High performance: How a multi-controller storage architecture shatters expectations for modern applications

NVMe-of and RoCE Storage Performance for GPU and GPUDirect at NVIDIA GTC Session # SS33030 (Click now to add to your conference agenda)

AI/ML/DL, Analytics and Virtualization with GPUs is transforming how we achieve extraordinary insights into extremely complex questions.  These new insights span industries like Federal, HPC & supercomputing, Large Enterprise and M&E.  Yet data storage IO with performance density and capacity density have not kept pace.

It is time to think differently about data storage for GPU-based applications.

IO must keep up.  You must have modern storage to rapidly ingest, analyze, visualize and respond in real-time at scale.  

Beyond a few days or a few weeks of ingest with DGX, you run out of NVMe capacity for GPU-based analytics and visualization tools.  You need storage that is as performant as locally attached NVMe, yet scales up and out in a linear fashion using low-latency, simple and cost-effective NVMe with RoCE to keep hungry GPUs satiated.  
In the upcoming GTC21 session High Performance: How a Multi-Controller Storage Architecture Shatters Expectations for Modern Applications  Pavilion will describe how its HyperParallel Data Platform:

  1. Offers months and years (not days and weeks) of analytics at the speed of thought.  
  2. Can deliver best-in-class performance across all vectors for block, file and object storage 
  3. Is proven with OmniSci, Graphistry, VMware and PixiT Media for your organization

For those looking to maximize on GPU investments and optimize pipelines, this is a must-attend session.

If you want to learn more about how the Pavilion is shattering expectations for GPU-based workloads, check out our other sessions and pre-session blogs:

How High Performance NVMe-oF Storage Accelerates CPU & GPU-Powered Virtualized Environments Demonstrated by Pavilion

How to respond to rapidly scaling Geospatial Intelligence with OmniSci and Pavilion

Visually investigating patterns in logs at scale with Graphistry, RAPIDS, and Pavilion