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8:30-9:35 AM
SARC-301-1: Computational Storage Applications (System Architectures Track)
Paper Title: High Performance Object Storage of AI applications

Paper Abstract: Computational storage can bring unique benefits in increasing the efficiency of CPU utilization in a data processing system for AI applications such as inferencing. This talk will discuss the benefits of leveraging Computational Storage in a disaggregated storage environment. We will demonstrate the ability of a solution to complement the CPU by taking away tasks that benefit from In-Situ processing within the storage device, thereby improving the overall system performance while lowering the TCO. Disaggregated storage is particularly attractive when using computational storage since scaling storage naturally yields to scaling of tasks that can be accelerated using computational storage. We experimented with accelerating the S3 Select functionality using our disaggregated computational storage (DCS) platform. Data tagging and partitioning utilizing sharding aspect of DCS platform further enhances ability to provide even greater performance for large data processing with parallel execution.

Paper Author: Mayank Saxena, Sr. Director Engineering, Samsung Semiconductor

Author Bio: Mayank is a seasoned storage engineering leader & entrepreneur focused on building innovative products at scale. He has 20+ years in technology R&D, software, product development, and team building experience at companies like NetApp, Microsoft Research, NortekControl and HP Labs. He successfully co-founded startup in the field of Cloud and IoT data processing. As an inventor he holds multiple US patents in the area of storage, data security and distributed networking. He holds an M.S. in Computer Science from USC