Using Computational Storage to Handle Big Data

Andy Walls of IBM discusses using computational storage to handle big data in this presentation delivered at Flash Memory Summit 2020. Machine learning, deep learning and analytics all require enormous amounts of data. Moving all that data to servers for processing is becoming increasingly burdensome and time-consuming. Sure, processing cores and GPUs are more powerful than ever, but network bandwidth and DRAM cost limit how big a dataset can be analyzed. As such, off-loading tasks to storage can reduce network traffic and optimize the work done by expensive processors. Computational storage allows data to reside close to processing power, thus allowing processing tasks to be in-line with data accesses. NVMe SSDs and external storage appliances can act as many distributed processing engines that can perform compression, sorting, searching, and profiling. Inference engines placed in the storage can even do pre-processing for machine learning. Ultimately, much of the application could be distributed to all the ASICs, FPGAs, and small processing units in NVMe SSDs. Computational storage examples already in use illustrate ways to overcome these issues, and there are still other promising directions to explore for the future. Computational storage examples already in use illustrate ways to overcome these issues, and there are still other promising directions to explore for the future.

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