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Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tools and aims to make them useful for HPC practitioners and scientists concerned with dependency graph control and customization and, uniquely, reproducible research.

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High-speed network interconnects are a key component of supercomputers. The challenge from a software packaging viewpoint is to provide an MPI stack able to get the performance out of that specialized networking hardware. As packagers, our approach has been to provide MPI packages that get the best performance of the underlying interconnect, be it Omni-Path, InfiniBand, or any other type of interconnect.

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We are pleased to publish the sixth Guix-HPC annual report. Launched in 2017, Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tool to empower HPC practitioners and scientists who need reliability, flexibility, and reproducibility; it aims to support Open Science and reproducible research.

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We have some exciting news to share: AMD has just contributed 100+ Guix packages adding several versions of the whole HIP and ROCm stack! ROCm is AMD’s Radeon Open Compute Platform, a set of low-level support tools for general-purpose computing on graphics processing units (GPGPUs), and HIP is the Heterogeneous Interface for Portability, a language one can use to write code (computational kernels) targeting GPUs or CPUs. The whole stack is free and “open source” software—a breath of fresh air!—and is seeing increasing adoption in HPC. And, it can now be deployed with Guix!

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Will those binaries actually work? This is a central question for HPC practitioners and one that’s sometimes hard to answer: increasingly complex software stacks being deployed, and often on a variety of clusters. Will that program pick the right libraries? Will it perform well? With each cluster having its own hardware characteristics, portability is often considered unachievable. As a result, HPC practitioners rarely take advantage of continuous integration and continuous delivery (CI/CD): building software locally on the cluster is common, and software validation is often a costly manual process that has to be repeated on each cluster.

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Some things in our software world are timeless. The venerable Environment Modules are one of these. If you’ve ever used a high-performance cluster in the last three decades, chances are you’re already familiar with it. Modules is about managing software environments, just like Guix is—or, perhaps more accurately, guix shell.

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It should come as no surprise that the execution speed of programs is a primary concern in high-performance computing (HPC). Many HPC practitioners would tell you that, among their top concerns, is the performance of high-speed networks used by the Message Passing Interface (MPI) and use of the latest vectorization extensions of modern CPUs.

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The guix pack command creates “application bundles” that can be used to deploy software on machines that do not run Guix (yet!), such as HPC clusters. Since its inception in 2017, it has seen a number of improvements, such as the ability to create Docker and Singularity container images. Some clusters lack these tools, though, and the addition of relocatable packs was a way to address that. This post looks at a new execution engine for relocatable packs that has just landed with the goal of improving performance.

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High-performance networks have constantly been evolving, in sometimes hard-to-decipher ways. Once upon a time, hardware vendors would pre-install an MPI implementation (often an in-house fork of one of the free MPI implementations) specially tailored for their hardware. Fortunately, this time appears to be gone. Despite that, there is still widespread belief that MPI cannot be packaged in a way that achieves best performance on a variety of contemporary high-speed networking hardware.

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I’m happy to announce that the bioinformatics group at the Max Delbrück Center that I’m working with has released a preprint of a paper on reproducibility with the title Reproducible genomics analysis pipelines with GNU Guix.

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This post marks the debut of Guix-HPC, an effort to optimize GNU Guix for reproducible scientific workflows in high-performance computing (HPC). Guix-HPC is a joint effort between Inria, the Max Delbrück Center for Molecular Medicine (MDC), and the Utrecht Bioinformatics Center (UBC). Ludovic Courtès, Ricardo Wurmus, Roel Janssen, and Pjotr Prins are driving the effort in each of these institutes, each one focusing specific areas of interest within this overall Guix-HPC effort. Our institutes have in common that they are users of HPC, and that, as scientific research institutes, they have an interest in using reproducible methodologies to carry out their research.

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