SNIA-SDC23-Emberson-Singhal-Crasta-Fabric-Attached-Memory.pdf

《SNIA-SDC23-Emberson-Singhal-Crasta-Fabric-Attached-Memory.pdf》由会员分享，可在线阅读，更多相关《SNIA-SDC23-Emberson-Singhal-Crasta-Fabric-Attached-Memory.pdf（21页珍藏版）》请在三个皮匠报告上搜索。

1、1|2023 Hewlett Packard Enterprise.All Rights Reserved.Virtual ConferenceSeptember 28-29,2021Fabric Attached MemoryHardware and Software ArchitectureClarete Crasta,Dave Emberson,Sharad Singhal2|2023 Hewlett Packard Enterprise.All Rights Reserved.AgendaMotivation Using fabric attached memory in HPC Ar

2、chitecture Software stackResults and use cases Microbenchmarks Arkouda-based graph processingSummary&future work 3|2023 Hewlett Packard Enterprise.All Rights Reserved.Need quick answers on larger data sizesEXPONENTIALLY INCREASINGDATAEXPLODINGDATASOURCESSHRINKINGTIME TOACTIONMassive advancesin compu

3、ting powerXX=NEEDED EVERYWHEREData nearly doubles every two years(2013-25)Source:IDC Data Age 2025 study,sponsored by Seagate,Nov 2018 4|2023 Hewlett Packard Enterprise.All Rights Reserved.AI and machine learning Applications in simulation,modeling,large language models HPC workflows/pipelines such

4、as those in genomics Applications to transform data in a workflow with large intermediate data sets Large scale graphs Applications inSecurity:website reputation,malware detectionSocial networks:Community detection,link predictionAdvertising:brand reputation,click-through predictionInternet of thing

5、s:traffic management,risk detection Applications have enormous memory footprints Datasets can be 10s-100s of terabytes to multi-petabytes in size Analytics performance is currently limited by the total amount of DRAM in the HPC cluster Random data access patterns Processor caches are inefficient due

6、 to low hit rates Distributed applications often require expert programmers Data movement introduces high latencies Demand paging to SSDs is very slow Moving data consumes time and energy Difficult to optimize system resource locality or network performanceCharacteristics of emerging applications5|2