In order to increase the speed of highly distributed workloads, it is important to move the processing closer to the data. Computational storage processing is a technique for accelerating these types of workloads by multiple orders of magnitude.
We’ll examine how computational storage processing functions and how it can speed up highly distributed workloads. We’ll also examine some challenges to establishing this technology as a mainstream technology.
It is necessary to understand how data is typically processed in a scaled system before understanding computational storage processing. The data is usually collected at a central location, then processed by the CPU located there, and then returned to the user.
However, instead of being processed by a CPU, computational storage processing uses a microprocessor that is specifically designed for this task. Data is still collected at a specific location with computational storage processing. In most cases, these microprocessors are located on the same physical disk as the data they are processing, removing the need to transfer data across a network.
The primary benefit of computational storage processing over traditional data processing is its ability to dramatically increase data processing speed. In some cases, it has been shown to increase data processing speed by multiple orders of magnitude.
It is also possible to reduce the load on a CPU through computational storage processing, since many of the tasks traditionally performed by the CPU can now be offloaded to these specialized microprocessors. Because of this, CPUs can be used for other tasks or turned off completely when not in use, thereby reducing energy consumption.
Additionally, computational storage devices often have built-in error-correcting mechanisms, making them more reliable than CPUs. This means they can continue to operate if there are errors in the data they are processing.
Among the main challenges of computational storage processing is the fact that it requires special hardware to work. As a result, it is not compatible with all systems and can require significant changes to existing infrastructure. As a result, it can be difficult and costly to adopt for many organizations.
Furthermore, computational storage devices are frequently slower than traditional CPUs when compared to traditional CPUs. As such, they may not be suitable for all applications that require speed, such as real-time applications or those that need to process large amounts of data quickly.
A final challenge associated with computational storage devices is their tendency to generate more heat than computing outside the shelf, which requires additional cooling.
While computational storage devices offer many benefits over traditional architectures, several challenges must be overcome before they can be more widely adopted. These include compatibility issues, cost, cooling requirements, and speed limitations. In spite of these challenges, computational storage devices offer great promise to speed up highly distributed workloads and reduce CPU load.
Storage Short Take - Dr J Metz - https://jmetz.com/2022/07/storage-short-take-47/