GeForce RTX 3080을 이용한 TensorFlow and NAMD 수행능력

RTX 3080을 이용한 AI 성능 [이런 Test를 해보는 해외 기술 블로거.. 대단하다.]

www.pugetsystems.com/labs/hpc/RTX3080-TensorFlow-and-NAMD-Performance-on-Linux-Preliminary-1885/#Results

Test system

Hardware

• Intel Xeon 3265W: 24-cores (4.4/3.4 GHz)
• Motherboard: Asus PRO WS C621-64L SAGE/10G (Intel C621-64L EATX)
• Memory: 6x REG ECC DDR4-2933 32GB (192GB total)
• NVIDIA RTX3080 and RTX TITAN

Software

• Ubuntu 20.04 Linux
• Docker version 19.03.12
• NVIDIA Driver Version: 455.23.04
• nvidia-container-toolkit 1.3.0-1
• NVIDIA NGC containers
  • nvcr.io/nvidia/tensorflow:20.08-tf1-py3
  • nvcr.io/hpc/namd:2.13-singlenode

Test Jobs

• TensorFlow-1.15: ResNet50 v1, fp32 and fp16
• NAMD-2.13: apoa1, stmv

Conclusions

NVIDIA is keeping the "spirit" of Moore's Law alive! The "Ampere" GPU based RTX 3080 is a significant step forward in performance-per-dollar. The results presented in this post are preliminary. They will only get better as the driver matures and as software developers tune their applications for better performance on the architecture.

I can tell you that some of the nice features on the Ampere Tesla GPUs are not available on the GeForce 30 series. There is no MIG (Multi-instance GPU) support and the double precision floating point performance is very poor compared to the Tesla A100 ( I compiled and ran nbody as a quick check). However, for the many applications where fp32 and fp16 are appropriate these new GeForce RTX30 GPUs look like they will make for very good and cost effective compute accelerators.