IT Storage Trend to Watch: Computational Storage Devices – ITPro Today

IT Pro Today is part of the Informa Tech Division of Informa PLC
This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC’s registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.
Karen D. Schwartz | Dec 15, 2021
Tech has no shortage of buzzy new technologies — and cutting through the hype to see what will actually impact the enterprise can be challenging. We’re here to help. Starting in 2021, our contributors will give a rundown on an emerging tech and whether it’ll pay off to pay attention to it. Here, we look at computational storage devices.
To see the other trends highlighted in our IT Trends To Watch series, read our Emerging IT Trends To Watch report.
Computational storage is an architecture that moves compute operations onto the storage device itself rather than on external storage. The architecture allows data to be processed and analyzed where it is created and stored. The data remains in place.
Because computational storage eliminates the need to move data to external devices for processing, it can increase the performance of data-intensive storage applications.
The two most common approaches to computational storage are general-purpose compute and stack optimization, according to Enrico Signoretti, senior data storage analyst at Gigaom.
In the general-purpose compute approach, a multi-core CPU or field-programmable gate array (FGPA) with RAM is integrated into the storage device. That integration allows data to be accessed concurrently by the host and integrated compute resources.
With stack optimization, compute resources in the storage device use APIs for software integration. Services like compression, encryption and data protection are integrated with the rest of the software stack to send high-demand operations to the device.
Computational storage doesn’t replace anything in a computing environment. Instead, the architecture moves compute operations onto the data storage device, bringing the compute operations close to the data. Computational storage allows for greater optimization.
Computational storage emerged in 2011 when company-sponsored university research began to develop the architectural concept, said Scott Shadley, co-chair of the Storage Networking Industry Association (SNIA) Computational Storage Technical Working Group.
The architecture has had many names since its beginnings. SNIA and a group of founding member companies standardized the term computational storage in 2018.
Interest in computational storage has grown as organizations face more unstructured data and requirements to store and process data at the edge. “There is simply no way for the CPU and DRAM complexes of our current computing architecture to effectively manage all that data if it has been stored on a storage device,” Shadley said. “By allowing the [computational storage device] to manage localized data and only return useful data, the overall system can behave more effectively.” 
The demand for computational storage devices will only intensify as the market understands that CPUs aren’t always the best way to process data, Shadley added.
Computational storage has numerous uses, including the following:

SamsungSamsung SmartSSD computational storage drive

The Samsung SmartSSD is an example of a computational storage drive.

The Samsung SmartSSD is an example of a computational storage drive.
A growing number of vendors incorporate computational storage into their offerings.
More information about text formats
Follow us:


Share this post:

Leave a Reply