Cybersecurity and Risk Management and World-wide Standards
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In this deck from the HPC User Forum in Santa Fe, Henry Newman from Seagate Government Solutions presents: Cybersecurity and Risk Management and World-wide Standards. "Henry’s talk described how cyber attacks and security breaches have become commonplace with explosion of data. More often than not, these breaches could have prevented or greatly reduced if these institutions would have followed prescribed security standards. As we move to the edge and go to 5G networks, there is going to be more distributed data and therefore protection is going to have to go out to the edge as well. Right now, we don’t really know how to do that, so things are going to get a lot worse. Henry described the inherent risks involved as the growth of data as world continues to go mobile. Analysis project that the world will have 163 zettabytes of data by 2025, with Figure 1 showing the growing gap between data that should be protected and data left unprotected. This gap reflects an increasing industry need for security and privacy technologies, systems, and processes to address it." Watch the video: https://wp.me/p3RLHQ-kiW Learn more: https://www.seagategov.com/ and http://hpcuserforum.com Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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Major Market Shifts in IT
In this deck from the Stanford HPC Conference, Shahin Khan from OrionX describes major market Shifts in IT.
"We will discuss the digital infrastructure of the future enterprise and the state of these trends."
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Watch the video: https://wp.me/p3RLHQ-lPP
Learn more: http://orionx.net
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http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
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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.
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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
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Watch the video: https://wp.me/p3RLHQ-lPQ
* Company website: https://www.advantech.com/
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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.
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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?
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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/
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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
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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
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http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Recommended
Major Market Shifts in IT
In this deck from the Stanford HPC Conference, Shahin Khan from OrionX describes major market Shifts in IT.
"We will discuss the digital infrastructure of the future enterprise and the state of these trends."
"We work with clients on the impact of Digital Transformation (DX) on them, their customers, and their messages. Generally, they want to track, in one place, trends like IoT, 5G, AI, Blockchain, and Quantum Computing. And they want to know what these trends mean, how they affect each other, and when they demand action, and how to formulate and execute an effective plan. If that describes you, we can help."
Watch the video: https://wp.me/p3RLHQ-lPP
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and
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Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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.
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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.
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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
In this deck from the Stanford HPC Conference, Ryan Quick from Providentia Worldwide describes how DNNs can be used to improve EDA simulation runs.
"Systems Intelligence relies on a variety of methods for providing insight into the core mechanisms for driving automated behavioral changes in self-healing command and control platforms. This talk reports on initial efforts with leveraging Semiconductor Electronic Design Automation (EDA) telemetry data from cross-domain sources including power, network, storage, nodes, and applications in neural networks as a driving method for insight into SI automation systems."
Watch the video: https://youtu.be/2WbR8tq-XbM
Learn more: http://www.providentiaworldwide.com/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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
In this deck from GTC Digital, William Beaudin from DDN presents: HPC at Scale Enabled by DDN A3i and NVIDIA SuperPOD.
Enabling high performance computing through the use of GPUs requires an incredible amount of IO to sustain application performance. We'll cover architectures that enable extremely scalable applications through the use of NVIDIA’s SuperPOD and DDN’s A3I systems.
The NVIDIA DGX SuperPOD is a first-of-its-kind artificial intelligence (AI) supercomputing infrastructure. DDN A³I with the EXA5 parallel file system is a turnkey, AI data storage infrastructure for rapid deployment, featuring faster performance, effortless scale, and simplified operations through deeper integration. The combined solution delivers groundbreaking performance, deploys in weeks as a fully integrated system, and is designed to solve the world's most challenging AI problems.
Watch the video: https://wp.me/p3RLHQ-lIV
Learn more: https://www.ddn.com/download/nvidia-superpod-ddn-a3i-ai400-appliance-with-the-exa5-filesystem/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
In this deck from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases.
"In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy. Then we apply optimization algorithms to generate and prune the set of model configurations to try.
Deep neural networks are revolutionizing many machine learning applications, but hundreds of trials may be needed to generate a good model architecture and associated hyperparameters. This is the challenge of model selection. It is time consuming and expensive, especially if you are only training one model at a time.
Massively parallel processing databases can have hundreds of workers, so can you use this parallel compute architecture to address the challenge of model selection for deep nets, in order to make it faster and cheaper?
It’s possible!
We will demonstrate results from this project using a version of Hyperband, which is a well known hyperparameter optimization algorithm, and the deep learning frameworks Keras and TensorFlow, all running on Greenplum database using Apache MADlib. Other topics will include architecture, scalability results and bright opportunities for the future."
Watch the video: https://wp.me/p3RLHQ-lsQ
Learn more: https://fosdem.org/2020/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Data Parallel Deep Learning
In this deck, Huihuo Zheng from Argonne National Laboratory presents: Data Parallel Deep Learning.
"The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two weeks of 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-lsl
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Making Supernovae with Jets
In this deck from DOE CSGF 2019, Chelsea Harris from the University of Michigan presents: Making Supernovae with Jets.
"Supernovae are the explosions of stars. One reason they are fundamentally important is they create and disperse elements heavier than carbon throughout the universe. Different stars explode in different ways, but the most common supernova type is from massive stars (greater than 10 times the mass of the sun) whose cores collapse to form a neutron star or black hole – "core collapse supernovae" or CC SNe. Even among massive stars, though, there are differences that can affect the outcome of core collapse. I am specifically interested in progenitors whose cores are rotating and magnetic (magnetorotational). Such cores may experience instabilities after collapse that launch a fast jet, which could rescue r-process elements formed near the proto-neutron star from destruction. The instabilities can also add power to the explosion and relieve tension between observations and theory. In addition to running simulations with existing FLASH code modules, I am developing a FLASH hydrodynamics module, SparkJoy, to perform these simulations at high order. These projects are part of a DOE INCITE project to explore progenitor effects on CC SNe and of the DOE SciDAC program "Towards Exascale Astrophysics of Mergers and Supernovae," a nationwide collaboration of supernova theorists unprecedented in its collaborative scale. Research like mine is made much easier at Michigan State University through the Department of Computational Mathematics, Science, and Engineering which, like the DOE CSGF, brings together members from different areas to share knowledge and strengthen each other's research."
Watch the video: https://wp.me/p3RLHQ-lr0
Learn more: https://www.krellinst.org/csgf/conf/2019/video/charris
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Adaptive Linear Solvers and Eigensolvers
In this deck from ATPESC 2019, Jack Dongarra from UT Knoxville presents: Adaptive Linear Solvers and Eigensolvers.
"Success in large-scale scientific computations often depends on algorithm design. Even the fastest machine may prove to be inadequate if insufficient attention is paid to the way in which the computation is organized. We have used several problems from computational physics to illustrate the importance of good algorithms, and we offer some very general principles for designing algorithms. Two subthemes are, first, the strong connection between the algorithm and the architecture of the target machine; and second, the importance of non-numerical methods in scientific computations."
Watch the video: https://wp.me/p3RLHQ-lq3
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Scientific Applications and Heterogeneous Architectures
In this deck from ATPESC 2019, Michela Taufer from UT Knoxville presents: Scientific Applications and Heterogeneous Architectures.
"This talk discusses two emerging trends in computing (i.e., the convergence of data generation and analytics, and the emergence of edge computing) and how these trends can impact heterogeneous applications. Next-generation supercomputers, with their extremely heterogeneous resources and dramatically higher performance than current systems, will generate more data than we need or, even, can handle. At the same time, more and more data is generated at the “edge,” requiring computing and storage to move closer and closer to data sources. The coordination of data generation and analysis across the spectrum of heterogonous systems including supercomputers, cloud computing, and edge computing adds additional layers of heterogeneity to applications’ workflows. More importantly, the coordination can neither rely on manual, centralized approaches as it is predominately done today in HPC nor exclusively be delegated to be just a problem for commercial Clouds. This talk presents case studies of heterogenous applications in precision medicine and precision farming that expand scientist workflows beyond the supercomputing center and shed our reliance on large-scale simulations exclusively, for the sake of scientific discovery."
Watch the video: https://wp.me/p3RLHQ-lq2
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
SW/HW co-design for near-term quantum computing
In this deck from ATPESC 2019, Yunong Shi from the University of Chicago presents: SW/HW co-design for near-term quantum computing.
"The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two weeks of 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-lpv
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
FPGAs and Machine Learning
In this deck from ATPESC 2019, James Moawad and Greg Nash from Intel present: FPGAs and Machine Learning.
"Neural networks are inspired by biological systems, in particular the human brain. Through the combination of powerful computing resources and novel architectures for neurons, neural networks have achieved state-of-the-art results in many domains such as computer vision and machine translation. FPGAs are a natural choice for implementing neural networks as they can handle different algorithms in computing, logic, and memory resources in the same device. Faster performance comparing to competitive implementations as the user can hardcore operations into the hardware. Software developers can use the OpenCL device C level programming standard to target FPGAs as accelerators to standard CPUs without having to deal with hardware level design."
Watch the video: https://wp.me/p3RLHQ-lnc
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
and
https://www.intel.com/content/www/us/en/products/programmable/fpga.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Deep Learning State of the Art (2020)
In this video from the MIT Deep Learning Series, Lex Fridman presents: Deep Learning State of the Art (2020).
"This lecture is on the most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series."
Watch the video: https://wp.me/p3RLHQ-lng
Learn more: https://deeplearning.mit.edu/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
JMeter webinar - integration with InfluxDB and Grafana
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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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
In this deck from GTC Digital, William Beaudin from DDN presents: HPC at Scale Enabled by DDN A3i and NVIDIA SuperPOD.
Enabling high performance computing through the use of GPUs requires an incredible amount of IO to sustain application performance. We'll cover architectures that enable extremely scalable applications through the use of NVIDIA’s SuperPOD and DDN’s A3I systems.
The NVIDIA DGX SuperPOD is a first-of-its-kind artificial intelligence (AI) supercomputing infrastructure. DDN A³I with the EXA5 parallel file system is a turnkey, AI data storage infrastructure for rapid deployment, featuring faster performance, effortless scale, and simplified operations through deeper integration. The combined solution delivers groundbreaking performance, deploys in weeks as a fully integrated system, and is designed to solve the world's most challenging AI problems.
Watch the video: https://wp.me/p3RLHQ-lIV
Learn more: https://www.ddn.com/download/nvidia-superpod-ddn-a3i-ai400-appliance-with-the-exa5-filesystem/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
In this deck from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases.
"In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy. Then we apply optimization algorithms to generate and prune the set of model configurations to try.
Deep neural networks are revolutionizing many machine learning applications, but hundreds of trials may be needed to generate a good model architecture and associated hyperparameters. This is the challenge of model selection. It is time consuming and expensive, especially if you are only training one model at a time.
Massively parallel processing databases can have hundreds of workers, so can you use this parallel compute architecture to address the challenge of model selection for deep nets, in order to make it faster and cheaper?
It’s possible!
We will demonstrate results from this project using a version of Hyperband, which is a well known hyperparameter optimization algorithm, and the deep learning frameworks Keras and TensorFlow, all running on Greenplum database using Apache MADlib. Other topics will include architecture, scalability results and bright opportunities for the future."
Watch the video: https://wp.me/p3RLHQ-lsQ
Learn more: https://fosdem.org/2020/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Data Parallel Deep Learning
In this deck, Huihuo Zheng from Argonne National Laboratory presents: Data Parallel Deep Learning.
"The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two weeks of 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-lsl
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Making Supernovae with Jets
In this deck from DOE CSGF 2019, Chelsea Harris from the University of Michigan presents: Making Supernovae with Jets.
"Supernovae are the explosions of stars. One reason they are fundamentally important is they create and disperse elements heavier than carbon throughout the universe. Different stars explode in different ways, but the most common supernova type is from massive stars (greater than 10 times the mass of the sun) whose cores collapse to form a neutron star or black hole – "core collapse supernovae" or CC SNe. Even among massive stars, though, there are differences that can affect the outcome of core collapse. I am specifically interested in progenitors whose cores are rotating and magnetic (magnetorotational). Such cores may experience instabilities after collapse that launch a fast jet, which could rescue r-process elements formed near the proto-neutron star from destruction. The instabilities can also add power to the explosion and relieve tension between observations and theory. In addition to running simulations with existing FLASH code modules, I am developing a FLASH hydrodynamics module, SparkJoy, to perform these simulations at high order. These projects are part of a DOE INCITE project to explore progenitor effects on CC SNe and of the DOE SciDAC program "Towards Exascale Astrophysics of Mergers and Supernovae," a nationwide collaboration of supernova theorists unprecedented in its collaborative scale. Research like mine is made much easier at Michigan State University through the Department of Computational Mathematics, Science, and Engineering which, like the DOE CSGF, brings together members from different areas to share knowledge and strengthen each other's research."
Watch the video: https://wp.me/p3RLHQ-lr0
Learn more: https://www.krellinst.org/csgf/conf/2019/video/charris
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Adaptive Linear Solvers and Eigensolvers
In this deck from ATPESC 2019, Jack Dongarra from UT Knoxville presents: Adaptive Linear Solvers and Eigensolvers.
"Success in large-scale scientific computations often depends on algorithm design. Even the fastest machine may prove to be inadequate if insufficient attention is paid to the way in which the computation is organized. We have used several problems from computational physics to illustrate the importance of good algorithms, and we offer some very general principles for designing algorithms. Two subthemes are, first, the strong connection between the algorithm and the architecture of the target machine; and second, the importance of non-numerical methods in scientific computations."
Watch the video: https://wp.me/p3RLHQ-lq3
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Scientific Applications and Heterogeneous Architectures
In this deck from ATPESC 2019, Michela Taufer from UT Knoxville presents: Scientific Applications and Heterogeneous Architectures.
"This talk discusses two emerging trends in computing (i.e., the convergence of data generation and analytics, and the emergence of edge computing) and how these trends can impact heterogeneous applications. Next-generation supercomputers, with their extremely heterogeneous resources and dramatically higher performance than current systems, will generate more data than we need or, even, can handle. At the same time, more and more data is generated at the “edge,” requiring computing and storage to move closer and closer to data sources. The coordination of data generation and analysis across the spectrum of heterogonous systems including supercomputers, cloud computing, and edge computing adds additional layers of heterogeneity to applications’ workflows. More importantly, the coordination can neither rely on manual, centralized approaches as it is predominately done today in HPC nor exclusively be delegated to be just a problem for commercial Clouds. This talk presents case studies of heterogenous applications in precision medicine and precision farming that expand scientist workflows beyond the supercomputing center and shed our reliance on large-scale simulations exclusively, for the sake of scientific discovery."
Watch the video: https://wp.me/p3RLHQ-lq2
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
SW/HW co-design for near-term quantum computing
In this deck from ATPESC 2019, Yunong Shi from the University of Chicago presents: SW/HW co-design for near-term quantum computing.
"The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two weeks of 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-lpv
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
FPGAs and Machine Learning
In this deck from ATPESC 2019, James Moawad and Greg Nash from Intel present: FPGAs and Machine Learning.
"Neural networks are inspired by biological systems, in particular the human brain. Through the combination of powerful computing resources and novel architectures for neurons, neural networks have achieved state-of-the-art results in many domains such as computer vision and machine translation. FPGAs are a natural choice for implementing neural networks as they can handle different algorithms in computing, logic, and memory resources in the same device. Faster performance comparing to competitive implementations as the user can hardcore operations into the hardware. Software developers can use the OpenCL device C level programming standard to target FPGAs as accelerators to standard CPUs without having to deal with hardware level design."
Watch the video: https://wp.me/p3RLHQ-lnc
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
and
https://www.intel.com/content/www/us/en/products/programmable/fpga.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Deep Learning State of the Art (2020)
In this video from the MIT Deep Learning Series, Lex Fridman presents: Deep Learning State of the Art (2020).
"This lecture is on the most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series."
Watch the video: https://wp.me/p3RLHQ-lng
Learn more: https://deeplearning.mit.edu/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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Cybersecurity and Risk Management and World-wide Standards
- 1. 11/24/18 MAXIMIZING DATA'S POTENTIAL 2019 April 02 HPC User Forum Meeting Cybersecurity and Risk Management and World-wide Standards Henry Newman CTO Seagate Government Solutions hsn@seagategov.com
- 2. 2 Agenda • Digital Disruption • Global Data Explosion • Market Transition to Security • Product Cybersecurity Scope • Security Certification and Standards • Manage / Mitigate Risks • Compliance and Certification Management • Product Security Operations • Summary
- 3. 3 To understand where we are going, it’s important to understand how we got here.
- 4. 4 Digital Disruption 1960 - 1970 Mainframe Centralized 1.0 1980 - 2000 Client-Server Distributed 2.0 2005 - 2020 Mobile-Cloud Centralized 3.0 2020 - Rise of the Edge Distributed END POINTS 10 million users 2 billion users 4.0 1+ trillion users 7 billion users
- 5. 55 GLOBAL DATA EXPLOSION The IDC Data Age 2025 report predicts massive volumes of data creation and a convergence of every industry utilizing the value of data. 163ZB 5
- 6. 6 Market Transition to Security is Occurring • Majority of data requires at least some form of protection • Actual amount of data protection falls far short • This gap presents an increasing industry need for security and privacy technologies, systems, and processes to address it • Substantial penalties for non- compliance Data created in 2025 that should be protected90% Amount that will actually be protected45% $7.9M* GDPR: 2-4% Annual Revenue • https://www.pcmag.com/news/362543/how-much-does-a-data-breach-cost • Average Cost of Data Breach in US from IBM and Ponemon study.
- 7. 7 Lines of Protection Cybersecurity Scope Enterprise Cybersecurity Integrated Assurance Management
- 8. 8 Enabling a Full Lifecycle Data Security Model Cybersecurity Scope Manage Risk
- 9. 9 Security Certification and Standards Trusted Life-Cycle Security Algorithms Crypto Module Security Functionality Secure Data Disposal Trusted Data Erasure & Privacy Security By-Design Trusted Cryptography Authentic Security & Products Datasphere Protection
- 10. 10 Security Algorithm Certifications Security Algorithms • Standard and Trusted Security Algorithms • Certifications of all algorithms o Data Encryption o Integrity & Signatures o Random # Generation o Key Derivation… • Required for FIPS 140-2 & Common Criteria Certs Cryptographic Algorithm Validation Program (CAVP) Trusted Cryptography
- 11. 11 Security Module Certifications: FIPS 140-2 Crypto Module • Fundamental Security Certification • Evaluation by Independent Labs • Required for Information Security Products in Sensitive and Unclassified space in US & Canada • Value recognized in other geographies Security By- Design Cryptographic Module Validation Program (CMVP)
- 12. 12 Security Module Certifications: Common Criteria (CC) Security Functionality • Security Use-Case (Protection Profile) Certification • Evaluation by Independent Labs • Certification recognized by 28 member nations globally for Information Security acquisition Datasphere Protection Common Criteria for Information Security Evaluation (CC)
- 13. 13 Sanitization Standard Secure Data Disposal • NIST SP 800-88 (Federal) & ISO 27040 (International) define media sanitization • NIST SP 800-57 Defines Crypto Algorithm Longevity for erasure assurance. Trusted Data Disposal & Privacy NIST Special Pub 800-88 ISO 27040 NIST Special Pub 800-57
- 14. 14 Trusted Life-Cycle Standards Trusted Life- Cycle • The Open Trusted Technology Provider Standard (O-TTPS) is now a sanctioned ISO Standard • Comprehensive Secure Technology Provider Standard • Sections for Secure Technology Development and Secure Supply Chain • The NIST Cybersecurity Framework Provides for common framework and language for managing Cyber Risk Authentic Security & Products Cybersecurity Framework ISO 20243 Trusted Tech Provider Standard
- 15. 15 Product Cybersecurity Scope Mitigate Risk Policy-based compliance aligned to OTTPS, ISO and the NIST Cybersecurity Framework (CSF) Policies • Product Development Policy • Product Development 3rd Parties Maturity Staircase to Cybersecurity Compliance • Gap Analysis • Conformance • Certification Preparation • Certification Scalable to Trusted Product Lifecycle • Design, Source, Manufacture, Deliver, Service • Product Security Operations Center (PSOC) • Product Security Incident Response Team (PSIRT) Transparent Compliance and Incident Response Management Integrated Assurance Management
- 16. 16 Product Security: Manage Risk Maturity Staircase Based Policy Compliance • Product Development Policy • Product Development 3rd Parties Identify Protect Detect Respond RecoverPolicies Gap Analysis Conformance Certification Preparation Certification Increasing Levels of Compliance Maturity
- 17. 17 Product Security: Certification Trusted Product Life Cycle Certification ISO 20243 Certify Design Source Manufacture Deliver Service Identify Protect Detect Respond Recover
- 18. 18 Certified Erase - Strong Data Protection Assurance NIST Special Pub 800-88 ISO 27040NIST Special Pub 800-57 ✓ Defines Strong Media Sanitization ✓ Defines Security Requirements ✓ Defines Erase Certificate. App. D Cryptographic Module Validation Program (CMVP) Cryptographic Algorithm Validation Program (CAVP) ✓ Independent Lab Validation ✓ Validates 800-88 Security Rqmts ✓ Public Online Policy & Certificate Common Criteria for Information Security Evaluation (CC) ✓ Independent Lab Validation ✓ Validates 800-88 Data Erasure ✓ Public Online Policy & Certificate EE – Encryption Engine Profile AA – Authorization Acquisition Profile ✓ Essential & Certified By Design ✓ Trusted Design & Life-cycle ✓ Verifiable HW Roots of Trust ISO 20243 Trusted Tech Provider Standard
- 19. 19 Summary Integrity Tainted Product Tampered Product Security Life-Cycle IP Leakage Over-privileged Access & Lack of Controls Confidentiality B2B Network Trust B2B Security Assurance Control / Detection Availability Secure Product Product Security Managed Risk in Digital Enterprises