SNIA-SDC23-Manno-Leveraging-Computational-Storage-Simulation-Science.pdf

1、1|2023 SNIA.All Rights Reserved.Virtual ConferenceSeptember 28-29,2021Leveraging Computational Storage for Simulation Science Storage System DesignDominic MannoLA-UR-23-299172|2023 SNIA.All Rights Reserved.Computational Storage Why Need flexibility in where computation is done(host,network,device)as

2、 economics will change over time Its not just energy and time to insight,some of these analytics require the same size analytics footprint as the simulation footprint(petabyte of ram)making analytics not always as feasible Data Agnostic Offloads Server memory BW does not allow many passes over strea

3、ming data Data Aware Offloads Analytics is often multiple orders of magnitude less reading than writing You just have a hard time finding what you are looking for(filter/index/histogram/etc.)Can we add metadata/indexing/ordering to data as it is written with almost no overhead and reap huge wins on

4、read(time,hdwr resources,energy)For Science Particle methods-“Ordered”row-based analytics(KV)Grid methods-columnar-based analytics Large Complex Grid methods -THE KITCHEN SINK3|2023 SNIA.All Rights Reserved.Data Agnostic Offload(ABOF)Offload erasure,encoding,compression and make it possible to run e

5、ach anywhere you like(host,network/DPU,storage/CSA/CSP/CSD)Consumable:Open ZFSKernel module for offloads to register intoZFS mods unstreamedTested under LustreData written is same,upon failure just fall back to hostNo app changes other than faster on less capable host Not a block interface,a program

6、ming interface Remote malloc,copy,operate on Great partnership4|2023 SNIA.All Rights Reserved.Analytics Under Erasure CSHDDData Aware Row And Col Based Analytics Offload TrialsRow(Point methods in Science)Early KV-CSD work was hashed based but many KV workloads require“order from chaos”Leverage LANL