NVIDIA GTC 2021 has just wrapped up with several joint announcements from NVIDIA and NetApp. NetApp shares with NVIDIA a vision and history of optimizing the full capabilities and business benefits of artificial intelligence for organizations of all sizes. The NVIDIA DGX A100 system is a next-generation universal platform for AI that deserves equally advanced storage and data management capabilities from NetApp.
At the heart of NVIDIA DGX A100 systems are 8 NVIDIA A100 Tensor Core GPUs; 6 NVSwitches built on third-generation NVLink technology for high-speed GPU-to-GPU communications; 8 Single-Port Mellanox ConnectX-6 HDR (high data rate) 200Gb/s InfiniBand host channel adapters (HCAs) for compute traffic; and 1 or 2 Dual-Port Mellanox ConnectX-6 HDR HCAs for external storage traffic that can run HDR speeds up to 200Gb/s InfiniBand or Ethernet.
Source: NVIDIA DGX A100 System Architecture
The NVIDIA DGX POD reference architecture combines DGX A100 systems, networking, and storage solutions into fully integrated offerings that are verified and ready to deploy. NetApp and NVIDIA are partnered to deliver industry-leading AI solutions. As an NVIDIA partner, NetApp offers two solutions for DGX A100 systems, one based on NetApp® ONTAP® AFF and the other based on NetApp EF-Series EF600 with BeeGFS. If your enterprise plans to run many distributed jobs using GPUs or partial vGPUs on an as-needed basis, and if you plan to use NFS and the rich data management functionality available in ONTAP, AFF solutions are a great fit. If you have fewer jobs using GPUs for long-running training operations and require the extreme performance of a parallel file system, consider E-Series solutions. Both solutions are accompanied by a reference architecture that includes observed bandwidth, IOPS, and training performance results under certain testing conditions.
Other storage solution vendors have published reference architectures as well, but they offer a motley set of results that superficially appear to paint some solutions as significantly superior to others. Readers are left to wonder: What do these results mean and how are we to interpret them? Are we POD racing on the same course?
First, a bit of a deeper dive into the DGX A100 system architecture. Recall that there are 8 ConnectX-6 HCAs intended for intra-DGX A100 compute traffic, and there are 1 or 2 dual-ported ConnectX-6 HCAs intended for external storage traffic. This means that for a real-world use case, the upper limit of single DGX A100 system storage throughput is the storage throughput of 1 or 2 dual-ported ConnectX-6 HCAs. NetApp’s approach to validation in the lab is based on real-world use cases. In this process, an external storage system serves data to multiple DGX A100 systems that work in concert, keeping NVLinks (and ultimately GPUs) busy using NVIDIA Collective Communications Library (NCCL) for distributed deep learning (DL) training and high-performance computing.
|NVIDIA DGX A100 systems||8||48|
|NetApp EF600 storage system||2 high-availability (HA) pair, includes 24x 1.92TB NVMe SSDs on each||4|
|NVIDIA QM8700 HDR IB switches||2 for compute cluster interconnect||2|
|2 for DGX access to BeeGFS servers||2|
|Lenovo SR655 servers||4 running BeeGFS server services||8|
|Solution||NetApp EF600 + BeeGFS|
|Block size (BW/IOPS)||1MB / 4KB|
|I/O (file) size||4GB|
|Jobs (BW/IOPS)||64 / 64|
Joey Parnell is a software architect and development lead at NetApp in the E-Series product group, responsible for reliability, availability, and data security features. Recently his focus has been technical leadership on durable, performant, reliable storage for video surveillance and AI solutions.
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