This webinar by Dov Nimratz (Senior Solution Architect, Consultant, GlobalLogic) was delivered at Embedded Community Webinar #1 on July 7, 2020.
Webinar agenda:
- CPU / GPU / TPU architectures
- Historical context
- CPU and their variations
- GPU or gin in a bottle for artificial intelligence tasks
- TPU architecture specialized artificial intelligence accelerator
- What's next in technology
More details and presentation: https://www.globallogic.com/ua/about/events/embedded-community-webinar-1/
X13 Products + Intel® Xeon® CPU Max Series–An Applications & Performance ViewRebekah Rodriguez
With Intel’s Jan 10th launch of the Intel® Xeon® Max CPU series – the industry’s first with high bandwidth memory (HBM) enabled CPU – Supermicro is proud to discuss its complete range of first-to-market X13 servers with high bandwidth memory. This Supermicro Systems, Applications, and Performance webinar shows how Supermicro’s Green Compute approach is the best solution for customers wanting to get more performance per watt, lowering CAPEX and OPEX spending.
Join us as we highlight our server solutions optimized for customer applications and for scale-out configurations that drive higher compute density in today’s modern data centers, along with some real performance improvements.
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
This webinar by Dov Nimratz (Senior Solution Architect, Consultant, GlobalLogic) was delivered at Embedded Community Webinar #1 on July 7, 2020.
Webinar agenda:
- CPU / GPU / TPU architectures
- Historical context
- CPU and their variations
- GPU or gin in a bottle for artificial intelligence tasks
- TPU architecture specialized artificial intelligence accelerator
- What's next in technology
More details and presentation: https://www.globallogic.com/ua/about/events/embedded-community-webinar-1/
X13 Products + Intel® Xeon® CPU Max Series–An Applications & Performance ViewRebekah Rodriguez
With Intel’s Jan 10th launch of the Intel® Xeon® Max CPU series – the industry’s first with high bandwidth memory (HBM) enabled CPU – Supermicro is proud to discuss its complete range of first-to-market X13 servers with high bandwidth memory. This Supermicro Systems, Applications, and Performance webinar shows how Supermicro’s Green Compute approach is the best solution for customers wanting to get more performance per watt, lowering CAPEX and OPEX spending.
Join us as we highlight our server solutions optimized for customer applications and for scale-out configurations that drive higher compute density in today’s modern data centers, along with some real performance improvements.
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
In this deck, Yuichiro Ajima from Fujitsu presents: The Tofu Interconnect D.
"Through the development of post-K, which will be equipped with this CPU, Fujitsu will contribute to the resolution of social and scientific issues in such computer simulation fields as cutting-edge research, health and longevity, disaster prevention and mitigation, energy, as well as manufacturing, while enhancing industrial competitiveness and contributing to the creation of Society 5.0 by promoting applications in big data and AI fields."
Learn more: https://insidehpc.com/2018/08/fujitsu-unveils-details-post-k-supercomputer-processor-powered-arm/
and
http://www.fujitsu.com/jp/solutions/business-technology/tc/catalog/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Short Survey on the current state of Field-programmable gate array usage in Deep learning by several companies like Intel Nervana and Google's TPU (tensor processing units) vs GPU usage in terms of energy consumption and performance.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
Supermicro’s Universal GPU: Modular, Standards Based and Built for the FutureRebekah Rodriguez
The Universal GPU system architecture combines the latest technologies that support multiple GPU form factors, CPU choices, storage, and networking options.Together, these components are optimized to deliver high performance in a balanced architecture in a highly scalable system. Systems can be optimized for each customer’s specific Artificial Intelligence (AI), Machine Learning (ML), or High Performance Computing (HPC) applications. Organizations worldwide are demanding new options for their future computing environments, which have the thermal headroom for the next generation of CPUs and GPUs.
Join this webinar to learn how to leverage Supermicro's Universal GPU system to simplify customer deployments, deliver ultimate modularity and customization options for AI to Omniverse environments.
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
In this deck from the UK HPC Conference, Gunter Roeth from NVIDIA presents: Hardware & Software Platforms for HPC, AI and ML.
"Data is driving the transformation of industries around the world and a new generation of AI applications are effectively becoming programs that write software, powered by data, vs by computer programmers. Today, NVIDIA’s tensor core GPU sits at the core of most AI, ML and HPC applications, and NVIDIA software surrounds every level of such a modern application, from CUDA and libraries like cuDNN and NCCL embedded in every deep learning framework and optimized and delivered via the NVIDIA GPU Cloud to reference architectures designed to streamline the deployment of large scale infrastructures."
Watch the video: https://wp.me/p3RLHQ-l2Y
Learn more: http://nvidia.com
and
http://hpcadvisorycouncil.com/events/2019/uk-conference/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Join us for an exciting and informative preview of the broadest range of next-generation systems optimized for tomorrow’s data center workloads, Powered by 4th Gen Intel® Xeon® Scalable Processors (formerly codenamed Sapphire Rapids).
Experts from Supermicro and Intel will discuss how the upcoming Supermicro X13 systems will enable new performance levels utilizing state-of-the-art technology, including DDR5, PCIe 5.0, Compute Express Link™ 1.1, and Intel® Advanced Matrix Extensions (Intel AMX).
Modular by Design: Supermicro’s New Standards-Based Universal GPU ServerRebekah Rodriguez
In this webinar, members of the Server Solution Team as well as a member of Supermicro’s Product Office will discuss Supermicro’s Universal GPU Server, the server’s modular, standards-based design, the important role of OCP Accelerator Module (OAM) form factor, and Universal Baseboard (UBB) in the system, as well as touching on AMD's next generation HPC accelerator. In addition, we will get some insights into trends in the HPC and AI/Machine Learning space, including the different software platforms and best practices that are driving innovation in our industry and daily lives. In particular: • Tools to enable use of the high performance hardware for HPC and Deep Learning applications • Tools to enable use of multiple GPUs, including RDMA, to solve highly demanding HPC and deep learning models, such as BERT • Running applications in containers with AMD’s next generation GPU system
NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.
Intel® QuickAssist Technology Introduction, Applications, and Lab, Including ...Michelle Holley
Abstract: Intel® QuickAssist Technology improves performance and efficiency across the data center and other computing platforms by handling the compute-intensive operations of bulk cryptography, public key cryptography, and data compression. In this course, we will give an overview of the technology along with the summary of resources to get started with integrating Intel® QAT into your platform solutions. We will also demonstrate using Intel® QAT with applications such as OpenSSL, NGINX, and HAProxy, with a hands-on lab.
Speaker Bios:
Joel Auernheimer, a Platform Application Engineer at Intel, has been focused on enabling customers to integrate Intel® QuickAssist Technology in their platform solutions. Joel is a native of Phoenix, Arizona and enjoys hiking, basketball, soccer, singing, and spending time with friends and family.
Joel Schuetze has been with Intel since 1996. For the last 9+ years he has worked as Platform Application Engineer supporting customers with Intel QuickAssist Technology.
The Supermicro X12 product line, powered by 3rd Gen Intel® Xeon® Scalable processors, contains many innovations that gives organizations more performance for a variety of workloads.
Join this webinar to learn more about the outstanding performance you can get by using Supermicro X12 servers and storage systems using the latest technologies from Intel®.
Watch the webinar: https://www.brighttalk.com/webcast/17278/514618
AMD has been away from the HPC space for a while, but now they are coming back in a big way with an open software approach to GPU computing. The Radeon Open Compute Platform (ROCm) was born from the Boltzman Initiative announced last year at SC15. Now available on GitHub, the ROCm Platform bringing a rich foundation to advanced computing by better integrating the CPU and GPU to solve real-world problems.
"We are excited to present ROCm, the first open-source HPC/ultrascale-class platform for GPU computing that’s also programming-language independent. We are bringing the UNIX philosophy of choice, minimalism and modular software development to GPU computing. The new ROCm foundation lets you choose or even develop tools and a language run time for your application."
Watch the video presentation: http://wp.me/p3RLHQ-fJT
Learn more: https://radeonopencompute.github.io/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Spark and Deep Learning frameworks with distributed workloadsS N
The increasing complexity of learning algorithms and deep neural networks, combined with size of data and parameters, has made it challenging to exploit existing large-scale data processing pipelines for training and inference.
Approaches are outlined for preprocessing, training, inference, and deployment across datasets that leverage Spark, its extended ecosystem of libraries, and deep learning frameworks.
In this deck, Yuichiro Ajima from Fujitsu presents: The Tofu Interconnect D.
"Through the development of post-K, which will be equipped with this CPU, Fujitsu will contribute to the resolution of social and scientific issues in such computer simulation fields as cutting-edge research, health and longevity, disaster prevention and mitigation, energy, as well as manufacturing, while enhancing industrial competitiveness and contributing to the creation of Society 5.0 by promoting applications in big data and AI fields."
Learn more: https://insidehpc.com/2018/08/fujitsu-unveils-details-post-k-supercomputer-processor-powered-arm/
and
http://www.fujitsu.com/jp/solutions/business-technology/tc/catalog/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Short Survey on the current state of Field-programmable gate array usage in Deep learning by several companies like Intel Nervana and Google's TPU (tensor processing units) vs GPU usage in terms of energy consumption and performance.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
Supermicro’s Universal GPU: Modular, Standards Based and Built for the FutureRebekah Rodriguez
The Universal GPU system architecture combines the latest technologies that support multiple GPU form factors, CPU choices, storage, and networking options.Together, these components are optimized to deliver high performance in a balanced architecture in a highly scalable system. Systems can be optimized for each customer’s specific Artificial Intelligence (AI), Machine Learning (ML), or High Performance Computing (HPC) applications. Organizations worldwide are demanding new options for their future computing environments, which have the thermal headroom for the next generation of CPUs and GPUs.
Join this webinar to learn how to leverage Supermicro's Universal GPU system to simplify customer deployments, deliver ultimate modularity and customization options for AI to Omniverse environments.
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
In this deck from the UK HPC Conference, Gunter Roeth from NVIDIA presents: Hardware & Software Platforms for HPC, AI and ML.
"Data is driving the transformation of industries around the world and a new generation of AI applications are effectively becoming programs that write software, powered by data, vs by computer programmers. Today, NVIDIA’s tensor core GPU sits at the core of most AI, ML and HPC applications, and NVIDIA software surrounds every level of such a modern application, from CUDA and libraries like cuDNN and NCCL embedded in every deep learning framework and optimized and delivered via the NVIDIA GPU Cloud to reference architectures designed to streamline the deployment of large scale infrastructures."
Watch the video: https://wp.me/p3RLHQ-l2Y
Learn more: http://nvidia.com
and
http://hpcadvisorycouncil.com/events/2019/uk-conference/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Join us for an exciting and informative preview of the broadest range of next-generation systems optimized for tomorrow’s data center workloads, Powered by 4th Gen Intel® Xeon® Scalable Processors (formerly codenamed Sapphire Rapids).
Experts from Supermicro and Intel will discuss how the upcoming Supermicro X13 systems will enable new performance levels utilizing state-of-the-art technology, including DDR5, PCIe 5.0, Compute Express Link™ 1.1, and Intel® Advanced Matrix Extensions (Intel AMX).
Modular by Design: Supermicro’s New Standards-Based Universal GPU ServerRebekah Rodriguez
In this webinar, members of the Server Solution Team as well as a member of Supermicro’s Product Office will discuss Supermicro’s Universal GPU Server, the server’s modular, standards-based design, the important role of OCP Accelerator Module (OAM) form factor, and Universal Baseboard (UBB) in the system, as well as touching on AMD's next generation HPC accelerator. In addition, we will get some insights into trends in the HPC and AI/Machine Learning space, including the different software platforms and best practices that are driving innovation in our industry and daily lives. In particular: • Tools to enable use of the high performance hardware for HPC and Deep Learning applications • Tools to enable use of multiple GPUs, including RDMA, to solve highly demanding HPC and deep learning models, such as BERT • Running applications in containers with AMD’s next generation GPU system
NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.
Intel® QuickAssist Technology Introduction, Applications, and Lab, Including ...Michelle Holley
Abstract: Intel® QuickAssist Technology improves performance and efficiency across the data center and other computing platforms by handling the compute-intensive operations of bulk cryptography, public key cryptography, and data compression. In this course, we will give an overview of the technology along with the summary of resources to get started with integrating Intel® QAT into your platform solutions. We will also demonstrate using Intel® QAT with applications such as OpenSSL, NGINX, and HAProxy, with a hands-on lab.
Speaker Bios:
Joel Auernheimer, a Platform Application Engineer at Intel, has been focused on enabling customers to integrate Intel® QuickAssist Technology in their platform solutions. Joel is a native of Phoenix, Arizona and enjoys hiking, basketball, soccer, singing, and spending time with friends and family.
Joel Schuetze has been with Intel since 1996. For the last 9+ years he has worked as Platform Application Engineer supporting customers with Intel QuickAssist Technology.
The Supermicro X12 product line, powered by 3rd Gen Intel® Xeon® Scalable processors, contains many innovations that gives organizations more performance for a variety of workloads.
Join this webinar to learn more about the outstanding performance you can get by using Supermicro X12 servers and storage systems using the latest technologies from Intel®.
Watch the webinar: https://www.brighttalk.com/webcast/17278/514618
AMD has been away from the HPC space for a while, but now they are coming back in a big way with an open software approach to GPU computing. The Radeon Open Compute Platform (ROCm) was born from the Boltzman Initiative announced last year at SC15. Now available on GitHub, the ROCm Platform bringing a rich foundation to advanced computing by better integrating the CPU and GPU to solve real-world problems.
"We are excited to present ROCm, the first open-source HPC/ultrascale-class platform for GPU computing that’s also programming-language independent. We are bringing the UNIX philosophy of choice, minimalism and modular software development to GPU computing. The new ROCm foundation lets you choose or even develop tools and a language run time for your application."
Watch the video presentation: http://wp.me/p3RLHQ-fJT
Learn more: https://radeonopencompute.github.io/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Spark and Deep Learning frameworks with distributed workloadsS N
The increasing complexity of learning algorithms and deep neural networks, combined with size of data and parameters, has made it challenging to exploit existing large-scale data processing pipelines for training and inference.
Approaches are outlined for preprocessing, training, inference, and deployment across datasets that leverage Spark, its extended ecosystem of libraries, and deep learning frameworks.
Presentation I gave at the SORT Conference in 2011. Was generalized from some work I had done with using GPUs to accelerate image processing at FamilySearch.
Distributed DNN training: Infrastructure, challenges, and lessons learnedWee Hyong Tok
Deep learning is revolutionizing a wide range of applications across various industries and in organizations of all sizes. Scalable DNN training is critical to the success of large-scale deep learning. The methodologies, tools, and infrastructure in this space are rapidly evolving. Drawing on their experiences building a multitenant, distributed DNN training infrastructure that uses familiar OSS components to execute Docker container-based deep learning workloads from hundreds of AI applications on clusters with thousands of GPUs, Kaarthik Sivashanmugam and Wee Hyong Tok share recommendations to address the common challenges in enabling scalable and efficient distributed DNN training and the lessons learned in building and operating a large-scale training infrastructure. Kaarthik and Wee Hyong introduce the challenges in distributed DNN training and provide an overview of the components that can enable distributed training on bare metal infrastructure, virtual machines, and containers. In addition, they outline practical tips for running deep learning workloads on Kubernetes clusters on Azure and explain how you can leverage deep learning toolkits (e.g., CNTK, TensorFlow) on these clusters to do distributed training.
microfluidics Neuralink hopes to use its microchips to treat conditions such as paralysis and blindness, and to help certain disabled people use computers and mobile technology. The chips - which have been tested in monkeys - are designed to interpret signals produced in the brain and relay information to devices via Bluetooth
Design Considerations, Installation, and Commissioning of the RedRaider Cluster at the Texas Tech University
High Performance Computing Center
Outline of this talk
HPCC Staff and Students
Previous clusters
• History, Performance, usage Patterns, and Experience
Motivation for Upgrades
• Compute Capacity Goals
• Related Considerations
Installation and Benchmarks Conclusions and Q&A
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...Chester Chen
Machine Learning at the Limit
John Canny, UC Berkeley
How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms.
Bio
John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.
Ariel Waizel discusses the Data Plane Development Kit (DPDK), an API for developing fast packet processing code in user space.
* Who needs this library? Why bypass the kernel?
* How does it work?
* How good is it? What are the benchmarks?
* Pros and cons
Ariel worked on kernel development at the IDF, Ben Gurion University, and several companies. He is interested in networking, security, machine learning, and basically everything except UI development. Currently a Solution Architect at ConteXtream (an HPE company), which specializes in SDN solutions for the telecom industry.
Mike Pittaro - High Performance Hardware for Data Analysis PyData
Choosing hardware for big data analysis is difficult because of the many options and variables involved. The problem is more complicated when you need a full cluster for big data analytics.
This session will cover the basic guidelines and architectural choices involved in choosing analytics hardware for Spark and Hadoop. I will cover processor core and memory ratios, disk subsystems, and network architecture. This is a practical advice oriented session, and will focus on performance and cost tradeoffs for many different options.
Assisting User’s Transition to Titan’s Accelerated Architectureinside-BigData.com
Oak Ridge National Lab is home of Titan, the largest GPU accelerated supercomputer in the world. This fact alone can be an intimidating experience for users new to leadership computing facilities. Our facility has collected over four years of experience helping users port applications to Titan. This talk will explain common paths and tools to successfully port applications, and expose common difficulties experienced by new users. Lastly, learn how our free and open training program can assist your organization in this transition.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
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
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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
2. Authors
• Norman P. Jouppi (first
author)
– Distinguished Engineer at Google
– Lead designer of several
microprocessors and graphics
accelerator
• David Patterson (fourth
author)
– Father of “RISC”
Ref: https://www.computer.org/web/awards/goode-norman-jouppi
3. Neural Networks
• Application
– MLP, CNN, RNN represent 95% of NN inference workload
in Google datacenters
– Each model needs 5M ~ 100M weights
• Hardware
– TPU has 25 time as many MACs and 3.5 times as much on-chip
memory as the K80 GPU
5. Origin
• Requirement
– DNNs might double computation demands
– Quickly produce a custom ASIC for inference
• Definition
– Coprocessor on the PCIE, plug into existing servers
– More like FPU (floating-point unit) than GPU
7. Architecture
• Matrix Multiply Unit
– Contains 256 x 256 MACs, can perform 8-bit multiply-and-
adds
– Designed for dense matrices
• Off-chip 8GiB DRAM (Weight Memory)
– Read-only (different from Global Memory of GPU)
– Supports many simultaneously active models
• Instruction Set
– Traditional CISC
– Read_Host_Memory/Read_Weights/MatrixMultiply/Convol
ve/Activate etc.
– 4-stage pipeline
10. Implementation
• Flows
– Data flows from the left (Unified Buffer)
– Weights are loaded from the top (Weight FIFO, 8GiB
DDR3 DRAM)
• Systolic System
– A network of processors which rhythmically compute and
pass data through the system
• Software Stack
– User Space Library and Kernel Driver (like Nvidia-GPU)
15. Discussion
• Fallacy: K80 GPU is a good match to inference
“GPUs have traditionally been seen as high-throughput
architectures that reply on high-bandwidth DRAM and thousands of
threads to achieve their goals”
16. Conclusion
• Advantage
– K80 GPU: 2496 32-bit, 8Mib on-chip memory
TPU: 65536 8-bit, 28Mib on-chip memory
– TPU leverages its advantage in MACs and on-chip
memory
– TPU succeeded because of the large matrix multiply
unit
17. Q1: Why don’t use TPU for training
• TPU’s on-chip 8GiB DRAM is read-only
– CPU paid a lot for synchronous operations on RAM
– Large mount of GPUs will lower the cost for single
chip
• GPU have more “parallel” performance
– Could train two small-model or a large mount of
samples at the same time
18. Q2: Why TPU faster?
• Application Specific Instruction Set
– Intel CPU (CISC) need decoding, out-of-order,
branch-prediction, SMT etc.
– GPU was optimized for “Parallel” rather than “Matrix”
• Read-only on-chip memory
• TensorRT makes GPU-inference much faster
19. GPU grows faster and faster
https://blogs.nvidia.com/blog/2017/04/10/ai-drives-rise-accelerated-computing-datacenter/
20. Q3: TPU or FPGA?
• They looks like the same
– By programming, FPGA could have similar
Matrix-Multiply-Unit
– FPGA could also have “read-only” on-chip memory
• Making a utterly new chip is a high-risk task
– AMD
– Calxeda
– Fusionio