Computational Storage is the Answer for Huge Data and Deep Problems (Apparently)

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Thursday, August 8, 2019
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 Computational storage accelerates applications by adding compute power where the data is stored. The results are fewer large data moves, less burden on CPUs and connections, and much greater scalability. Initial trials with large, complex systems programs show remarkable results. For example, by applying computational storage in an NVMe-based system, the RocksDB production in-memory database yields 6x the throughput with half the CPU requirements. Similarly, the widely used ZFS production filesystem becomes twenty times more power-efficient. Like results can be achieved in data-driven applications in AI/ML, HPC, real-time analytics, security, IoT, and content delivery networks. Furthermore, the advantages will surely increase as data stores keep increasing rapidly in size and customers want to delve deeper into problems to gain a competitive advantage.