GTC 2021 Keynote with NVIDIA CEO Jensen Huang

NVIDIA CEO Jensen announced NVIDIA’s first data center CPU, Grace, named after Grace Hopper, a U.S. Navy rear admiral and computer programming pioneer. Grace is a highly specialized processor targeting largest data intensive HPC and AI applications as the training of next-generation natural-language processing models that have more than one trillion parameters.

Further accelerating the infrastructure upon which hyperscale data centers, workstations, and supercomputers are built, Huang announced the NVIDIA BlueField-3 DPU.

The next-generation data processing unit will deliver the most powerful software-defined networking, storage and cybersecurity acceleration capabilities.

Where BlueField-2 offloaded the equivalent of 30 CPU cores, it would take 300 CPU cores to secure, offload, and accelerate network traffic at 400 Gbps as BlueField-3— a 10x leap in performance, Huang explained.


Deep Learning Training Performance with Nvidia A100 and V100 on Dell EMC PowerEdge R7525 Servers

Articles from:  Deep Learning Training Performance on Dell EMC PowerEdge R7525 Servers with NVIDIA A100 GPUs

CUDA Basic Linear Algebra

  • For FP16, the HGEMM TFLOPs of the NVIDIA A100 GPU is 2.27 times faster than the NVIDIA V100S GPU.
  • For FP32, the SGEMM TFLOPs of the NVIDIA A100 GPU is 1.3 times faster than the NVIDIA V100S GPU.
  • For TF32, performance improvement is expected without code changes for deep learning applications on the new NVIDIA A100 GPUs. This expectation is because math operations are run on NVIDIA A100 Tensor Cores GPUs with the new TF32 precision format. Although TF32 reduces the precision by a small margin, it preserves the range of FP32 and strikes an excellent balance between speed and accuracy. Matrix multiplication gained a sizable boost from 13.4 TFLOPS (FP32 on the NVIDIA V100S GPU) to 86.5 TFLOPS (TF32 on the NVIDIA A100 GPU).


MLPerf Training v0.7 ResNet-50

Both runs using two NVIDIA A100 GPUs and two NVIDIA V100S GPUs converged at the 40th epoch. The NVIDIA A100 run took 166 minutes to converge, which is 1.8 times faster than the NVIDIA V100S run. Regarding throughput, two NVIDIA A100 GPUs can process 5240 images per second, which is also 1.8 times faster than the two NVIDIA V100S GPUs.

HPC Application Performance with Nvidia V100 versus A100 on Dell PowerEdge R7525 Servers

Articles Taken from: HPC Application Performance on Dell PowerEdge R7525 Servers with NVIDIA A100 GPGPUs

Difference between Nvidia A100 GPGPU versus Nvidia V100s GPGPU

Form factor SXM4 PCIe Gen4 SXM2 PCIe Gen3
GPU architecture Ampere Volta
Memory size 40 GB 40 GB 32 GB 32 GB
CUDA cores 6912 5120
Base clock 1095 MHz 765 MHz 1290 MHz 1245 MHz
Boost clock 1410 MHz 1530 MHz 1597 MHz
Memory clock 1215 MHz 877 MHz 1107 MHz
MIG support Yes No
Peak memory bandwidth Up to 1555 GB/s Up to 900 GB/s Up to 1134 GB/s
Total board power 400 W 250 W 300 W 250 W

Benchmark Results (In Summary)

HPL performance comparison for the PowerEdge R7525 server with either NVIDIA A100 or NVIDIA V100S GPGPUs

HPCG performs at a rate 70 percent higher with the NVIDIA A100 GPGPU due to higher memory bandwidth

HPCG performs at a rate 70 percent higher with the NVIDIA A100 GPGPU due to higher memory bandwidth

Getting on board Nvidia GPGPU on CentOS KVM

  1. For vGPU test you’ll need a license, which can be requested here:
  2. Other documentation for installing vGPU on  Red Hat / CentOS is here:
  3. Virtual GPU Software Quick Start Guide

In summary the steps are:
– Install a piece of sw in the host/hypervisor to help virtualize GPUs
– Install the GPU drivers inside the guest OS of the VMs
– Install a license server (flex) for the licensing
– Configure license server and settings within the VM to connect to the license server