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Programming: Red Hat JBoss Enterprise Application Platform (EAP), Coding on a Chromebook, HPC and What’s New In Python 3.7.2

Submitted by Roy Schestowitz on Thursday 24th of January 2019 06:23:06 PM Filed under
Development
  • Announcing the general availability of Red Hat JBoss Enterprise Application Platform 7.2

    Today we are announcing the general availability of Red Hat JBoss Enterprise Appplication Platform (EAP) 7.2, which brings greater compliance with Java Enterprise Edition (EE) 8, JDK 11/Java SE 11, and further support for Microsoft Windows and enterprise Java microservices. With this release, Red Hat is continuing our commitment to Java EE 8 and Jakarta EE, the new home for cloud-native Java, a community-driven specification under the Eclipse Foundation.

    JBoss EAP is an open source Java EE 8-compliant application server and is used by organizations around the world to deploy and manage business-critical enterprise Java applications, whether in bare metal, virtualized, containerized, on-premises, private, public or hybrid cloud environments. JBoss EAP 7.2 is Java EE 8 certified, which means new functionality as well as updates to a variety of existing capabilities.

  • Coding on a Chromebook: I’m all in with Crostini and the Pixel Slate

    One of the most read posts on About Chromebooks is from last April when I detailed how I was coding with my Pixelbook. Technically, I wasn’t coding on the Pixelbook, because my solution was to remotely access a Raspberry Pi that had Python installed on my local network. Thanks to the addition of Linux containers through Project Crostini in Chrome OS, I don’t have to rely on — or be limited to — another computing device for my coding.

  • Reflections on HPC Past, Present, and Future

    What you need are the tools to enable you to use these systems effectively. This comes down to engineering your network, your storage, and so forth, to meet your criteria. Part of this work is to iterate quickly, which does imply common toolsets across environments. And it generally suggests local resources for development, that at least mirror some aspects of the large remote systems. Again, to minimize friction.

    Watching the toolchains grow up has been fairly uplifting. You can write your own CUDA code to handle GPU work. Or, in ML, you can leverage higher level toolkits such as TensorFlow, MXnet, PyTorch, etc. For a number of reasons, ML folks seem to have converged around Python as their lingua franca, though given its many issues (GIL, format as structure, etc.), the door is open to other systems.

    One of my favorite is Julia language. It has a wonderful interface to GPUs via the JuliaGPU project. Unlike Python, it doesn’t have a GIL, as it is compiled and built for parallelism, nor does it have structure by indentation. It has a native GPU compiler built in to its LLVM stack, meaning that it can optimize not merely for the CPUs, but also for the GPUs. It is rapidly maturing, so there are a few rough spots, but I expect tools like this to become more standard for HPC applications.

  • What’s New In Python 3.7.2: Look At 7 Features That You Should Know

    There’s a lot of new material in Python Latest version and much of it is pretty simple. For example, str, bytes and bytearray now have an isascii() function that returns ‘true’ if they only include ASCII characters.

    In this article, we list down 7 essential features of the latest version which Python aspirants must get updated with.

»

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