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9:45-10:50 AM
SARC-301-2: Computational Storage: The Road Ahead (System Architectures Track)
Paper Title: Making Real File Systems Faster with Applied Computational Storage

Paper Abstract: The exploration of computation near flash storage has been prompted by the advent of network-attached flash-based storage enclosures operating at tens of gigabytes/sec, server memory bandwidths struggling to keep up with network I/O bandwidths, and the need for massive data storage, management, manipulation and analysis. Multiple tasks from analytical/indexing functions to compression, erasure encoding, and deduplication are all more performant, efficient and economical when performed near storage devices. The NVMe Computational Storage standard requires computational storage offload demonstrations to ensure usefulness for task offloads at end-user sites. Demonstrating a standards-based ecosystem for offloading computation to near data storage is a contribution to the computing, networking, and storage communities. The Accelerated Box of Flash (ABOF) project (a collaboration between Eideticom, Nvidia, Aeon, SK hynix, and LANL) has produced a novel network-attached computational storage system that allows host applications to directly leverage programmable computational elements in the ABOF without hiding any of the computation behind a block storage interface.

Paper Author: Brad Settlemyer, Principal Staff, NVIDIA

Author Bio: Brad Settlemyer is a principal researcher in NVIDIA Networking's Advanced Technology group. Before coming to NVIDIA he spent 7 years at Los Alamos National Laboratory where he lead the storage systems research efforts within the Ultrascale Research Center and his team was responsible for designing and deploying state of the art storage systems for enabling scientific discovery. Prior to Los Alamos he was a storage systems researcher at Oak Ridge National Laboratory. He received his Ph.D in computer engineering from Clemson University in 2009 with a research focus on the design of parallel file systems. His experience includes projects ranging from ephemeral file system design to archival storage systems using molecular information technology and he has published papers on emerging storage systems, long distance data movement, system modeling, and storage system algorithms. His work has been featured in national media, in 2019 his team won an R&D100 award for their work on DeltaFS, in 2020 their work on computational storage was recognized with a Government Innovation Award, and in 2021 he won an R&D100 award for ADS Codex a system for encoding digital data into DNA molecules.