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DTSTART:19700308T020000
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DTSTAMP:20260522T150126Z
LOCATION:D173
DTSTART;TZID=America/Chicago:20181115T103000
DTEND;TZID=America/Chicago:20181115T110000
UID:submissions.supercomputing.org_SC18_sess270_exforum101@linklings.com
SUMMARY:Next Generation NVMe-Native Parallel File System Accelerating AI W
 orkloads
DESCRIPTION:Liran Zvivbel (WekaIO Ltd)\n\nAny IT infrastructure that suppo
 rts an AI and analytics workflow needs to be able to handle a large number
  of files, and large amounts of data storage with high throughput access t
 o all the data. Legacy file systems can’t supply high throughput and high 
 file IOPS, as they were designed for HDD and are not suitable for low late
 ncy, small file IO and metadata heavy workloads that are common in AI and 
 analytics. This results in I/O starvation, a major problem for an AI syste
 m. We’ll introduce a next-gen massively parallel shared file system that i
 s NAND FLASH and NVMe-optimized, which is solving the I/O starvation probl
 em.  \nWhat you will learn:\n- Case study: how a large autonomous driving 
 car manufacturer maximizes their investment in GPUs by ensuring they are s
 aturated with data\n- How to make I/O compute bound again\n- How to maximi
 ze data accessibility for your cluster\n\nTag: Architectures, Memory, Stor
 age\n\nSession Chair: Paul Peltz (Oak Ridge National Laboratory (ORNL))\n\
 n
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