In this deck, John Gustafson presents: An Energy Efficient and Massively Parallel Approach to Valid Numerics.
"Written by one of the foremost experts in high-performance computing and the inventor of Gustafson’s Law, The End of Error: Unum Computing explains a new approach to computer arithmetic: the universal number (unum). The unum encompasses all IEEE floating-point formats as well as fixed-point and exact integer arithmetic. This new number type obtains more accurate answers than floating-point arithmetic yet uses fewer bits in many cases, saving memory, bandwidth, energy, and power."
Watch the video presentation: http://wp.me/p3RLHQ-dTk
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Keynote from Intellifest 2012 addressing the differences between narrow (classical) Artificial Intelligence and Artificial General Intelligence. Implications of cloud computing for AGI are also discussed.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.
Keynote from Intellifest 2012 addressing the differences between narrow (classical) Artificial Intelligence and Artificial General Intelligence. Implications of cloud computing for AGI are also discussed.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Traditional supervised approaches would require a strong assumption about what is normal and what not plus a non negligible effort in labeling the training dataset. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the data without requiring data labels. In this talk we will review a few popular techniques used in shallow machine learning and propose two semi-supervised approaches for novelty detection: one based on reconstruction error and another based on lower-dimensional feature compression.
TinyML: Machine Learning for MicrocontrollersRobert John
My presentation at TensorFlow User Groups Sub-Saharan Africa Summit discusses machine learning for embedded devices, the importance, and the challenges.
Introduction to AIoT & TinyML - with ArduinoAndri Yadi
On last March 21, 2020, we participated in worldwide Arduino Day 2020 and organized the online event for Bandung, Indonesia. This is the deck I delivered for my talk and demo.
Paper multi-modal biometric system using fingerprint , face and speechAalaa Khattab
Biometric system is often not able to meet the desired performance requirements.
In order to enable a biometric system to operate effectively in different applications and environments, a multimodal biometric system is preferred.
In this paper introduce a multimodal biometric system which integrates fingerprint verification , face recognition and speaker verification.
https://mcv-m6-video.github.io/deepvideo-2019/
This lecture provides an overview how the temporal information encoded in video sequences can be exploited to learn visual features from a self-supervised perspective. Self-supervised learning is a type of unsupervised learning in which data itself provides the necessary supervision to estimate the parameters of a machine learning algorithm.
Master in Computer Vision Barcelona 2019.
http://pagines.uab.cat/mcv/
Beyond Floating Point – Next Generation Computer Arithmeticinside-BigData.com
John Gustafson from the National University of Singapore presented this talk at Stanford.
“A new data type called a “posit” is designed for direct drop-in replacement for IEEE Standard 754 floats. Unlike unum arithmetic, posits do not require interval-type mathematics or variable size operands, and they round if an answer is inexact, much the way floats do. However, they provide compelling advantages over floats, including simpler hardware implementation that scales from as few as two-bit operands to thousands of bits. For any bit width, they have a larger dynamic range, higher accuracy, better closure under arithmetic operations, and simpler exception-handling. For example, posits never overflow to infinity or underflow to zero, and there is no “Not-a-Number” (NaN) value. Posits should take up less space to implement in silicon than an IEEE float of the same size. With fewer gate delays per operation as well as lower silicon footprint, the posit operations per second (POPS) supported by a chip can be significantly higher than the FLOPs using similar hardware resources. GPU accelerators, in particular, could do more arithmetic per watt and per dollar yet deliver superior answer quality.”
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Watch the video presentation: http://wp.me/p3RLHQ-gjH
In this deck, the Radio Free HPC team looks at the future of Operating Systems in the new world of computing.
Listen to the Podcast: http://www.radiofreehpc.com/wp/?p=686
TinyML: Machine Learning for MicrocontrollersRobert John
My presentation at TensorFlow User Groups Sub-Saharan Africa Summit discusses machine learning for embedded devices, the importance, and the challenges.
Introduction to AIoT & TinyML - with ArduinoAndri Yadi
On last March 21, 2020, we participated in worldwide Arduino Day 2020 and organized the online event for Bandung, Indonesia. This is the deck I delivered for my talk and demo.
Paper multi-modal biometric system using fingerprint , face and speechAalaa Khattab
Biometric system is often not able to meet the desired performance requirements.
In order to enable a biometric system to operate effectively in different applications and environments, a multimodal biometric system is preferred.
In this paper introduce a multimodal biometric system which integrates fingerprint verification , face recognition and speaker verification.
https://mcv-m6-video.github.io/deepvideo-2019/
This lecture provides an overview how the temporal information encoded in video sequences can be exploited to learn visual features from a self-supervised perspective. Self-supervised learning is a type of unsupervised learning in which data itself provides the necessary supervision to estimate the parameters of a machine learning algorithm.
Master in Computer Vision Barcelona 2019.
http://pagines.uab.cat/mcv/
Beyond Floating Point – Next Generation Computer Arithmeticinside-BigData.com
John Gustafson from the National University of Singapore presented this talk at Stanford.
“A new data type called a “posit” is designed for direct drop-in replacement for IEEE Standard 754 floats. Unlike unum arithmetic, posits do not require interval-type mathematics or variable size operands, and they round if an answer is inexact, much the way floats do. However, they provide compelling advantages over floats, including simpler hardware implementation that scales from as few as two-bit operands to thousands of bits. For any bit width, they have a larger dynamic range, higher accuracy, better closure under arithmetic operations, and simpler exception-handling. For example, posits never overflow to infinity or underflow to zero, and there is no “Not-a-Number” (NaN) value. Posits should take up less space to implement in silicon than an IEEE float of the same size. With fewer gate delays per operation as well as lower silicon footprint, the posit operations per second (POPS) supported by a chip can be significantly higher than the FLOPs using similar hardware resources. GPU accelerators, in particular, could do more arithmetic per watt and per dollar yet deliver superior answer quality.”
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Watch the video presentation: http://wp.me/p3RLHQ-gjH
In this deck, the Radio Free HPC team looks at the future of Operating Systems in the new world of computing.
Listen to the Podcast: http://www.radiofreehpc.com/wp/?p=686
La Social TV: caratteristiche ed evoluzione sul second screenEmanuela Zaccone
Cos'è la Social TV?
Quali sono state le sue evoluzioni?
Questo il tema del mio workshop sul tema Social Video per il Creativity Day 2012 (Roma, 28 marzo 2012)
BM NeXtScale - the next generation of dense computing
transtec HPC solutions with IBM NeXtScale are highly dense systems for those workloads that are currently the fastest growing, such as social media, analytics, technical computing and cloud applications. NeXtScale has been developed with standard components and provides up to three times as many cores in a one-unit rack unit when compared to previous versions.
The increasing use of this workload and delivery model generates increased demands on data centres. Operators are on the look-out for new technologies that can deal with the current demands with the highest possible level of performance and the lowest possible level of power consumption. NeXtScale is the latest addition to the transtec IBM x86 portfolio: Developed to allow applications with the power of a "supercomputer" to run in data centres – via a simple, flexible and open architecture.
Session four of my series on many cores turns to data, both big and small. Looks at MapReduce but approaches sideways from a classic computer science perspective.
Lessons Learned From Four Years of API Management Implementation Success at UnumCA Technologies
Delivering secure, convenient access to financial protection benefits for over 80,000 employers, including a third of Fortune 500 companies, requires the careful choreography of more than 140 applications, services and mobile experiences. To address this rapid growth, Unum embarked early on an API gateway strategy that has resulted in faster adoption, better security and reduced risks. Join Tom Porterfield, Sr. Software Engineer at Unum, as he shares lessons learned from four years of implementation success with API management solutions from CA Technologies.
For more information, please visit http://cainc.to/Nv2VOe
Open Ethernet: an open-source approach to modern network designAlexander Petrovskiy
The era of closed proprietary hardware platforms is coming to an end. Today, in the world of Web-scale IT, the industry is starting to adopt new approach, based on the principles of openness, scalabilty and customizability. However, in more conservative networking industry, traditional equipment and proprietary technologies from a single vendor are often being used, which limits the flexibility, prevents innovation and narrows down the choice.
The "Open Ethernet" initiative from Mellanox brings open source principles into the world of modern networking and allows customers to select the best hardware and software to design network infrastructure, based on open and standard protocols and technologies, also opening the way for broad adoption of SDN.
In this deck, the Radio Free HPC team reviews the results from SC16 Student Cluster Competition.
Watch the video presentation: http://wp.me/p3RLHQ-g2G
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In this deck from the HPC User Forum in Tucson, Earl Joseph from IDC presents: 2016 IDC HPC Market Update.
Watch the video presentation: http://wp.me/p3RLHQ-fbt
Learn more: http://hpcuserforum.com
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Beating Floating Point at its Own Game: Posit Arithmeticinside-BigData.com
In this deck from the HPC Advisory Council Australia Conference, John Gustafson from Singapore National University presents: Beating Floats at Their Own Game.
"Dr. Gustafson has recently finished writing a book, The End of Error: Unum Computing, that presents a new approach to computer arithmetic: the unum. The universal number, or unum format, encompasses all IEEE floating-point formats as well as fixed-point and exact integer arithmetic. This approach obtains more accurate answers than floating-point arithmetic yet uses fewer bits in many cases, saving memory, bandwidth, energy, and power."
Learn more: http://johngustafson.net/
Watch the video: https://wp.me/p3RLHQ-h8F
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Smaller and Easier: Machine Learning on Embedded ThingsNUS-ISS
Machine learning, meet things. Embedded machine learning is the blend of Machine Learning with Internet of Things and Edge Computing. This talk will cover recent topics in the Embedded Machine Learning field that have made it easier for anyone to deploy ML on small devices. We'll look at: TinyML, Edge Impulse, and Eloquent Arduino.
04 accelerating dl inference with (open)capi and posit numbersYutaka Kawai
This was presented by Louis Ledoux and Marc Casas at OpenPOWER summit EU 2019. The original one is uploaded at:
https://static.sched.com/hosted_files/opeu19/1a/presentation_louis_ledoux_posit.pdf
Sean Kandel - Data profiling: Assessing the overall content and quality of a ...huguk
The task of “data profiling”—assessing the overall content and quality of a data set—is a core aspect of the analytic experience. Traditionally, profiling was a fairly cut-and-dried task: load the raw numbers into a stat package, run some basic descriptive statistics, and report the output in a summary file or perhaps a simple data visualization. However, data volumes can be so large today that traditional tools and methods for computing descriptive statistics become intractable; even with scalable infrastructure like Hadoop, aggressive optimization and statistical approximation techniques must be used. In this talk Sean will cover technical challenges in keeping data profiling agile in the Big Data era. He will discuss both research results and real-world best practices used by analysts in the field, including methods for sampling, summarizing and sketching data, and the pros and cons of using these various approaches.
Sean is Trifacta’s Chief Technical Officer. He completed his Ph.D. at Stanford University, where his research focused on user interfaces for database systems. At Stanford, Sean led development of new tools for data transformation and discovery, such as Data Wrangler. He previously worked as a data analyst at Citadel Investment Group.
Invited paper from the Computers & Graphics Journal Presented at the SMI 2023 Conference in Genoa, Italy on July 4th 2023. Abstract: Highly complex and dense models of 3D objects have recently become indispensable in digital industries. Mesh decimation then plays a crucial role in the production pipeline to efficiently get visually convincing yet compact expressions of complex meshes. However, the current pipeline typically does not allow artists control the decimation process, just a simplification rate. Thus a preferred approach in production settings splits the process into a first pass of saliency detection highlighting areas of greater detail, and allowing artists to iterate until satisfied before simplifying the model. We propose a novel, efficient multi-scale method to compute mesh saliency at coarse and finer scales, based on fast mesh entropy of local surface measurements. Unlike previous approaches, we ensure a robust and straightforward calculation of mesh saliency even for densely tessellated models with millions of polygons. Moreover, we introduce a new adaptive subsampling and interpolation algorithm for saliency estimation. Our implementation achieves speedups of up to three orders of magnitude over prior approaches. Experimental results showcase its resilience to problem scenarios that efficiently scales up to process multi-million vertex meshes. Our evaluation with artists in the entertainment industry also demonstrates its applicability to real use-case scenarios.
Authors:
Rafael Kuffner dos Anjos, Leeds, UK
Richard Andrew Roberts and Benjamin Allen, VUW, NZ,
Joaquim Jorge, INESC-ID, U Lisboa, PT
Ken Anjyo, OLM, JP
Erich Elsen, Research Scientist, Baidu Research at MLconf NYC - 4/15/16MLconf
Training Recurrent Neural Networks at Scale: One of our projects at Baidu’s Silicon Valley AI Lab is using deep learning to develop state of the art end-to-end speech recognition systems based on recurrent neural networks for multiple languages. The training set for each language is multiple terabytes in size and each model requires in excess of 10 Exaflops to train. Training such models requires scale and techniques that are unusual for deep learning but more common in high performance computing. I will talk about the challenges involved and the software and hardware solutions that we employ.
Hadoop Summit 2012 | Bayesian Counters AKA In Memory Data Mining for Large Da...Cloudera, Inc.
Processing of large data requires new approaches to data mining: low, close to linear, complexity and stream processing. While in the traditional data mining the practitioner is usually presented with a static dataset, which might have just a timestamp attached to it, to infer a model for predicting future/takeout observations, in stream processing the problem is often posed as extracting as much information as possible on the current data to convert them to an actionable model within a limited time window. In this talk I present an approach based on HBase counters for mining over streams of data, which allows for massively distributed processing and data mining. I will consider overall design goals as well as HBase schema design dilemmas to speed up knowledge extraction process. I will also demo efficient implementations of Naive Bayes, Nearest Neighbor and Bayesian Learning on top of Bayesian Counters.
Hardware fails, applications fail, our code... well, it fails too (at least mine). To prevent software failure we test. Hardware failures are inevitable, so we write code that tolerates them, then we test. From tests we gather metrics and act upon them by improving parts that perform inadequately. Measuring right things at right places in an application is as much about good engineering practices and maintaining SLAs as it is about end user experience and may differentiate successful product from a failure.
In order to act on performance metrics such as max latency and consistent response times we need to know their accurate value. The problem with such metrics is that when using popular tools we get results that are not only inaccurate but also too optimistic.
During my presentation I will simulate services that require monitoring and show how gathered metrics differ from real numbers. All this while using what currently seems to be most popular metric pipeline - Graphite together with metrics.dropwizard.io library - and get completely false results. We will learn to tune it and get much better accuracy. We will use JMeter to measure latency and observe how falsely reassuring the results are. Finally I will show how HdrHistogram helps in gathering reliable metrics. We will also run tests measuring performance of different metric classes.
In this work we propose a novel architecture for real-time classification based on the combination of a Reservoir and a decision tree. This combination makes classification fast, reduces the number of hyper-parameters and keeps the good temporal properties of recurrent neural networks.
The capabilities of the proposed architecture to learn some typical string-based functions with strong temporal dependences are evaluated in the paper. The paper shows how the new architecture is able to incrementally learn these functions in real-time with fast adaptation to unknown sequences and analyzes the influence of the reduced number of hyper-parameters in the behaviour of the proposed solution.
Similar to Unum Computing: An Energy Efficient and Massively Parallel Approach to Valid Numerics (20)
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
Learn more: http://orionx.net
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
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Preparing to program Aurora at Exascale - Early experiences and future direct...inside-BigData.com
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
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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
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The Incorporation of Machine Learning into Scientific Simulations at Lawrence...inside-BigData.com
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...inside-BigData.com
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 ...inside-BigData.com
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
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 Monitoringinside-BigData.com
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
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
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
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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
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
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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
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 Accelerationinside-BigData.com
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 Efficientlyinside-BigData.com
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
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 computinginside-BigData.com
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
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
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.