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GCC 9 vs. Clang 8 C/C++ Compiler Performance On AMD Threadripper, Intel Core i9

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Since the release of the GCC 9 stable compiler suite earlier this month we have begun firing up a number of compiler benchmarks for this annual feature update to the GNU Compiler Collection. For your viewing pleasure today is looking at the performance of GCC 8 against GCC 9 compared to LLVM Clang 8 as the latest release of this friendly open-source compiler competition. This GCC 8 vs. GCC 9 vs. Clang 8 C/C++ compiler benchmarking was done on an Intel Core i9 7980XE and AMD Ryzen Threadripper 2990WX high-end desktop/workstation systems.

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Programming: SYCL, Python, SwiftWasm and NodeRun

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  • hipSYCL Gets New Compilation Toolchain For Taking SYCL Directly To CUDA & ROCm

    Could 2019 be the year that SYCL really takes off for this single-source C++-based programming model? There's certainly a lot of interesting projects going on around SYCL. 

    Intel is making very interesting moves and among them are working on upstreaming SYCL support in LLVM. It appears this SYCL play of theirs will be a key part of their "oneAPI" effort that is supposed to be out by year's end.

  • How to Extract Build Info from Jenkins with Python

    The Python Jenkins package will work with both Hudson and Jenkins which JenkinsAPI only works with Jenkins. I usually use Python Jenkins because of this, although I have recently started looking to see which one works better with artifacts and I discovered that JenkinsAPI is actually better for that sort of thing. So you will need to evaluate both of these packages depending on what you need to do.

  • Stylin’ with Pandas

    I have been working on a side project so I have not had as much time to blog. Hopefully I will be able to share more about that project soon.

    In the meantime, I wanted to write an article about styling output in pandas. The API for styling is somewhat new and has been under very active development. It contains a useful set of tools for styling the output of your pandas DataFrames and Series. In my own usage, I tend to only use a small subset of the available options but I always seem to forget the details. This article will show examples of how to format numbers in a pandas DataFrame and use some of the more advanced pandas styling visualization options to improve your ability to analyze data with pandas.

  • Introducing SwiftWasm, a tool for compiling Swift to WebAssembly

    The SwiftWasm tool is built on top of the WASI SDK, which is a WASI-enabled C/ C++ toolchain. This makes the WebAssembly executables generated by SwiftWasm work on both browsers and standalone WebAssembly runtimes such as Wasmtime, Fastly’s Lucet, or any other WASI-compatible WebAssembly runtime.

  • NodeRun Is Node.js For Everyone

    NodeRun is the free, easy, and social way to develop and deploy full-stack, enterprise-ready Node.js applications. We provide the cloud-based visual IDE, the database, and the server – you just need to bring your imagination.

    We built NodeRun with business application development in mind. You can develop line-of-business applications faster than by traditional methods, making it an ideal solution for new and experienced Node.js developers alike. When you create an application in NodeRun, we provide you with a small instance of Ubuntu Linux, a MariaDB database, and a Node.js server running Express and our Profound.js framework. Everything you need to start developing your application and all running in the cloud.

  • Alberto Flores (albertoefg): New beginning

    Over the last few days I've had a few great news and some small wins. I was accepted in Google Summer of Code as a contributor to Krita, this gave me so much happiness. I also got my passport so I feel one step closer to the Krita Sprint and it is going to be awesome, I am really excited to meet everyone because everyone who is part of Krita has been great to me, Krita has this great positive community. I also started to do contributions to the manual and even though is something small I feel proud of myself. I also asked for a KDE Developer Account.

    I also had been going out more and working out, which is a nice change after being depressed and solitary for so long. And I've been more happy and optimist about life in general. I also got my grades for this four month period: 9.9,9.8,9.7,9.0,9.0, so I had a good period, but a new one has started already.

Programming Leftovers

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Programming Leftovers

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  • Cat shirts and Groundhog Day: the Kubernetes 1.14 release interview

    Last week we celebrated one year of the Kubernetes Podcast from Google. In this weekly show, my co-host Adam Glick and I focus on all the great things that are happening in the world of Kubernetes and Cloud Native. From the news of the week, to interviews with people in the community, we help you stay up to date on everything Kubernetes.

    Every few cycles we check in on the release process for Kubernetes itself. Last year we interviewed the release managers for Kubernetes 1.11, and shared that transcript on the Kubernetes blog. We got such great feedback that we wanted to share the transcript of our recent conversation with Aaron Crickenberger, the release manager for Kubernetes 1.14.

  • Learn Python by teaching in your community

    Since ancient times, we've known that one of the most effective ways to learn something is by teaching it to someone else. I've put that strategy into practice by teaching Python in my community. If you want to learn Python for free and have fun at the same time, I recommend you consider doing the same.

    I started on this journey about six years ago, based on the recommendation of a friend who was teaching himself Python. It all came together about a year later when I bought my first Raspberry Pi and learned about the Turtle module in Python.

    The Turtle was an epiphany that changed my life and perception of myself as a learner. Turtle graphics helped me understand mathematics and its relevance in my life. I began to explore Python vigorously and, as I gained confidence, I started sharing my newfound knowledge with the teachers and students I came in contact with.

  • Don't test in production? Test in production!

    If you last updated your IT security standards five or more years ago, chances are they don't line up well with the realities of today's DevOps and site reliability engineering (SRE) practices. One particularly sticky topic is testing in production—and, thus, testing with production data—because DevOps and SRE blur the line between what is production and what is not; what is a test and what is not.

  • EuroPython 2019: Early-bird ticket sales open today

    As previously announced, we will be opening early-bird tickets sales today at 12:00 CEST.

    We will have 200 early-bird tickets available when we open up ticket sales. They offer a discount of up to 50% over the standard rate and are usually sold out within a few hours. In 2018 early bird tickets were sold out in just 45 minutes.

  • Building and understanding reactive microservices using Eclipse Vert.x and distributed tracing
  • Introducing Mypy, an Experimental Optional Static Type Checker for Python
  • Migrating Java applications to Quarkus, Part 2: Before and after

Programming: C vs C++, KNN, Python, GitLab and ActiveState's Survey

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  • F() vs F(void) in C vs C++

    Prefer f(void) in C to potentially save a 2B instruction per function call when targeting x86_64 as a micro-optimization. -Wstrict-prototypes can help. Doesn’t matter for C++.

  • Simplest algorithm to get started with machine learning

    Hello, my name is Alex. And this article is about KNN algorithm. It's been a while since I first time met term "machine learning". For me, as a front-end developer, it's always been a struggle to understand it. There are so many things like supervised, unsupervised, reinforcement learning. Hundreds types of algorithms and neural networks.

  • Python Lambda Function with Examples
  • Python dicts and memory usage
  • AJAX calls in Django 2.2
  • On the Rookout for live data: Instant observability to fix software bugs and open AI black boxes

    Software bugs are a pain: buggy software can drop anything from your sales to aircraft in mid-flight. Debugging software is hard, tedious, and costs a fortune. A multitude of frameworks and processes have been created to facilitate software testing and ensure fewer bugs make it to production, and invariably they all fail from time to time.

    When this happens, the pain for software developers and the nail-biting for businesses starts. Developers have to find the source code that caused the bug, and execute this in a test environment that resembles the production one as closely as possible. The situation that caused the bug has to be recreated, too.

    The way this usually works is by adding logging statements and breakpoints in the code, and retracing execution in the code and its dependencies until the bug is located and can be fixed. Then the new code has to be rebuilt and redeployed in production.

  • How GitLab Cracked The Code Of Remote Working: GitLab Contribute Report

    Industrial revolutions changed our world. They built the foundations of the modern civilizations. But there were some side effects of these revolutions, too. One of the biggest “damages” industrial revolutions did to our society was breaking families apart. We have to leave our homes, our kids, our loved ones behind and go to ‘work’. We spend several years of our lives in just commuting to work.

  • ActiveState survey: developers spend 20 percent less time on programming compared to 2018

    Security, stability and managing dependencies of open source languages have been keeping developers from fully using their time toward what they do best: programming. A recent ActiveState survey titled “2019 Developer Survey: Open Source Runtimes” found that developers spend 20 percent less time on programming than last year.

    The survey included responses from 1,250 developers, 65 percent of whom consider themselves professional developers, from all over the world.

    According to the survey, about half of the developers ranked adding or incorporating a new language as difficult to very difficult. More than 61 percent said they spend four hours or less per day on programming and 65 percent said they don’t contribute or maintain open source projects. About half of those developers blamed a lack of time.

Programming: Google, Python and More

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  • Google is starting to reveal the secrets of its experimental Fuchsia OS

    Google Fuchsia remains shrouded in mystery, but the company is slowly beginning to open up about the next-generation operating system, what its purpose is, and what devices it might power. At Google’s I/O developer conference this past week, Android and Chrome chief Hiroshi Lockheimer offered some rare insight into Fuchsia, albeit at a very high level, in front of public audiences.

    What we do know about Fuchsia is that it’s an open source project, similar to AOSP, but could run all manner of devices, from smart home gadgets to laptops to phones. It’s also known to be built on an all-new, Google-built kernel called “zircon,” formerly known as “magenta,” and not the Linux kernel that forms the foundation of Android and Chrome OS.

  • Google launches Jetpack Compose, an open-source, Kotlin-based UI development toolkit

    Google today announced the first preview of Jetpack Compose, a new open-source UI toolkit for Kotlin developers who want to use a reactive programming model similar to React Native and Vue.js.

    Jetpack Compose is an unbundled toolkit that is part of Google’s overall Android Jetpack set of software components for Android developers, but there is no requirement to use any other Jetpack components. With Jetpack Compose, Google is essentially bringing the UI-as-code philosophy to Android development. Compose’s UI components are fully declarative and allow developers to create layouts by simply describing what the UI should look like in their code. The Compose framework will handle all the gory details of UI optimization for the developer.

  • Guido van Rossum On Python and Diversity in Open Source

    In a wide ranging conversation Guido van Rossum outlines how and why he initiated the Python language and why he resigned as BDFL after 28 years. He also discusses the issue of diversity in open source communities with interviewer Swapnil Bhartiya, the founder of the TFiR website.

    Although the video was only posted on TFiR, and published on You Tube on May 4, 2019 the interview took place last September during Oracle Code One, which Van Rossum was attending to receive of the five 2018 Oracle Groundbreaker Awards.

  • Setu announces open source learning platform for coders & designers building fintech apps

    Fintech focused software maker Setu has launched D91 labs – an open source initiative by the company to understand and enhance the financial lives of Bharat that embrace financial inclusion at the grass root level.

    D91 labs aspires to become last mile understanding for developers building fintech product for the masses. It publishes open source research, insights, frameworks and component libraries for designers, developers, and other stakeholders.

    Announcing D91 labs, the firm also published three posts on blogging platform Medium around designing for choices in payments, a case study depicting the plight of individuals who borrow from unorganised lenders and a survey on mobile payments in India.

  • Google & FB’s Latest Open Source Contributions To Machine Learning
  • Hardware Random Number Generator PRG320
  • Talk Python to Me: #211 Classic CS problems in Python

Linux 5.2 Live-Patching Makes Use Of GCC 9 Option That May Slowdown Select Cases

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If you make use of Linux kernel live-patching or even just have CONFIG_LIVEPATCH enabled, when switching to Linux 5.2 and building with GCC 9, the performance may be impacted in select workloads.

The Linux livepatching infrastructure updates were sent in this week for the Linux 5.2 kernel merge window. The changes aren't too exciting but does include the change we previously outlined of turning on GCC 9's live-patching option as part of the kernel build when CONFIG_LIVEPATCH is enabled.

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Python and Keeping Systems Simple

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Programming: ML, RcppArmadillo, Lightweight Java Game Library (LWJGL), Rust and Debian

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  • Top 20 Best ML Algorithms For Both Newbies and Professionals

    When I started to work with machine learning problems, then I feel panicked which algorithm should I use? Or which one is easy to apply? If you are as like me, then this article might help you to know an overview of machine learning algorithms.

  • Dirk Eddelbuettel: RcppArmadillo 0.9.400.3.0

    The recent 0.9.400.2.0 release of RcppArmadillo required a bug fix release. Conrad follow up on Armadillo 9.400.2 with 9.400.3 – which we packaged (and tested extensively as usual). It is now on CRAN and will get to Debian shortly.

    Armadillo is a powerful and expressive C++ template library for linear algebra aiming towards a good balance between speed and ease of use with a syntax deliberately close to a Matlab. RcppArmadillo integrates this library with the R environment and language–and is widely used by (currently) 597 other packages on CRAN.

  • LWJGL 3.2.2 Updates Against Vulkan 1.1, Other New Packages

    For those making use of the Lightweight Java Game Library (LWJGL) for cross-platform libraries common to games/multimedia software, version 3.2.2 is now available with nearly a half year worth of updates.

  • Rust doubly-linked list

    I have now released (and published on my doubly-linked list library for Rust.

    Of course in Rust you don't usually want a doubly-linked list. The VecDeque array-based double-ended queue is usually much better. I discuss this in detail in my module's documentation.

  • New Debian Developers and Maintainers (March and April 2019)

    The following contributors got their Debian Developer accounts in the last two months:

    Jean-Baptiste Favre (jbfavre)
    Andrius Merkys (merkys)
    The following contributors were added as Debian Maintainers in the last two months:

    Christian Ehrhardt
    Aniol Marti
    Utkarsh Gupta
    Nicolas Schier
    Stewart Ferguson
    Hilmar Preusse

Programming: Compilers, Rust, Zope on Python 3 and More Python Bits

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  • Intel's BFloat16 Support Plumbed Into GCC 10 & LLVM Clang 9 Compilers

    Last month Intel developers began working on adding BFloat16 support to the open-source/Linux compilers for this new instruction debuting with next-generation Xeon "Cooper Lake" server processors. That support is now squared away for GCC 10, due out next year, and LLVM Clang 9, which will be released this autumn.

    BFloat16 floating-point support is intended for deep learning / machine learning workloads and will be supported by next-gen Cooper Lake processors and is also found within Nervana NNPs and FPGAs. BFloat16 has also been of interest to other companies like Google and their TPUs.

  • Rust doubly-linked list, redux

    I have declared rc-dlist-deque, my doubly-linked list library for Rust, to be 1.0.0. Little has changed, apart from the version number and some documentation updates.

    In particular, I thought I would expand on my previous comments to the effect that you don't want a doubly linked list in Rust.

  • Celebration: Zope 4 final release

    After hard, long years of preparation Earl Zope now finally made it to get a permanent license for the Python 3 wonderland: In September 2016 almost 20 people started with the reanimation of Zope at the Zope Resurrection sprint. This marked the beginning of a wonderful journey for Earl Zope himself for the people who helped him. In August 2017 Earl Zope became aware that his Python 2 country will irreversibly be destroyed by 2020. Earl Zope was successfully applying for for a beta permission for the Python 3 wonderland in September 2017. This beta permission has been extended 9 times to give Earl Zope time to become a good citizen in his new home country.

  • Python for Data Science : Learn in 3 Days

    This tutorial helps you to learn Data Science with Python with examples. Python is an open source language and it is widely used as a high-level programming language for general-purpose programming. It has gained high popularity in data science world. As data science domain is rising these days, IBM recently predicted demand for data science professionals would rise by more than 25% by 2020. In the PyPL Popularity of Programming language index, Python scored second rank with a 14 percent share. In advanced analytics and predictive analytics market, it is ranked among top 3 programming languages for advanced analytics.

  • Introduction to Reinforcement Learning with Python
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