2090 Future RCP8.5 2070-2090 (modified SST) Global Pacific Atlantic StormCountperyear AllStormsHurricanes Major Hurricanes S. Bates and N. Rosenbloom, NCAR • Community Earth System Model (CESM) • 0.25° atmosphere/land • Forced with interannually-varying sea surface temperature and sea ice • 908 nodes • 1.4 model years per calendar day
- 8. Observed TC tracks and intensities (1981-2010) CESM (Atm-Only) CESM (Fully-Coupled) Both coupled and uncoupled versions of CESM simulate realistic spatial reasonably captures key features of observed TC activity IBTrACS https://www.ncdc.noaa.gov/ibtracs/ • Community Earth System Model (CESM) • Saved sub-daily atmosphere and ocean fields • Developed a reliable tracking algorithm for tropical cyclones in the model • Computationally expensive due to resolution, sub-daily output, and simulation length (decades). • Courtesy: Kevin Reed [See also: Wehner et al CAM5: 1 degree • C sk Courtesy Kevin Reed (See also Wehner et al. 2014, JAMES) R. Sriver and H. Li University of Illinois
- 10. RCP4.5 – Present Day RCP8.5 – Present Day • Weather, Research, and Forecasting Model (WRF) • 12km atmos • 90 nodes • 4 months every 1 calendar day • 150 years • higher resolution to capture some of the mesoscale processes that take place on small scales that are important for extreme temperature and precipitation (e.g. Midwest soil moisture for summer TMAX; convective processes for extreme precipitation) D. Wuebbles and Z. Zobel University of Illinois Blue Waters Professor
- 11. ¡ Entrainment is the introduction of dry air from outside the cloud inward, by its own turbulent motions (eddies). ¡ Predicting entrainment (and the resulting cloud dilution– drying and cooling) is important for: forecasting cloud/storm development & precipitation in weather forecasting models and climate models The turbulent eddies that entrain dry air also give a cumulus cloud its “cauliflower” appearance. • Cloud simulations have historically underestimated entrainment. • It has commonly been stated that this is a result of inadequate model resolution for properly representing the smaller eddies contributing to the entrainment. But no one knew for sure… Sonia Lasher-Trapp, U. Illinois Blue Waters Professor
- 12. • CM1 model– George Bryan, NCAR • Coarse-grained, pure MPI, 3-D cloud model designed to scale to tens of thousands of processors • Requires model output at high temporal resolution (3 to 6 seconds) Resolving ~ 40-50 m eddies 10 meter grid spacing: 1152 x 1152 x 700 grid points (~930 M), Dt = 0.15 s, for 1800 s à 4096 processors for ~80 hrs à Output files ~7 GB x 300 files ~2 TB • Limit at which further grid refinement is unproductive at diluting the cloud = ~15m • Before this study, the impact of increasing resolution on entrainment was only speculation. • Impact: guide parameterizations in larger-scale weather and climate models. Sonia Lasher-Trapp, U. Illinois Blue Waters Professor
- 13. Leigh Orf University of Wisconsin - Madison
- 14. Annual Mean Color Annual Mean Texture Zhao, G., L. Di Girolamo, D.J. Diner, C.J. Bruegge, K. Mueller, and D.L. Wu, 2016: Regional changes in Earth’s color and texture as observed from space over a 15-year period. IEEE Trans. Geosci. Remote Sens., 54(7), 4240-4249, doi:10.1109/TGRS.2016.2538723 Terra/MISR spectral radiance data from 2000 to 2015 (240 TB) was processed on Blue Waters to examine how the Earth’s color and texture changed over this period. The first color composite and texture images of the Earth’s climate Larry Di Girolamo, U. Illinois Blue Waters Professor
- 15. • Resolution • Number of simulations (ensembles), scenarios • Amount of data saved • Frequency of sampling • Mechanisms investigated or novel way in which they were investigated Cutting Edge Research Enabled by Blue Waters
- 16. • Blue Waters provided a massively parallel system, one of the largest storage and bandwidth computing facilities and excellent sharing services. • Blue Waters, with its huge number of nodes, its high speed, and its large storage capability for high-resolution model output and analysis allows us to push the spatial scale limit much farther than in the past. • The hardware needed to run these kinds of simulations quickly exceeds the limits of most computers. • Blue Waters staff have helped us to learn new and practical ways to visualize the output for easier analysis. • The Blue Waters staff understand the needs of our project and facilitate getting jobs through the queue. It’s not just the machine that enables our science but the staff as well. • If we didn’t have Blue Waters, we would have accomplished about 1/10 of what we did. • These are the calculations that I had been waiting to perform during my career, and Blue Waters presented that opportunity! • Machines like Blue Waters can create incredible simulations and amazing amounts of data that will long exceed the machine's life span. WHY BLUE WATERS?