GPU accelerated computing continues to provide excellent high performance computing support to help scientific computing, AI, DL, and ML computing continue to accelerate. Under the development trend in recent years, HPC simulation computing has also begun to use deep learning related methods. To improve computing performance and accuracy more efficiently, we can see that GPU accelerated computing is highly helpful to scientific research. Further research continues, GPU Computing, with the help of NVLink proposed by NVIDIA, has effectively improved the overall Multi-GPU In terms of computing performance, the computing of the multi-GPU servers is also taken care of by the Infiniband high-speed interconnect network that continues to improve performance, helping the GPU Supercomputer to provide better computing support.
Recently, NVIDIA has put more attention to use NVLink to enhance the communication bandwidth between the CPU and the GPU. It is expected that through the new generation of GPU accelerated computing architecture design, help high performance computing can be improved to a better state, and more problems can be solved in the field of scientific research. Accelerated computing requires more than just powerful chips. We achieve incredible speedups through full-stack invention, from the chips and systems to the algorithms and apps they run. In this session, we will share with you new generation chip, architecture design, full-stack software SDK and libraries and HPC compiler related information.