NVIDIA, Tesla K80 데이터 분석 가속기 공개
무슨 발표인가
- Tesla K40 대비 성능 2배 향상, 메모리 대역폭 2배 증가
- CPU 대비 10배 높은 성능
- 머신러닝·데이터 분석·과학 컴퓨팅 분야 지원
원문 (영어)
NVIDIA today unveiled a new addition to the NVIDIA Tesla Accelerated Computing Platform: the Tesla K80 dual-GPU accelerator , the world's highest performance accelerator designed for a wide range of machine learning, data analytics, scientific, and high performance computing (HPC) applications.
The Tesla K80 dual-GPU is the new flagship offering of the Tesla Accelerated Computing Platform , the leading platform for accelerating data analytics and scientific computing. It combines the world's fastest GPU accelerators, the widely used CUDA parallel computing model, and a comprehensive ecosystem of software developers, software vendors, and data center system OEMs.
The Tesla K80 dual-GPU accelerator delivers nearly two times higher performance and double the memory bandwidth of its predecessor, the Tesla K40 GPU accelerator. With ten times higher performance than today's fastest CPU, it outperforms CPUs and competing accelerators on hundreds of complex analytics and large, computationally intensive scientific computing applications .
Users can unlock the untapped performance of a broad range of applications with the accelerator's enhanced version of NVIDIA GPU Boost technology (PDF), which dynamically converts power headroom into the optimal performance boost for each individual application.
Industry-Leading Performance for Science, Data Analytics, Machine Learning The Tesla K80 dual-GPU accelerator was designed with the most difficult computational challenges in mind, ranging from astrophysics, genomics and quantum chemistry to data analytics.
It is also optimized for advanced deep learning tasks, one of the fastest growing segments of the machine learning field.
원문: NVIDIA News — "NVIDIA Unveils World's Fastest Accelerator For Data Analytics And Scientific Computing" (2024-04-05) 공식 원문: https://nvidianews.nvidia.com/news/nvidia-unveils-world-s-fastest-accelerator-for-data-analytics-and-scientific-computing