DDN Success Story: Cambridge-1
A NVIDIA SUCCESS STORY
The NVIDIA Cambridge-1 system is accelerating health research such as medical imaging, genomics, and drug discovery. Listen in to Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA, as he talks about why NVIDIA chose DDN storage solution to help build the UK’s most powerful AI supercomputer.
DDN Success Story: NVIDIA Selene
DDN NVIDIA SELENE VIDEO
Find out why NVIDIA chose DDN A3I storage to power one of the world’s most powerful AI supercomputers, and listen in to Prethvi Kashinkunti, Senior Data Center Systems Engineer at NVIDIA, share about the process of designing, building and administering deep learning supercomputers at NVIDIA.
Since NVIDIA’s invention of the GPU in 1999, the company has set new standards.
These standards include computing innovation, deep-learning and data analytics.
With the introduction of the NVIDIA DGX™ A100, NVIDIA raised the bar once more.
By consolidating the power of an entire data center into a single platform, NVIDIA is revolutionizing how complex machine learning workflows and AI models are developed and deployed in an enterprise.
This computing marvel also led to the creation of Selene.
Selene is the world’s seventh fastest computer in total performance and the world’s fastest commercially-available system – the NVIDIA DGX SuperPOD™ Solution.
DGX A100 systems are being used to fight COVID-19, fuel autonomous vehicles, develop superhuman language understanding, and transform almost every facet of business with AI.
Accelerating AI at Scale with Selene DGX A100 SuperPOD and DDN Parallel File System Storage
Learn about the behavior of data-intensive workloads on the Selene DGX A100 SuperPOD deployment and DDN’s A3I storage.
We’ll showcase the impact of using a parallel file system to optimize the data path and maximize GPU utilization for high-performance AI workloads. Presented by P. Kashinkunti (NVIDIA) W. Beaudin (DDN).
"Having a partner who stands shoulder-to-shoulder with our engineers to solve the big challenges is where the true value comes from. We’re definitely pushing the boundaries of what’s possible today with DDN while exploring new frontiers for the future."
~ Michael Houston
Chief Architect - AI Systems
CLOSER COLLABORATION: GPUDIRECT STORAGE
DDN was the first storage provider to certify with NVIDIA GPUDirect technology, part of the NVIDIA Magnum IO Suite, to stream data direct into DGX GPUs.
Removing this bottleneck increases efficiency by eliminating data copying via host memory, reducing latency, and freeing CPU resources for other tasks.
2021 LUSTRE USER GROUP
UF Information Technology (UFIT) hosted the 2021 Lustre User Group (LUG) conference in partnership with Open Scalable File Systems (OpenSFS) and Platinum Sponsor, DDN.
The annual LUG conference is the high performance computing industry’s primary venue for discussion on the open source Lustre file system and other technologies. The conference focuses on the latest Lustre developments and allows attendees to network with peers.
Using Parallel File Systems for AI – Does it Work?
Julie Bernauer, Director of Deep Learning Systems Engineering at NVIDIA, talks about using a parallel file system for AI workloads and lessons learned along the way.
She discusses what it took to build a flexible, simple solution for both cluster admins and users.
Accelerating AI at Scale with Selene DGX A100 SuperPOD and Lustre
Julie Bernauer, Director of Deep Learning Systems Engineering at NVIDIA, and Prethvi Kashinkunti, Deep Learning Systems Engineer at NVIDIA, discuss NVIDIA’s Selene Supercomputer and the how and why behind building such an advanced system.
DDN’s AI400X delivers scalable petabytes of powerful storage for NVIDIA Selene and NVIDIA DGX SuperPOD
Extremely fast storage needed to fuel the exponential growth of AI models, drive increasing demand for higher data rates per GPU and I/O bandwidth
High degrees of parallel access to small and large datasets necessitated balanced storage performance
Storage architecture flexibility with easy horizontal scalability required for incremental and on-demand data center expansion
Dramatic increase in AI workload performance enables organizations to iterate faster and boost data science productivity
Modular platform ensures AI infrastructure scalability with greater speed and cost efficiency
Fully integrated AI reference architectures democratize AI infrastructure for diverse workloads with streamlined deployment and operation