SANTA CLARA, Calif. – The leading hyperscale data center players continue to roll out new hardware designs to power machine learning applications. At the recent Open Compute Summit, Microsoft and NVIDIA unveiled a new hyperscale GPU accelerator for artificial intelligence workloads in the cloud.
The HGX-1 is an open source design that packs eight NVIDIA Tesla P100 GPUs in each chassis, with a switching design based on NVIDIA’s NVLink interconnect technology and the PCIe standard, which enables a CPU to dynamically connect to any number of GPUs (graphics processing units).
This approach allows cloud service providers using HGX-1 to offer customers a range of CPU and GPU machine instance configurations. Microsoft and NVIDIA built the new system in collaboration with Ingrasys, a subsidiary of Chinese hardware giant Foxconn.
The new hardware is part of Project Olympus, the Microsoft initiative to speed the development of open hardware optimized for cloud data centers, which features modular system binding blocks and early access to designs for the Open Compute Project community.
Microsoft Likes Extreme Scalability
“The HGX-1 AI accelerator provides extreme performance scalability to meet the demanding requirements of fast-growing machine learning workloads, and its unique design allows it to be easily adopted into existing data centers around the world,” wrote Kushagra Vaid, general manager and distinguished engineer, Azure Hardware Infrastructure, Microsoft, in a blog post.
The HGX-1 wasn’t the only AI hardware introduced at the Open Compute Summit. The Facebook infrastructure team unveiled its Big Basin machine learning server, which also features eight NVIDIA Tesla P100 accelerators connected by NVLink.
In artificial intelligence (AI), computers are assembled into neural networks that emulate the learning process of the human brain to solve new challenges. It’s a process that requires lots of computing horsepower, and hyperscale data center operators are using GPUs, FPGAs and ASICs offer more sophisticated processing architectures that can bring HPC-style parallel processing of workloads. This is especially important in machine learning, where neural networks must be trained by absorbing large datasets and refining its algorithms for accuracy. Once a system is trained, it uses inference to “learn” as it encounters new information.
NVIDIA Sees New Standard
NVIDIA’s graphics processing (GPU) technology has been one of the biggest beneficiaries of the rise of specialized computing, gaining traction with workloads in supercomputing, artificial intelligence (AI) and connected cars.
A CPU consists of a few cores optimized for sequential serial processing, while a GPU has a parallel architecture consisting of hundreds or even thousands of smaller cores designed for handling multiple tasks simultaneously. This has proven ideal for the data crunching required to train and power AI workloads.
