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Launch day for Sylabs: Portable high-performance containers for Linux

Today is launch day for Sylabs — a new company focused on promoting Singularity within the enterprise and high-performance computing (HPC) environments and on advancing the fields of artificial intelligence (AI), machine/deep learning, and advanced analytics.

And while it’s launch day for Sylabs, it’s not launch day for the technology it will be promoting. Singularity has already made great strides for HPC and has given Linux itself more prominence in HPC as it has moved more deeply into the areas of scientific and enterprise computing. With its roots at Lawrence Berkeley National Laboratory (Berkeley Lab), Singularity is already providing a platform for a lot of heavy-duty scientific research and is expected to move into many other areas, such as machine learning, and may even change the way some difficult analytical problems are approached.

What is Singularity container technology?

If you’re not familiar with Singularity as a relatively new (late 2015 introduction) container technology, you might be intrigued by some background. It was designed with HPC in mind, is already in its 13th release, and supports millions of containers every day through the Open Science Grid. It’s also open source.

While already the container platform of choice in academia and HPC centers, Singularity’s features will prove increasingly valuable in areas such as machine learning and data science — data-intensive applications that require high-performance resources. And as businesses seek to make better use of increasing data availability, they will need tools that can work with large amounts of data and containers that support and regularize its processing.

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