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Deprecating support for the Linux kernel

After years in the making, Guix recently gained support for running natively on the GNU/Hurd operating system. That means you will soon be able to replace... (kernel linux-libre) with (kernel hurd) (initial-herd hurd) ...in your operating-system declaration and reboot into the future! Running on the Hurd was always a goal for Guix, and supporting multiple kernels is a huge maintenance burden. As such it is expected that the upcoming Guix 1.1 release will be the last version featuring the Linux-Libre kernel. Future versions of Guix System will run exclusively on the Hurd, and we expect to remove Linux-Libre entirely by Guix 2.0. The Linux kernel will still be supported when using Guix on "foreign" distributions, but it will be on a best-effort basis. We hope that other distributions will follow suit and adopt the Hurd in order to increase security and freedom for their users. We provide a pre-built virtual machine image with the Hurd for download with SHA256 056e69ae4b5fe7a062b954a5be333332152caa150359c20253ef77152334c662. Read more Also: GNU Guix Wants To Replace The Linux-Libre Kernel With The Hurd Micro-Kernel

Yesterday in Ubuntu Blog

  • Ubuntu Blog: Steps to maximise robotics security with Ubuntu

    The Robot Operating System (ROS) is a popular open-source platform for advanced robotics. Its flexibility and ease-of-use make it well-suited to a wide array of robotics applications – however, these robots are not always sufficiently protected against security threats. Opportunistic attacks are by far the most prevalent, and robots with inadequate ROS security make tempting targets for bad actors. With that in mind, approaching robotics security proactively is crucial to preventing breaches and saving resources in the long run. Security starts with the underlying operating system, and building robots on Ubuntu unlocks a number of easy, yet effective, measures for maximising protection against the most dominant threats.

  • OpenStack distributions: How to choose the right one?

    Choosing the right OpenStack distribution is essential to the success of an OpenStack project at every organisation. When selecting one, organisations should always follow certain criteria. Is it possible to operate the considered OpenStack distributions economically? How easy is it to deploy them? Can the organisation upgrade its production OpenStack cloud without affecting the workloads? Everyone planning to deploy OpenStack should ask themselves these questions. And there is always more criteria to consider. In order to facilitate the OpenStack vendor selection process for the organisations, we have recently published an OpenStack distributions comparison website. It highlights the key differences between three leading OpenStack platforms: Canonical’s Charmed OpenStack, Red Hat OpenStack Platform and Mirantis Cloud Platform. Now, in the following blog, I am going to describe some tips which organisations should follow to make sure that they choose the right OpenStack distribution.

  • Accelerate AI/ML workloads with Kubeflow and System Architecture

    AI/ML model training is becoming more time consuming due to the increase in data needed to achieve higher accuracy levels. This is compounded by growing business expectations to frequently re-train and tune models as new data is available. The two combined is resulting in heavier compute demands for AI/ML applications. This trend is set to continue and is leading data center companies to prepare for greater compute and memory-intensive loads for AI.

  • FIPS 140-2: Stay compliant and secure with Canonical

    FIPS 140-2 is a set of publicly announced cryptographic standards developed by the National Institute of Standards and Technology. It is an essential part of FEDRamp requirements for many governmental agencies in the US and Canada, as well as their business partners from all around the world. Furthermore, as a well established and verified security standard, an increasing number of large companies and financial institutions are asking for FIPS compliance. Yet, FIPS certification process introduces challenges that could impact your security. Ubuntu lets you choose the way to implement FIPS-certified cryptographic modules with two distinct FIPS alternatives to choose from to overcome those challenges.

Programming: LLVM Clang, GCC, 30 Years of Haskell and More

  • Intel GCC Patches + PRM Update Adds SERIALIZE Instruction, Confirm Atom+Core Hybrid CPUs

    Intel has seemingly just updated their public programming reference manual as well as sending out some new patches to the GCC compiler for supporting new instructions on yet-to-be-released CPUs. Hitting the mailing list early this morning was support for TSXLDTRK. TSXLDTRK is Intel TSX Suspend Load Address Tracking and is confirmed as coming with Sapphire Rapids / Golden Cove. With that is the XSUSLDTRK to suspend tracking load addresses and XRESLDTRK so that software developers can choose the memory accesses that do not need to be tracked by a TSX (Transactional Synchronization Extensions) read set.

  • Upstreaming LLVM's Fortran "Flang" Front-End Has Been Flung Back Further

    Upstreaming of LLVM's Fortran front-end developed as "f18" and being upstreamed with the Flang name was supposed to happen back in January. Three months later, the developers still are struggling to get the code into shape for integration.

  • LLVM Clang 10.0 Compiler Performance On Intel + AMD CPUs Under Linux

    With last week's release of LLVM/Clang 10.0, here are our first benchmarks looking at the stable release of the Clang 10.0 C/C++ compiler compared to its previous (v9.0.1) release on various Intel and AMD processors under Ubuntu Linux.

  • GCC 11 Will Likely Support Using LLVM's libc++

    While GCC 10 isn't even out for a few more weeks, looking ahead to next year's GCC 11 release is already one interesting planned change. GCC 11's C++ front-end (G++) will likely offer support for using LLVM's libc++ standard library. There was recently a question asked on the GCC mailing list over the ability to do -stdlib=libc++ for using LLVM's C++ standard library in conjunction with the GCC C++ compiler.

  • How does kanban relate to DevOps?

    Kanban means "visual signal" and has its roots in the Toyota manufacturing industry. It was developed by Taiichi Ohno to improve manufacturing efficiency. When we jump a few decades into the future, kanban complements agile and lean, often used with frameworks such as scrum, Scaled Agile Framework, and Disciplined Agile to visualize and manage work.

  • Joachim Breitner: 30 years of Haskell

    Vitaly Bragilevsky, in a mail to the GHC Steering Committee, reminded me that the first version of the Haskell programming language was released exactly 30 years ago. On April 1st. So that raises the question: Was Haskell just an April fool's joke that was never retracted?

  • Monthly Report - March

    I lost a friend of mine, Jeff Goff (aka DrForr), who passed away on 13th March, 2020, while snorkeling with a group in the Bahamas. He will be missed by many of his friends. May his soul rest in peace. Most of the time last month was occupied by COVID-19. Being a type-2 diabetic didn't help the cause either. I have suffered with consistent cough all my life. It is really scary when think from COVID-19 point of view. I have survived so far by the grace of ALLAH s.w.t. I have been working from home since the first week of March. I have been kind of self quarantined. Kids, specially the twins (3 years old) not allowed to play with me. It is really hard to focus on work but somehow I have managed so far. I am getting used to it now.

IBM/Red Hat Leftovers

  • World Backup Day: A plan of action

    World Backup Day reminds us all of just how important backups are. You don't get how important they are, perhaps, until you've experienced an outage that you can't recover from by any troubleshooting method. Backups are a pain but they are a necessary evil and can save you when things go bad. And things always go bad. This article helps you make a plan.

  • Running an event-driven health management business process through a few scenarios: Part 1

    In the previous series of articles, Designing an event-driven business process at scale: A health management example (which you need to read to fully understand this one), you designed and implemented an event-driven scalable business process for the population health management use case. Now, you will run this process through a few scenarios.

  • Getting to open hybrid cloud

    So, you’ve read our e-book and are convinced that adopting an open hybrid cloud Platform is a key part of digitally transforming. Great! Now how do you get your applications and associated infrastructure there? There are many aspects that should be considered when digitally transforming and adopting an open hybrid cloud including people, culture, process, and technology. While these are all important, in this post we will focus on process and technology. A common way of speaking about migrating or modernizing workloads to the cloud was popularized in 2016 by Amazon Web Services in their post, "6 Strategies for Migration Applications to the Cloud." We will use the categorization popularized in that article to explore how Red Hat is making it quicker and easier to move your applications and their associated infrastructure to the open hybrid cloud.

  • Command and control: The Red Hat Ceph Storage 4 Dashboard changes the game

    Ease of use was a key development theme for Red Hat Ceph Storage 4. In our last post, we covered the role that the new install UI plays in enabling administrators to deploy Ceph Storage 4 in a simple and guided manner, without prior Ceph expertise. Simplifying installation is only the first step—the second step is simplifying day-to-day management. To meet this challenge, Ceph Storage 4 introduces a new graphical user interface called the Dashboard.

  • Red Hat DNF 4.2.21 Package Manager Released Today!

    DNF 4.2.21 Released Today: DNF is otherwise named as Dandified YUM Package Manager. DNF is basically developed by Red Hat for RPM based distributions. The team Red Hat developers announced the latest version of DNF 4.2.21 has been released. They promised that the new version may have many new essential bugs fixes and software tweaks.

  • Three ways our hybrid cloud architecture makes it easy to add AI to fulfillment
  • Gain transparency into fulfillment decisions

    In a previous blog, I introduced IBM Sterling Fulfillment Optimizer With Watson® and provided answers to five frequently asked questions. Once clients have implemented this AI-powered solution to optimize fulfillment, they tend to have another question: Why did Sterling Fulfillment Optimizer make the decisions that it did? In this blog, we’ll look at what’s in Watson’s head. When an order is sent to Sterling Fulfillment Optimizer, the order goes through many rules, configurations, constraints, and cost-optimization comparisons to determine the best fulfillment option. Sometimes, as a user, the recommendation intuitively feels right, but other times it may not – particularly if you’re dealing with complex orders and a complex fulfillment network. If an order is placed in Chicago and Sterling Fulfillment Optimizer recommends that different order lines for the order be fulfilled from nodes in Los Angeles and Dallas, you may have difficulty understanding why that was the best choice to maximize profits. What isn’t immediately evident is that behind the scenes, Sterling Fulfillment Optimizer is using big data analytics, AI, and machine learning to look for trends and patterns. It analyzes sell-through patterns, rate-of-sale and probability-of-sale data to determine the risk of stockouts or markdowns for each SKU node combination, automatically calculating the lowest overall fulfillment cost at that moment. This is critical because that moment in time is always changing as the fulfillment network and sell-through patterns continuously change, and business preferences may change as well. Remember from the last blog that I discussed how you may decide to prioritize one or more factors over the total cost due to promotions or seasonality. In this example, where the order is fulfilled from Los Angeles and Dallas, the solution determined — based on visibility into real-time data and balancing multiple factors simultaneously — that if the order had been fulfilled from a single node in Chicago, which at that moment was low on inventory, the risk of stockout would have been high.