Implementing Computational Storage Solutions

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Publish Date: 
Wednesday, September 23, 2020
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Abstract: 

Moving large amounts of data between storage and compute cannot scale given the ever-increasing storage capacities. A shift to computational storage that brings compute closer to the stored data provides the solution. Data-driven applications that benefit from database searches, data manipulation, and machine learning can perform better and be more scalable if developers add computation directly to storage. Flexibility is key in the architecture of a Computational Storage device hardware and software implementation. Hardware flexibility minimizes development cost, controller cost, and controller power. Software flexibility leverages existing ecosystems and software stacks to simplify code development and facilitate workload deployment to the compute on the drive. Implementing the hardware and software flexibility into a Computational Storage Drive requires forethought and deliberate consideration to achieve a successful solution. This presentation will show how to simplify computational storage architectures. Attendees will walk away with how to reduce power, area, and complexity of their computational storage controller, and leverage Linux and the Linux ecosystem of software to facilitate software development and workload management by taking advantage of computational storage capabilities.

Learning Objectives

Attendees will learn about flexible hardware architectures that enable Computational Storage.,Attendees will learn about flexible software that executes on the the compute in a CSD.,Attendees will learn how to simplify the hardware and software development while retaining the full capability of Computational Storage.

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