Podcasts about Parallel computing

Czy Unreal Engine jest wielowątkowy? Tak! W takim razie dlaczego prasa krajowa i zagraniczna pisze inaczej? Pozwólcie że wyjaśnię.ANKIETA GADŻETOWA: https://forms.gle/xWeRqchaUgBCM8519Patronite Okiem Deva:https://patronite.pl/okiemdevaDiscord Okiem Deva:https://discord.gg/4anD7dJJn8Linki: https://linktr.ee/okiemdevaKompilacja ofert pracy:https://docs.google.com/document/d/1agVeF3cW5aK4skDnHnB0IVwmma7n1rD8Sm9btTCTFdw/edit?usp=sharingPytania do następnego live'ahttps://forms.gle/4rEHhLdoiPUDWdtE7Unreal Engine a wielowątkowość00:00:00 Intro00:01:19 Kontekst sprawy00:17:24 Odrobina historii00:26:04 Unreal Engine i wielowątkowość00:38:43 Podsumowanie00:43:00 Outro----------------Wspierający:Spotify: AdamP, Kozak12, JJanekWichowskiIntern Dev: Piotr ŁypJunior Dev: Kasia Nie, Krzysztof Mierzejewski, HARDCOROWYKOŚCIU GAMEPLAY!, Konrad Szejnfeld (ko_sz), Aloxxx, GerWanTPL, Saaellor, Marcin Pietrzyk (Mithrandir), Mateusz Salach, kondi16, Kruszynka Izka, Khart, nothing, Tomek “Tomku” Mar, Anna Weglarz, ExitWound, Kojo Bojo, ZajacccXD, Ania Węglarzy, FiSherMan414, MichalDev, Aliwera, Lucyfer, KopkooRegular Dev: Leniwa Ola, Marcin “kowboj_czacza” Ignasiak, Adam Kabalak, Backgroundcharacter_A, Kaspa Anonim, Mateusz Stolarz Stolarczyk, czekająca na cud, kona 1, PabloRal, R W, scooby666, Mat Heus (Sizuae), Bonga, vxd555, Remigiusz Maciaszek, Jakub Staniszewski, Ari Gold, bibruRG_78, Łukasz Korzanowski, Artur Loska, Andrzej JanickiSenior Dev: Michał G, Magdalena Porath, Mateusz Śródka, Rafał Jaszczuk, Krzysztof Szczech, Kornel Kisielewicz, Marek Leśniak, Jan Skiba, Arek Dudkowski, Aleksander Świątek, Gatki, Michał Kondzior, Michał Moskal, DamianRoman, Ard, Jakub WmH, Robert Jurasz, Mateusz Myga, HARDCOROWYKOŚCIU GAMEPLAY!, Wered, Michał Król, Magdalena Płoszaj-Kotynia, Krzysiek Prus, Łukasz Klejnberg, Bartosz Majcherczak, Agata Pławna, Patryk Bzdyra, Veman, Arkadiusz Czeryna, Jakub Kornatowski (@GramyNaMacu), Agnieszka Rumińska, Michał Stankiewicz, Cezary Łysoń, Mariusz Kowalski, Tek, Maxjestic, Buła Losu, Łukasz Kister, Regito93, KovalGames, Piotr Beyer, Pielgrzym, Tomasz Golik, Arti “Niezłomny”, jmozgawa, Mateusz Kozielecki, JayKob, Piteroix, Warsi, enonemasta, Miras, Daniel Zabłocki, Pawelek1329, Morfiniusz, Mateusz Lokś, Szymon Góraj, Wojciech Wojciechowski, keksimus maximus, Arashito, Bonga, BloszamelowyJacuś, JOORDAN, BrzusPrincipal Dev: Wojciech Uziębło, Dawid Kuc, Zuzanna Lepianka, Mariolka Mazur, Leszek Lisowski, Tomasz Kowalczykhttps://workplays.it/----------------Źródła:https://youtu.be/477qF6QNSvchttps://wccftech.com/first-unreal-engine-6-info-shared-by-epics-tim-sweeney-preview-versions-in-2-3-years-goal-is-to-go-multithreaded/https://www.gry-online.pl/newsroom/unreal-engine-6-ma-zmienic-podejscie-do-tworzenia-gier-szef-epica/zf2d5d0https://futurebeat.pl/newsroom/my-uzywamy-tylko-jednego-rdzenia-epic-przyznaje-sie-do-ograniczen/z72d5d8https://github.com/donaldwuid/unreal_source_explained/blob/master/main/main.mdhttps://dev.epicgames.com/community/learning/tutorials/VkLD/unreal-engine-multi-process-rendering-introductionhttps://dev.epicgames.com/documentation/en-us/unreal-engine/threaded-rendering-in-unreal-enginehttps://unrealcommunity.wiki/multi-threading:-how-to-create-threads-in-ue4-0bsy2g96https://www.xda-developers.com/multi-core-cpus-modern-games/https://www.siberoloji.com/the-evolution-of-computer-processors-from-single-core-to-multi-core-and-beyond/https://www.techspot.com/article/2363-multi-core-cpu/https://www.anandtech.com/show/1645/3https://www.gamedeveloper.com/programming/threading-3d-game-engine-basicshttps://wiki.cdot.senecapolytechnic.ca/wiki/GPU621/History_of_Parallel_Computing_and_Multi-core_Systemshttps://en.wikipedia.org/wiki/POWER4

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Danny Hillis is an inventor, scientist, author, and engineer. While completing his doctorate at MIT, he pioneered the parallel computers that are the basis for the processors used for AI and most high-performance computer chips. He is now a founding partner with Applied Invention, working on new ideas in cybersecurity, medicine, and agriculture.Kevin Kelly is the founding executive editor of WIRED magazine, the former editor and publisher of the Whole Earth Review, and a bestselling author of books on technology and culture, including Excellent Advice for Living. Subscribe to Kevin's newsletter, Recomendo, at recomendo.com. Sponsors:Momentous high-quality supplements: https://livemomentous.com/tim (code TIM for 20% off)Eight Sleep's Pod 4 Ultra sleeping solution for dynamic cooling and heating: https://eightsleep.com/tim (save between $400 and $600 on the Pod 4 Ultra)AG1 all-in-one nutritional supplement: https://DrinkAG1.com/Tim (1-year supply of Vitamin D (and 5 free AG1 travel packs) with your first subscription purchase.)*For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissPast guests on The Tim Ferriss Show include Jerry Seinfeld, Hugh Jackman, Dr. Jane Goodall, LeBron James, Kevin Hart, Doris Kearns Goodwin, Jamie Foxx, Matthew McConaughey, Esther Perel, Elizabeth Gilbert, Terry Crews, Sia, Yuval Noah Harari, Malcolm Gladwell, Madeleine Albright, Cheryl Strayed, Jim Collins, Mary Karr, Maria Popova, Sam Harris, Michael Phelps, Bob Iger, Edward Norton, Arnold Schwarzenegger, Neil Strauss, Ken Burns, Maria Sharapova, Marc Andreessen, Neil Gaiman, Neil de Grasse Tyson, Jocko Willink, Daniel Ek, Kelly Slater, Dr. Peter Attia, Seth Godin, Howard Marks, Dr. Brené Brown, Eric Schmidt, Michael Lewis, Joe Gebbia, Michael Pollan, Dr. Jordan Peterson, Vince Vaughn, Brian Koppelman, Ramit Sethi, Dax Shepard, Tony Robbins, Jim Dethmer, Dan Harris, Ray Dalio, Naval Ravikant, Vitalik Buterin, Elizabeth Lesser, Amanda Palmer, Katie Haun, Sir Richard Branson, Chuck Palahniuk, Arianna Huffington, Reid Hoffman, Bill Burr, Whitney Cummings, Rick Rubin, Dr. Vivek Murthy, Darren Aronofsky, Margaret Atwood, Mark Zuckerberg, Peter Thiel, Dr. Gabor Maté, Anne Lamott, Sarah Silverman, Dr. Andrew Huberman, and many more.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of InTechnology, Camille gets into parallel computing with Pradeep Dubey, Intel Senior Fellow at Intel Labs. They talk about how parallel computing works, why it's becoming more necessary, how it uses AI and machine learning to process large amounts of data, the challenges of designing systems and architecture for parallel computing, how machines can help humans make better decisions, and much more. The views and opinions expressed are those of the guests and author and do not necessarily reflect the official policy or position of Intel Corporation.

Today's guest is theoretical computer scientist Leslie Valiant - currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University. Among his many accolades, Leslie was awarded the Turing Award in 2010 for transformative contributions to the theory of computation, including the theory of PAC learning which stands for Probably Approximately Correct, as well as the complexity of enumeration and of algebraic computation, and the theory of parallel and distributed computing.In this episode, Leslie and I discuss his life and career journey – from what problems he has looked to solve in his career to how his PAC theory was first received and his latest book, The Importance of Being Educable.Have you ever wondered what your digital footprint says about you? Or curious how you can make your pitch stand out?Then check out WhiteBridge.ai – it's an AI-powered digital identity research tool that finds, verifies, and analyzes publicly collected data about someone and structures it into an insightful report.They actually ran a report on me and I was seriously impressed!But not only can you use it to check your online digital profile but you could use it to help you quickly research and understand other people whether it's a potential client, employee or investor – the report gives you more than enough useful info on the person for you to truly personalize your correspondence to them and help you build that early rapport.Want to learn more? Head to https://whitebridge.ai and use my discount code DANIELLE30 for 30% off your first report.Please enjoy my conversation with Leslie Valiant.

Today's guest is Tamiko Thiel – lead product designer of The Connection Machine – a revolutionary massively parallel artificial intelligence supercomputer which was developed in the 1980s. Originally conceived by Danny Hillis from MIT's artificial intelligence lab where he was studying under Marvin Minsky, Danny got an incredibly talented team together including Richard Feynman, Brewster Kale, Tamiko, and others to create what would become the fastest and most effective supercomputer of the time. And it's this part of her career that we focus on today.However, Tamiko went on to become a pioneering digital artist who has worked in the realm of virtual reality for the past thirty years, starting in 1994 when she worked with Steven Spielberg on the Starbright World project where they created an online interactive 3D virtual world for seriously ill children.Tamiko also received a Bachelor of Science degree in Product Design Engineering, from Stanford University in 1979 and received a Masters in Mechanical Engineering from MIT in 1983, with a focus on human-machine design and computer graphics, as well as a diploma from the Academy of fine arts in Munich, Germany. In today's conversation we dig deep into that special time in history when all the so-called experts said what Danny, Tamiko and co. were working on at Thinking Machines couldn't be done and where… they proved them all wrong.Enjoy!--------------Image of Tamiko copyright Tamiko ThielTamiko website / LinkedIn /  InstagramI am not on social media this year but stay in touch via my Newsletter / YouTube--------------Tamiko in London March 2024The Travels of Mariko Horo interactive virtual reality installationBy Tamiko Thiel, 2006/2017, with original music by Ping JinIn "GLoW: ILLUMINATING INNOVATION"Bush House Arcade, King's College, Strand, LondonExhibition: 08 March - 20 April 2024Panel and opening event: 07 March, 6:30pmLocation: Great Hall, King's Building, Strand, King's College LondonThe CM-1 t-shirt and Tamiko's Travels of Mariko Horo mesh top will be shown in the following, with information on how to order them (from my web shops: http://tamikothiel.com/cm/cm-tshirt.html)Curiosity Cabinet, King's College171 Strand/Corner of Surrey St., Londonhttps://www.kcl.ac.uk/news/curiosity-cabinet-showcases-antiquities-and-oddities-on-the-strand

This episode features an interview between Matt Trifiro and Wolfgang Gentzsch, Co-Founder and President of UberCloud. Wolfgang is a passionate engineer, computer scientist, and entrepreneur with 30 years of experience working in engineering simulations, high-performance computing, scientific research, university teaching, and the software industry, from hands-on practices to expert consulting to leadership positions. He is an entrepreneur with six successful startups in Germany and the US, in engineering, high-performance computing, and cloud. Wolfgang is a member of numerous conference program, steering, and organizing committees, with 50+ keynote speaker appointments.In this episode, Wolfgang tells us about the early days of network computing and how the grid was the predecessor to the cloud. He describes how advancements in connectivity and processing power can lead to revolutionary changes in everything from technology to healthcare. Wolfgang also explains what he thinks edge computing is today, and how his company is working to help democratize access to computing power in the cloud that was previously too expensive or too complex for most organizations to use.---------Key Quotes:“Definitely, the grid was the predecessor of the cloud. And that's why there is not a real huge difference in both. The cloud infrastructure was completely virtualized and therefore fully automated and now I use that word democratized because almost everybody was able to use cloud resources then; which you couldn't easily say about grid. The grid was really for specialists in research centers.” “You can innovate at your fingertips these days. You don't have to build, you know, 2, 3, 4, 5 models and crash them against the wall. Now you do it in the cloud, which might cost a thousand dollars or $5,000 even, but it's much, much, much, cheaper. So, there are tons of benefits these days when you move to the cloud.” “Now HPC is really in the hands of everybody. For engineers and scientists a few decades ago it was only given into the hands of specialists, and that door is open for so many new applications,  making any kind of research or products basically coming out much faster with exponential acceleration, which will continue tol help us to solve problems, real problems. It's I mean, like in healthcare, for example, or climate and weather forecast, and also new technologies like electrical cars, autonomous driving, and all that stuff. So, I mean it is successfully making our lives even more convenient, more comfortable, and also solving mankind problems which we are facing.”---------Show Timestamps:(01:45) Getting involved in technology(03:05) Difference between Scaler and Vector Computers  (07:45) Conversion of Parallel Computing and the Internet (13:00) Network Computing and the Cloud  (19:45) Convergence of Grid and Cloud Computing (23:45) High Performance Computing and Super Computing (28:15) Difference Between the Cloud and High Performance Computing(30:45) Uber Cloud (39:45) Living Heart Valve Project (41:45) Uber Cloud Project Example(46:45) Growth of High Performance Computing and the Edge (53:05) Future of the Cloud(55:15) Is the Network or the Internet the Computer?(60:30) What's Exciting in the Future--------Sponsor:Over the Edge is brought to you by Dell Technologies - unlock the potential of your infrastructure with edge solutions. From hardware and software to data and operations, across your entire multi-cloud environment, we're here to help you simplify your edge so you can generate more value. Learn more by visiting DellTechnologies.com/SimplifyYourEdge for more information or click on the link in the show notes.--------Links:Follow Matt on TwitterConnect with Wolfgang on LinkedInwww.CaspianStudios.com

In our latest Electronic Specifier Insights podcast, we spoke to Jos Martin, Director Of Engineering - Cloud Integration and Parallel Computing at MathWorks about the application of AI  

Parallel Computing with Dask and Coiled Python makes data science and machine learning accessible to millions of people around the world. However, historically Python hasn't handled parallel computing well, which leads to issues as researchers try to tackle problems on increasingly large datasets. Dask is an open source Python library that enables the existing Python data science stack (Numpy, Pandas, Scikit-Learn, Jupyter, ...) with parallel and distributed computing. Today Dask has been broadly adopted by most major Python libraries, and is maintained by a robust open source community across the world. This talk discusses parallel computing generally, Dask's approach to parallelizing an existing ecosystem of software, and some of the challenges we've seen in deploying distributed systems. Finally, we also addressed the challenges of robustly deploying distributed systems, which ends up being one of the main accessibility challenges for users today. We hope that by the end of the meetup attendees will better understand parallel computing, have built intuition around how Dask works, and have the opportunity to play with their own Dask cluster on the cloud. Matthew is an open source software developer in the numeric Python ecosystem. He maintains several PyData libraries, but today focuses mostly on Dask a library for scalable computing. Matthew worked for Anaconda Inc for several years, then built out the Dask team at NVIDIA for RAPIDS, and most recently founded Coiled Computing to improve Python's scalability with Dask for large organizations. Matthew has given talks at a variety of technical, academic, and industry conferences. A list of talks and keynotes is available at (https://matthewrocklin.com/talks). Matthew holds a bachelor’s degree from UC Berkeley in physics and mathematics, and a PhD in computer science from the University of Chicago. Check out our posts here to get more context around where we're coming from: https://medium.com/coiled-hq/coiled-dask-for-everyone-everywhere-376f5de0eff4 https://medium.com/coiled-hq/the-unbearable-challenges-of-data-science-at-scale-83d294fa67f8 ----------- Connect With Us ✌️------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/ Connect with Matthew on LinkedIn: https://www.linkedin.com/in/matthew-rocklin-461b4323/

Springer has put out a ton of awesome textbooks, and they made over 500 of them available for free download, including a couple dozen tech ebooks!An Introduction to Machine Learning, 2nd ed. 2017 by Miroslav KubatAutomata and Computability, 1997 by Dexter C. KozenComputational Geometry, 3rd ed. 2008 by Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark OvermarsComputer Vision, 2011 by Richard SzeliskiConcise Guide to Databases, 2013 by Peter Lake, Paul CrowtherConcise Guide to Software Engineering, 1st ed. 2017 by Gerard O'ReganCryptography Made Simple, 1st ed. 2016 by Nigel SmartData Mining, 2015 by Charu C. AggarwalData Structures and Algorithms with Python, 2015 by Kent D. Lee, Steve HubbardDigital Image Processing, 2nd ed. 2016 by Wilhelm Burger, Mark J. BurgeEye Tracking Methodology, 3rd ed. 2017 by Andrew T. DuchowskiFoundations for Designing User-Centered Systems, 2014 by Frank E. Ritter, Gordon D. Baxter, Elizabeth F. ChurchillFoundations of Programming Languages, 2nd ed. 2017 by Kent D. LeeFundamentals of Business Process Management, 2013 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersFundamentals of Multimedia, 2nd ed. 2014 by Ze-Nian Li, Mark S. Drew, Jiangchuan LiuGuide to Competitive Programming, 1st ed. 2017 by Antti LaaksonenGuide to Computer Network Security, 4th ed. 2017 by Joseph Migga KizzaGuide to Discrete Mathematics, 1st ed. 2016 by Gerard O'ReganIntroduction to Artificial Intelligence, 2nd ed. 2017 by Wolfgang ErtelIntroduction to Data Science, 1st ed. 2017 by Laura Igual, Santi SeguíIntroduction to Deep Learning, 1st ed. 2018 by Sandro SkansiIntroduction to Evolutionary Computing, 2nd ed. 2015 by A.E. Eiben, J.E. SmithLaTeX in 24 Hours, 1st ed. 2017 by Dilip DattaModelling Computing Systems, 2013 by Faron Moller, Georg StruthObject-Oriented Analysis, Design and Implementation, 2nd ed. 2015 by Brahma Dathan, Sarnath RamnathPrinciples of Data Mining, 3rd ed. 2016 by Max BramerProbability and Statistics for Computer Science, 1st ed. 2018 by David ForsythPython Programming Fundamentals, 2nd ed. 2014 by Kent D. LeeRecommender Systems, 1st ed. 2016 by Charu C. AggarwalThe Algorithm Design Manual, 2nd ed. 2008 by Steven S SkienaThe Data Science Design Manual, 1st ed. 2017 by Steven S. SkienaThe Python Workbook, 2014 by Ben StephensonUML @ Classroom, 2015 by Martina Seidl, Marion Scholz, Christian Huemer, Gerti KappelUnderstanding Cryptography, 2010 by Christof Paar, Jan PelzlFundamentals of Business Process Management, 2nd ed. 2018 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersGuide to Scientific Computing in C++, 2nd ed. 2017 by Joe Pitt-Francis, Jonathan WhiteleyFundamentals of Java Programming, 1st ed. 2018 by Mitsunori OgiharaLogical Foundations of Cyber-Physical Systems, 1st ed. 2018 by André PlatzerNeural Networks and Deep Learning, 1st ed. 2018 by Charu C. AggarwalSystems Programming in Unix/Linux, 1st ed. 2018 by K.C. WangIntroduction to Parallel Computing, 1st ed. 2018 by Roman Trobec, Boštjan Slivnik, Patricio Bulić, Borut RobičAnalysis for Computer Scientists, 2nd ed. 2018 by Michael Oberguggenberger, Alexander OstermannIntroductory Computer Forensics, 1st ed. 2018 by Xiaodong Linhttps://www.springernature.com/gp/librarians/the-link/blog/blogposts-ebooks/free-access-to-a-range-of-essential-textbooks/17855960

Dr. Didem Unat, the first computer scientist in Turkey to receive an ERC award and recipient of several other awards*. She leads ParCoreLab on high-performance computing at Koc. They work on designing programming models for computer architectures as well as optimizing deep learning frameworks such as Tensorflow and DeepGraph Library for speed.

Springer has put out a ton of awesome textbooks, and they made over 500 of them available for free download, including a couple dozen tech ebooks!An Introduction to Machine Learning, 2nd ed. 2017 by Miroslav KubatAutomata and Computability, 1997 by Dexter C. KozenComputational Geometry, 3rd ed. 2008 by Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark OvermarsComputer Vision, 2011 by Richard SzeliskiConcise Guide to Databases, 2013 by Peter Lake, Paul CrowtherConcise Guide to Software Engineering, 1st ed. 2017 by Gerard O'ReganCryptography Made Simple, 1st ed. 2016 by Nigel SmartData Mining, 2015 by Charu C. AggarwalData Structures and Algorithms with Python, 2015 by Kent D. Lee, Steve HubbardDigital Image Processing, 2nd ed. 2016 by Wilhelm Burger, Mark J. BurgeEye Tracking Methodology, 3rd ed. 2017 by Andrew T. DuchowskiFoundations for Designing User-Centered Systems, 2014 by Frank E. Ritter, Gordon D. Baxter, Elizabeth F. ChurchillFoundations of Programming Languages, 2nd ed. 2017 by Kent D. LeeFundamentals of Business Process Management, 2013 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersFundamentals of Multimedia, 2nd ed. 2014 by Ze-Nian Li, Mark S. Drew, Jiangchuan LiuGuide to Competitive Programming, 1st ed. 2017 by Antti LaaksonenGuide to Computer Network Security, 4th ed. 2017 by Joseph Migga KizzaGuide to Discrete Mathematics, 1st ed. 2016 by Gerard O'ReganIntroduction to Artificial Intelligence, 2nd ed. 2017 by Wolfgang ErtelIntroduction to Data Science, 1st ed. 2017 by Laura Igual, Santi SeguíIntroduction to Deep Learning, 1st ed. 2018 by Sandro SkansiIntroduction to Evolutionary Computing, 2nd ed. 2015 by A.E. Eiben, J.E. SmithLaTeX in 24 Hours, 1st ed. 2017 by Dilip DattaModelling Computing Systems, 2013 by Faron Moller, Georg StruthObject-Oriented Analysis, Design and Implementation, 2nd ed. 2015 by Brahma Dathan, Sarnath RamnathPrinciples of Data Mining, 3rd ed. 2016 by Max BramerProbability and Statistics for Computer Science, 1st ed. 2018 by David ForsythPython Programming Fundamentals, 2nd ed. 2014 by Kent D. LeeRecommender Systems, 1st ed. 2016 by Charu C. AggarwalThe Algorithm Design Manual, 2nd ed. 2008 by Steven S SkienaThe Data Science Design Manual, 1st ed. 2017 by Steven S. SkienaThe Python Workbook, 2014 by Ben StephensonUML @ Classroom, 2015 by Martina Seidl, Marion Scholz, Christian Huemer, Gerti KappelUnderstanding Cryptography, 2010 by Christof Paar, Jan PelzlFundamentals of Business Process Management, 2nd ed. 2018 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersGuide to Scientific Computing in C++, 2nd ed. 2017 by Joe Pitt-Francis, Jonathan WhiteleyFundamentals of Java Programming, 1st ed. 2018 by Mitsunori OgiharaLogical Foundations of Cyber-Physical Systems, 1st ed. 2018 by André PlatzerNeural Networks and Deep Learning, 1st ed. 2018 by Charu C. AggarwalSystems Programming in Unix/Linux, 1st ed. 2018 by K.C. WangIntroduction to Parallel Computing, 1st ed. 2018 by Roman Trobec, Boštjan Slivnik, Patricio Bulić, Borut RobičAnalysis for Computer Scientists, 2nd ed. 2018 by Michael Oberguggenberger, Alexander OstermannIntroductory Computer Forensics, 1st ed. 2018 by Xiaodong Linhttps://www.springernature.com/gp/librarians/the-link/blog/blogposts-ebooks/free-access-to-a-range-of-essential-textbooks/17855960

The Freya Distributed Compute Platform enables thousands or hundreds of thousands of computers to be harnessed to solve mammoth computations. The networks of computers can be donated voluntarily for good causes, or Data Centres can be used in their off peak time to deliver a new commercial Super Computer Service. We speak to Andy Ramgobin who explains the project and the team

Wolfgang Gentzsch is Co-founder and President of UberCloud. Together with Burak Yenier he founded UberCloud in 2014 to develop novel HPC technology to move complex engineering simulation workloads to the cloud. He was a professor of applied mathematics & computer science, working as a scientist at the Max Planck Institute and as Head of CFD department at the German Aerospace Center and he founded several companies in the Parallel Computing sector. From 2004 to 2008 Wolfgang was a member of US President's Council of Advisors for Science and Technology (PCAST). ————————————————————————————— Connect with me here: ✉️ My weekly email newsletter: jousef.substack.com

In this episode I talk with Matt Rocklin. Matt is best known for his work on Dask, a parallel computing package built into the PyData stack. After working on open source software at Anaconda and NVIDIA he now founded his own company centered around Dask called Coiled Computing. In this episode we talk about the insights into open source he gained through his career, what Dask is and how it is funded, and then of course his new company.Links:https://twitter.com/mrocklinhttps://dask.orghttps://coiled.iohttps://matthewrocklin.comhttps://rapids.aihttps://pangeo.orghttps://prefect.ioThanks to my Patrons for their support, especially:Daniel GerlancRichard CraibJonathan NgSupport me here to get early access: https://www.patreon.com/twiecki PyData is a registered trademark of NumFOCUS, Inc.Support the show (https://www.patreon.com/twiecki)

Artificial intelligence has been in the news for a long time. Robert J. Marks airs one of his older interviews with Jim French on KIRO Radio to show the similarity to today’s reporting on artificial intelligence. Mind Matters News appreciates the permission of Jim French and KIRO Radio in Seattle to rebroadcast this interview. Show Notes 01:08 | Do computers Read More › Source

Artificial intelligence has been in the news for a long time. Robert J. Marks airs one of his older interviews with Jim French on KIRO Radio to show the similarity to today’s reporting on artificial intelligence. Mind Matters News appreciates the permission of Jim French and KIRO Radio in Seattle to rebroadcast this interview. Show Notes 01:08 | Do computers… Source

丽莎老师讲机器人之CPU、GPU、TPU、NPU都是些什么之一欢迎收听丽莎老师讲机器人，想要孩子参加机器人竞赛、创意编程、创客竞赛的辅导，找丽莎老师！欢迎添加微信号：153 5359 2068，或搜索微信公众号：我最爱机器人。如今技术日新月异，物联网、人工智能、深度学习等概念遍地开花，各类芯片名词GPU, TPU, NPU,DPU,层出不穷......它们都是什么？与CPU又是什么关系？今天我们就来科普一下这些各种PU”！CPUCPU( Central Processing Unit, 中央处理器)就是机器的“大脑”，也是布局谋略、发号施令、控制行动的“总司令官”。CPU的结构主要包括运算器（ALU, Arithmetic and Logic Unit）、控制单元（CU, Control Unit）、寄存器（Register）、高速缓存器（Cache）和它们之间通讯的数据、控制及状态的总线。简单来说就是：计算单元、控制单元和存储单元，计算单元主要执行算术运算、移位等操作以及地址运算和转换；存储单元主要用于保存运算中产生的数据以及指令等；控制单元则对指令译码，并且发出为完成每条指令所要执行的各个操作的控制信号。所以一条指令在CPU中执行的过程是这样的：读取到指令后，通过指令总线送到控制器（黄色区域）中进行译码，并发出相应的操作控制信号；然后运算器（绿色区域）按照操作指令对数据进行计算，并通过数据总线将得到的数据存入数据缓存器（大块橙色区域）。CPU遵循的是冯诺依曼架构，其核心就是：存储程序，顺序执行。讲到这里，有没有看出问题，没错——在这个结构图中，负责计算的绿色区域占的面积似乎太小了，而橙色区域的缓存Cache和黄色区域的控制单元占据了大量空间。高中化学有句老生常谈的话叫：结构决定性质，放在这里也非常适用。因为CPU的架构中需要大量的空间去放置存储单元（橙色部分）和控制单元（黄色部分），相比之下计算单元（绿色部分）只占据了很小的一部分，所以它在大规模并行计算能力上极受限制，而更擅长于逻辑控制。另外，因为遵循冯诺依曼架构（存储程序，顺序执行），CPU就像是个一板一眼的管家，人们吩咐的事情它总是一步一步来做。但是随着人们对更大规模与更快处理速度的需求的增加，这位管家渐渐变得有些力不从心。于是，大家就想，能不能把多个处理器放在同一块芯片上，让它们一起来做事，这样效率不就提高了吗？没错，GPU便由此诞生了。GPU在正式讲解GPU之前，我们先来讲讲上文中提到的一个概念——并行计算。并行计算(Parallel Computing)是指同时使用多种计算资源解决计算问题的过程，是提高计算机系统计算速度和处理能力的一种有效手段。它的基本思想是用多个处理器来共同求解同一问题，即将被求解的问题分解成若干个部分，各部分均由一个独立的处理机来并行计算。并行计算可分为时间上的并行和空间上的并行。时间上的并行是指流水线技术，比如说工厂生产食品的时候分为四步：清洗-消毒-切割-包装。如果不采用流水线，一个食品完成上述四个步骤后，下一个食品才进行处理，耗时且影响效率。但是采用流水线技术，就可以同时处理四个食品。这就是并行算法中的时间并行，在同一时间启动两个或两个以上的操作，大大提高计算性能。空间上的并行是指多个处理机并发的执行计算，即通过网络将两个以上的处理机连接起来，达到同时计算同一个任务的不同部分，或者单个处理机无法解决的大型问题。比如小李准备在植树节种三棵树，如果小李1个人需要6个小时才能完成任务，植树节当天他叫来了好朋友小红、小王，三个人同时开始挖坑植树，2个小时后每个人都完成了一颗植树任务，这就是并行算法中的空间并行，将一个大任务分割成多个相同的子任务，来加快问题解决速度。所以说，如果让CPU来执行这个种树任务的话，它就会一棵一棵的种，花上6个小时的时间，但是让GPU来种树，就相当于好几个人同时在种。GPU全称为Graphics Processing Unit，图形处理器，就如它的名字一样，GPU最初是用在个人电脑、工作站、游戏机和一些移动设备（如平板电脑、智能手机等）上运行绘图运算工作的微处理器。为什么GPU特别擅长处理图像数据呢？这是因为图像上的每一个像素点都有被处理的需要，而且每个像素点处理的过程和方式都十分相似，也就成了GPU的天然温床。GPU的构成相对简单，有数量众多的计算单元和超长的流水线，特别适合处理大量的类型统一的数据。但GPU无法单独工作，必须由CPU进行控制调用才能工作。CPU可单独作用，处理复杂的逻辑运算和不同的数据类型，但当需要大量的处理类型统一的数据时，则可调用GPU进行并行计算。GPU的工作大部分都计算量大，但没什么技术含量，而且要重复很多很多次。就像你有个工作需要计算几亿次一百以内加减乘除一样，最好的办法就是雇上几十个小学生一起算，一人算一部分，反正这些计算也没什么技术含量，纯粹体力活而已；而CPU就像老教授，积分微分都会算，就是工资高，一个老教授资顶二十个小学生，你要是富士康你雇哪个？GPU就是用很多简单的计算单元去完成大量的计算任务，纯粹的人海战术。这种策略基于一个前提，就是小学生A和小学生B的工作没有什么依赖性，是互相独立的。但有一点需要强调，虽然GPU是为了图像处理而生的，但是我们通过前面的介绍可以发现，它在结构上并没有专门为图像服务的部件，只是对CPU的结构进行了优化与调整，所以现在GPU不仅可以在图像处理领域大显身手，它还被用来科学计算、密码破解、数值分析，海量数据处理（排序，Map-Reduce等），金融分析等需要大规模并行计算的领域。所以GPU也可以认为是一种较通用的芯片。

The Microchip Welcome to the History of Computing Podcast, where we explore the history of information technology. Because understanding the past prepares us for the innovations of the future! Todays episode is on the history of the microchip, or microprocessor. This was a hard episode, because it was the culmination of so many technologies. You don't know where to stop telling the story - and you find yourself writing a chronological story in reverse chronological order. But few advancements have impacted humanity the way the introduction of the microprocessor has. Given that most technological advances are a convergence of otherwise disparate technologies, we'll start the story of the microchip with the obvious choice: the light bulb. Thomas Edison first demonstrated the carbon filament light bulb in 1879. William Joseph Hammer, an inventor working with Edison, then noted that if he added another electrode to a heated filament bulb that it would glow around the positive pole in the vacuum of the bulb and blacken the wire and the bulb around the negative pole. 25 years later, John Ambrose Fleming demonstrated that if that extra electrode is made more positive than the filament the current flows through the vacuum and that the current could only flow from the filament to the electrode and not the other direction. This converted AC signals to DC and represented a boolean gate. In the 1904 Fleming was granted Great Britain's patent number 24850 for the vacuum tube, ushering in the era of electronics. Over the next few decades, researchers continued to work with these tubes. Eccles and Jordan invented the flip-flop circuit at London's City and Guilds Technical College in 1918, receiving a patent for what they called the Eccles-Jordan Trigger Circuit in 1920. Now, English mathematician George Boole back in the earlier part of the 1800s had developed Boolean algebra. Here he created a system where logical statements could be made in mathematical terms. Those could then be performed using math on the symbols. Only a 0 or a 1 could be used. It took awhile, John Vincent Atanasoff and grad student Clifford Berry harnessed the circuits in the Atanasoff-Berry computer in 1938 at Iowa State University and using Boolean algebra, successfully solved linear equations but never finished the device due to World War II, when a number of other technological advancements happened, including the development of the ENIAC by John Mauchly and J Presper Eckert from the University of Pennsylvania, funded by the US Army Ordinance Corps, starting in 1943. By the time it was taken out of operation, the ENIAC had 20,000 of these tubes. Each digit in an algorithm required 36 tubes. Ten digit numbers could be multiplied at 357 per second, showing the first true use of a computer. John Von Neumann was the first to actually use the ENIAC when they used one million punch cards to run the computations that helped propel the development of the hydrogen bomb at Los Alamos National Laboratory. The creators would leave the University and found the Eckert-Mauchly Computer Corporation. Out of that later would come the Univac and the ancestor of todays Unisys Corporation. These early computers used vacuum tubes to replace gears that were in previous counting machines and represented the First Generation. But the tubes for the flip-flop circuits were expensive and had to be replaced way too often. The second generation of computers used transistors instead of vacuum tubes for logic circuits. The integrated circuit is basically a wire set into silicon or germanium that can be set to on or off based on the properties of the material. These replaced vacuum tubes in computers to provide the foundation of the boolean logic. You know, the zeros and ones that computers are famous for. As with most modern technologies the integrated circuit owes its origin to a number of different technologies that came before it was able to be useful in computers. This includes the three primary components of the circuit: the transistor, resistor, and capacitor. The silicon that chips are so famous for was actually discovered by Swedish chemist Jöns Jacob Berzelius in 1824. He heated potassium chips in a silica container and washed away the residue and viola - an element! The transistor is a semiconducting device that has three connections that amplify data. One is the source, which is connected to the negative terminal on a battery. The second is the drain, and is a positive terminal that, when touched to the gate (the third connection), the transistor allows electricity through. Transistors then acts as an on/off switch. The fact they can be on or off is the foundation for Boolean logic in modern computing. The resistor controls the flow of electricity and is used to control the levels and terminate lines. An integrated circuit is also built using silicon but you print the pattern into the circuit using lithography rather than painstakingly putting little wires where they need to go like radio operators did with the Cats Whisker all those years ago. The idea of the transistor goes back to the mid-30s when William Shockley took the idea of a cat's wicker, or fine wire touching a galena crystal. The radio operator moved the wire to different parts of the crystal to pick up different radio signals. Solid state physics was born when Shockley, who first studied at Cal Tech and then got his PhD in Physics, started working on a way to make these useable in every day electronics. After a decade in the trenches, Bell gave him John Bardeen and Walter Brattain who successfully finished the invention in 1947. Shockley went on to design a new and better transistor, known as a bipolar transistor and helped move us from vacuum tubes, which were bulky and needed a lot of power, to first gernanium, which they used initially and then to silicon. Shockley got a Nobel Prize in physics for his work and was able to recruit a team of extremely talented young PhDs to help work on new semiconductor devices. He became increasingly frustrated with Bell and took a leave of absence. Shockley moved back to his hometown of Palo Alto, California and started a new company called the Shockley Semiconductor Laboratory. He had some ideas that were way before his time and wasn't exactly easy to work with. He pushed the chip industry forward but in the process spawned a mass exodus of employees that went to Fairchild in 1957. He called them the “Traitorous 8” to create what would be Fairchild Semiconductors. The alumni of Shockley Labs ended up spawning 65 companies over the next 20 years that laid foundation of the microchip industry to this day, including Intel. . If he were easier to work with, we might not have had the innovation that we've seen if not for Shockley's abbrasiveness! All of these silicon chip makers being in a small area of California then led to that area getting the Silicon Valley moniker, given all the chip makers located there. At this point, people were starting to experiment with computers using transistors instead of vacuum tubes. The University of Manchester created the Transistor Computer in 1953. The first fully transistorized computer came in 1955 with the Harwell CADET, MIT started work on the TX-0 in 1956, and the THOR guidance computer for ICBMs came in 1957. But the IBM 608 was the first commercial all-transistor solid-state computer. The RCA 501, Philco Transac S-1000, and IBM 7070 took us through the age of transistors which continued to get smaller and more compact. At this point, we were really just replacing tubes with transistors. But the integrated circuit would bring us into the third generation of computers. The integrated circuit is an electronic device that has all of the functional blocks put on the same piece of silicon. So the transistor, or multiple transistors, is printed into one block. Jack Kilby of Texas Instruments patented the first miniaturized electronic circuit in 1959, which used germanium and external wires and was really more of a hybrid integrated Circuit. Later in 1959, Robert Noyce of Fairchild Semiconductor invented the first truly monolithic integrated circuit, which he received a patent for. While doing so independently, they are considered the creators of the integrated circuit. The third generation of computers was from 1964 to 1971, and saw the introduction of metal-oxide-silicon and printing circuits with photolithography. In 1965 Gordon Moore, also of Fairchild at the time, observed that the number of transistors, resistors, diodes, capacitors, and other components that could be shoved into a chip was doubling about every year and published an article with this observation in Electronics Magazine, forecasting what's now known as Moore's Law. The integrated circuit gave us the DEC PDP and later the IBM S/360 series of computers, making computers smaller, and brought us into a world where we could write code in COBOL and FORTRAN. A microprocessor is one type of integrated circuit. They're also used in audio amplifiers, analog integrated circuits, clocks, interfaces, etc. But in the early 60s, the Minuteman missal program and the US Navy contracts were practically the only ones using these chips, at this point numbering in the hundreds, bringing us into the world of the MSI, or medium-scale integration chip. Moore and Noyce left Fairchild and founded NM Electronics in 1968, later renaming the company to Intel, short for Integrated Electronics. Federico Faggin came over in 1970 to lead the MCS-4 family of chips. These along with other chips that were economical to produce started to result in chips finding their way into various consumer products. In fact, the MCS-4 chips, which split RAM , ROM, CPU, and I/O, were designed for the Nippon Calculating Machine Corporation and Intel bought the rights back, announcing the chip in Electronic News with an article called “Announcing A New Era In Integrated Electronics.” Together, they built the Intel 4004, the first microprocessor that fit on a single chip. They buried the contacts in multiple layers and introduced 2-phase clocks. Silicon oxide was used to layer integrated circuits onto a single chip. Here, the microprocessor, or CPU, splits the arithmetic and logic unit, or ALU, the bus, the clock, the control unit, and registers up so each can do what they're good at, but live on the same chip. The 1st generation of the microprocessor was from 1971, when these 4-bit chips were mostly used in guidance systems. This boosted the speed by five times. The forming of Intel and the introduction of the 4004 chip can be seen as one of the primary events that propelled us into the evolution of the microprocessor and the fourth generation of computers, which lasted from 1972 to 2010. The Intel 4004 had 2,300 transistors. The Intel 4040 came in 1974, giving us 3,000 transistors. It was still a 4-bit data bus but jumped to 12-bit ROM. The architecture was also from Faggin but the design was carried out by Tom Innes. We were firmly in the era of LSI, or Large Scale Integration chips. These chips were also used in the Busicom calculator, and even in the first pinball game controlled by a microprocessor. But getting a true computer to fit on a chip, or a modern CPU, remained an elusive goal. Texas Instruments ran an ad in Electronics with a caption that the 8008 was a “CPU on a Chip” and attempted to patent the chip, but couldn't make it work. Faggin went to Intel and they did actually make it work, giving us the first 8-bit microprocessor. It was then redesigned in 1972 as the 8080. A year later, the chip was fabricated and then put on the market in 1972. Intel made the R&D money back in 5 months and sparked the idea for Ed Roberts to build The Altair 8800. Motorola and Zilog brought competition in the 6900 and Z-80, which was used in the Tandy TRS-80, one of the first mass produced computers. N-MOSs transistors on chips allowed for new and faster paths and MOS Technology soon joined the fray with the 6501 and 6502 chips in 1975. The 6502 ended up being the chip used in the Apple I, Apple II, NES, Atari 2600, BBC Micro, Commodore PET and Commodore VIC-20. The MOS 6510 variant was then used in the Commodore 64. The 8086 was released in 1978 with 3,000 transistors and marked the transition to Intel's x86 line of chips, setting what would become the standard in future chips. But the IBM wasn't the only place you could find chips. The Motorola 68000 was used in the Sun-1 from Sun Microsystems, the HP 9000, the DEC VAXstation, the Comodore Amiga, the Apple Lisa, the Sinclair QL, the Sega Genesis, and the Mac. The chips were also used in the first HP LaserJet and the Apple LaserWriter and used in a number of embedded systems for years to come. As we rounded the corner into the 80s it was clear that the computer revolution was upon us. A number of computer companies were looking to do more than what they could do with he existing Intel, MOS, and Motorola chips. And ARPA was pushing the boundaries yet again. Carver Mead of Caltech and Lynn Conway of Xerox PARC saw the density of transistors in chips starting to plateau. So with DARPA funding they went out looking for ways to push the world into the VLSI era, or Very Large Scale Integration. The VLSI project resulted in the concept of fabless design houses, such as Broadcom, 32-bit graphics, BSD Unix, and RISC processors, or Reduced Instruction Set Computer Processor. Out of the RISC work done at UC Berkely came a number of new options for chips as well. One of these designers, Acorn Computers evaluated a number of chips and decided to develop their own, using VLSI Technology, a company founded by more Fairchild Semiconductor alumni) to manufacture the chip in their foundry. Sophie Wilson, then Roger, worked on an instruction set for the RISC. Out of this came the Acorn RISC Machine, or ARM chip. Over 100 billion ARM processors have been produced, well over 10 for every human on the planet. You know that fancy new A13 that Apple announced. It uses a licensed ARM core. Another chip that came out of the RISC family was the SUN Sparc. Sun being short for Stanford University Network, co-founder Andy Bchtolsheim, they were close to the action and released the SPARC in 1986. I still have a SPARC 20 I use for this and that at home. Not that SPARC has gone anywhere. They're just made by Oracle now. The Intel 80386 chip was a 32 bit microprocessor released in 1985. The first chip had 275,000 transistors, taking plenty of pages from the lessons learned in the VLSI projects. Compaq built a machine on it, but really the IBM PC/AT made it an accepted standard, although this was the beginning of the end of IBMs hold on the burgeoning computer industry. And AMD, yet another company founded by Fairchild defectors, created the Am386 in 1991, ending Intel's nearly 5 year monopoly on the PC clone industry and ending an era where AMD was a second source of Intel parts but instead was competing with Intel directly. We can thank AMD's aggressive competition with Intel for helping to keep the CPU industry going along Moore's law! At this point transistors were only 1.5 microns in size. Much, much smaller than a cats whisker. The Intel 80486 came in 1989 and again tracking against Moore's Law we hit the first 1 million transistor chip. Remember how Compaq helped end IBM's hold on the PC market? When the Intel 486 came along they went with AMD. This chip was also important because we got L1 caches, meaning that chips didn't need to send instructions to other parts of the motherboard but could do caching internally. From then on, the L1 and later L2 caches would be listed on all chips. We'd finally broken 100MHz! Motorola released the 68050 in 1990, hitting 1.2 Million transistors, and giving Apple the chip that would define the Quadra and also that L1 cache. The DEC Alpha came along in 1992, also a RISC chip, but really kicking off the 64-bit era. While the most technically advanced chip of the day, it never took off and after DEC was acquired by Compaq and Compaq by HP, the IP for the Alpha was sold to Intel in 2001, with the PC industry having just decided they could have all their money. But back to the 90s, ‘cause life was better back when grunge was new. At this point, hobbyists knew what the CPU was but most normal people didn't. The concept that there was a whole Univac on one of these never occurred to most people. But then came the Pentium. Turns out that giving a chip a name and some marketing dollars not only made Intel a household name but solidified their hold on the chip market for decades to come. While the Intel Inside campaign started in 1991, after the Pentium was released in 1993, the case of most computers would have a sticker that said Intel Inside. Intel really one upped everyone. The first Pentium, the P5 or 586 or 80501 had 3.1 million transistors that were 16.7 micrometers. Computers kept getting smaller and cheaper and faster. Apple answered by moving to the PowerPC chip from IBM, which owed much of its design to the RISC. Exactly 10 years after the famous 1984 Super Bowl Commercial, Apple was using a CPU from IBM. Another advance came in 1996 when IBM developed the Power4 chip and gave the world multi-core processors, or a CPU that had multiple CPU cores inside the CPU. Once parallel processing caught up to being able to have processes that consumed the resources on all those cores, we saw Intel's Pentium D, and AMD's Athlon 64 x2 released in May 2005 bringing multi-core architecture to the consumer. This led to even more parallel processing and an explosion in the number of cores helped us continue on with Moore's Law. There are now custom chips that reach into the thousands of cores today, although most laptops have maybe 4 cores in them. Setting multi-core architectures aside for a moment, back to Y2K when Justin Timberlake was still a part of NSYNC. Then came the Pentium Pro, Pentium II, Celeron, Pentium III, Xeon, Pentium M, Xeon LV, Pentium 4. On the IBM/Apple side, we got the G3 with 6.3 million transistors, G4 with 10.5 million transistors, and the G5 with 58 million transistors and 1,131 feet of copper interconnects, running at 3GHz in 2002 - so much copper that NSYNC broke up that year. The Pentium 4 that year ran at 2.4 GHz and sported 50 million transistors. This is about 1 transistor per dollar made off Star Trek: Nemesis in 2002. I guess Attack of the Clones was better because it grossed over 300 Million that year. Remember how we broke the million transistor mark in 1989? In 2005, Intel started testing Montecito with certain customers. The Titanium-2 64-bit CPU with 1.72 billion transistors, shattering the billion mark and hitting a billion two years earlier than projected. Apple CEO Steve Jobs announced Apple would be moving to the Intel processor that year. NeXTSTEP had been happy as a clam on Intel, SPARC or HP RISC so given the rapid advancements from Intel, this seemed like a safe bet and allowed Apple to tell directors in IT departments “see, we play nice now.” And the innovations kept flowing for the next decade and a half. We packed more transistors in, more cache, cleaner clean rooms, faster bus speeds, with Intel owning the computer CPU market and AMD slowly growing from the ashes of Acorn computer into the power-house that AMD cores are today, when embedded in other chips designs. I'd say not much interesting has happened, but it's ALL interesting, except the numbers just sound stupid they're so big. And we had more advances along the way of course, but it started to feel like we were just miniaturizing more and more, allowing us to do much more advanced computing in general. The fifth generation of computing is all about technologies that we today consider advanced. Artificial Intelligence, Parallel Computing, Very High Level Computer Languages, the migration away from desktops to laptops and even smaller devices like smartphones. ULSI, or Ultra Large Scale Integration chips not only tells us that chip designers really have no creativity outside of chip architecture, but also means millions up to tens of billions of transistors on silicon. At the time of this recording, the AMD Epic Rome is the single chip package with the most transistors, at 32 billion. Silicon is the seventh most abundant element in the universe and the second most in the crust of the planet earth. Given that there's more chips than people by a huge percentage, we're lucky we don't have to worry about running out any time soon! We skipped RAM in this episode. But it kinda' deserves its own, since RAM is still following Moore's Law, while the CPU is kinda' lagging again. Maybe it's time for our friends at DARPA to get the kids from Berkley working at VERYUltra Large Scale chips or VULSIs! Or they could sign on to sponsor this podcast! And now I'm going to go take a VERYUltra Large Scale nap. Gentle listeners I hope you can do that as well. Unless you're driving while listening to this. Don't nap while driving. But do have a lovely day. Thank you for listening to yet another episode of the History of Computing Podcast. We're so lucky to have you!

Episode Notes: Sameer Shende, an expert on what’s called the Tuning and Analysis Utilities (TAU) performance evaluation tool for applications, explains its features and benefits.

Learn about parallel computing, the rise of heterogeneous processing (also known as hybrid processing), and quantum engineering in today's EEs Talk Tech electrical engineering podcast!

Moore's Law -- putting more and more transistors on a chip -- accelerated the computing industry by so many orders of magnitude, it has (and continues to) achieve seemingly impossible feats. However, we're now resorting to brute-force hacks to keep pushing it beyond its limits and are getting closer to the point of diminishing returns (especially given costly manufacturing infrastructure). Yet this very dynamic is leading to "a Cambrian explosion" in computing capabilities… just look at what's happening today with GPUs, FPGAs, and neuromorphic chips. Through such continuing performance improvements and parallelization, classic computing continues to reshape the modern world. But we're so focused on making our computers do more that we're not talking enough about what classic computers can't do -- and that's to compute things the way nature does, which operates in quantum mechanics. So our smart machines are really quite dumb, argues Rigetti Computing founder and CEO Chad Rigetti; they're limited to human-made binary code vs. the natural reality of continuous variables. This in turn limits our ability to work on problems that classic computers can't solve, such as key applications in computational chemistry or large-scale optimization for machine learning and artificial intelligence. Which is where quantum computing comes in. But what is quantum computing, really -- beyond the history and the hype? And where are we in reaching the promise of practical quantum computers? (Hint: it will take a hybrid approach to get there.) Who are the players -- companies, countries, types of people/skills -- working on it, and how can a startup compete in this space? Finally, what will it take to get "the flywheel" of application development and discovery going? Part of the answer comes full circle to the same economic engine that drove previous computing advances, argues Chris Dixon; Moore's Law, after all, is more of an economic principle that combined the forces of capitalism, a critical mass of ideas, and people moving things forward by sheer will. Quantum computing is finally getting pulled into the same economic forces as well.

Dr. Michael Voss, Software Architect at Intel, and Robert Geva, Senior Principal Engineer and Compiler Writer at Intel, join us to discuss parallel computing with Intel® Threading Building Blocks (Intel® TBB). Intel® TBB is a C++ library that enables developers to add parallelism to their applications without closely managing threading details. In this interview, Voss and Geva assess the software industry's current use of parallelism, highlight Intel® TBB's ability to optimize parallelized applications for current and future architectures, and report on benefits customers in industries like visual effects, financial services, and healthcare have already realized with Intel® TBB. For more information on Intel® TBB, please visit https://www.threadingbuildingblocks.org/.

Rob and Jason are joined by Dori Exterman to discuss parallel computing strategies and Incredibuild. An expert software developer and product strategist, Dori Exterman has 20 years of experience in the software development industry. As Chief Technical Officer of IncrediBuild, he directs the company's product strategy and is responsible for product vision, implementation, and technical partnerships. Before joining IncrediBuild, Dori held a variety of technical and product development roles at software companies, with a focus on architecture, performance and advanced technologies. He is an expert and frequent speaker on technological advancement in development tools specializing in Embarcadero (formerly Borland) environments, and manages the Israeli development forum for these tools. News Herb Sutter Trip Report Testing GCC in the wild JF Bastien Trip Report - Happy with C++17 Dori Exterman Dori Exterman Links Considerations for choosing the parallel computing strategy - Dori Exterman - Meeting C++ 2015 Incredibuild  

Новости SMT_rails - Shared Mustache Templates for Rails 3 шарим темплейты между Ruby и JavaScript. Пример приложения - первые 10 продуктов генерится на сервере, следующие по скролу (scroll pagination) загружаются через JS. Для обоих используется один темплейт. Вышла книга The dRuby Book: Distributed and Parallel Computing with Ruby Вышел Ruby 1.9.3 patchlevel 194 Ехуда Кац запустил проект Токайдо 16 апреля вышел ruby-plsql Вышло расширение к Chrome CoffeeConsole mruby and MobiRuby 19 и 20 мая Brainwashing Обсуждение В догонку к рассказу про IDE LightTable — редактор будущего RSense Eclim vim-ruby GNU Global cscope ctags Структуры данных и алгоритмы Массив Множество Список Хеш Стек Очередь Граф Дерево поиска AVL-дерево B-дерево Суффиксное дерево Бинарный поиск facebook и 6 рукопожатий Конкурс Описание задачи на конкурс, спешите до 15 мая решить задачу и получить свой кусочек тяжелой программистской славы!

Jared Hoberock of NVIDIA gives the introductory lecture to CS 193G: Programming Massively Parallel Processors. (March 30, 2010)

Prith Banerjee, senior vice president of research with Hewlett-Packard, discusses future research at HP. Before joining HP, he was the engineering dean at the University of Illinois at Chicago. His research interests are in very large-scale-integration, computer-aided design; parallel computing; and compilers.

In the 1990s a number of technological innovations appeared that revolutionized biology, and 'Bioinformatics' became a new scientific discipline. Microarrays can measure the abundance of tens of thousands of mRNA species, data on the complete genomic sequences of many different organisms are available, and other technologies make it possible to study various processes at the molecular level. In Bioinformatics and Biostatistics, current research and computations are limited by the available computer hardware. However, this problem can be solved using high-performance computing resources. There are several reasons for the increased focus on high-performance computing: larger data sets, increased computational requirements stemming from more sophisticated methodologies, and latest developments in computer chip production. The open-source programming language 'R' was developed to provide a powerful and extensible environment for statistical and graphical techniques. There are many good reasons for preferring R to other software or programming languages for scientific computations (in statistics and biology). However, the development of the R language was not aimed at providing a software for parallel or high-performance computing. Nonetheless, during the last decade, a great deal of research has been conducted on using parallel computing techniques with R. This PhD thesis demonstrates the usefulness of the R language and parallel computing for biological research. It introduces parallel computing with R, and reviews and evaluates existing techniques and R packages for parallel computing on Computer Clusters, on Multi-Core Systems, and in Grid Computing. From a computer-scientific point of view the packages were examined as to their reusability in biological applications, and some upgrades were proposed. Furthermore, parallel applications for next-generation sequence data and preprocessing of microarray data were developed. Microarray data are characterized by high levels of noise and bias. As these perturbations have to be removed, preprocessing of raw data has been a research topic of high priority over the past few years. A new Bioconductor package called affyPara for parallelized preprocessing of high-density oligonucleotide microarray data was developed and published. The partition of data can be performed on arrays using a block cyclic partition, and, as a result, parallelization of algorithms becomes directly possible. Existing statistical algorithms and data structures had to be adjusted and reformulated for the use in parallel computing. Using the new parallel infrastructure, normalization methods can be enhanced and new methods became available. The partition of data and distribution to several nodes or processors solves the main memory problem and accelerates the methods by up to the factor fifteen for 300 arrays or more. The final part of the thesis contains a huge cancer study analysing more than 7000 microarrays from a publicly available database, and estimating gene interaction networks. For this purpose, a new R package for microarray data management was developed, and various challenges regarding the analysis of this amount of data are discussed. The comparison of gene networks for different pathways and different cancer entities in the new amount of data partly confirms already established forms of gene interaction.

In dieser Folge erzählen Beat (siehe auch HF-011) und Venty über Cluster, SMP und Distributed Computing. Ausserdem gibts am Schluss der Folge einen kleinen Wettbewerb. Wie schwer war der schwerste Computer, an dem Beat bisher mitgearbeitet hat? Eure Antwort schickt Ihr bis Ende Mai 2009 per Mail an radio@hackerfunk.ch. Trackliste Tonka – Palace Gardens (Loader) Zürisee – Zürisee Lava – Still in Love Aygan – Days so hard daXX – Breeze Nächste Sendung am Samstag, 6. Juni 2009, 19:00 Uhr Beat :: Beats Homepage Beowulf :: The Beowulf Cluster Site Parallel Computing :: Wikipedia Kategorie zum Parallel Computing Top 500 Supercomputer :: Die Top 500 der schnellsten Computer der Welt Clustering 101 :: Alles rund um Cluster und Clustering Folding@HOME :: Offizielle Site zu Folding@HOME bei der Stanford University Swissteam :: Webseite des Distributed Computing Swissteams Rechenkraft :: Umfassende Uebersicht der laufenden Distributed Computing Projekte File Download (61:13 min / 93 MB)

Now that the IT industry is urgently facing perhaps its greatest challenge in 50 years, and computer architecture is a necessary but not sufficient component to any solution, this talk declares that computer architecture is interesting once again.