Sumit Gupta, VP, HPC & Data Analytics at IBM
Over at the IBM System Blog, Sumit Gupta writes that the company’s new IBM Power System 822LC with Nvidia Tesla P100 GPUs is already demonstrating impressive performance on Deep Learning training applications.
Deep learning training speed measures how quickly and efficiently a deep neural network can be trained to identify and categorize information within a particular learning set. Deep learning software frameworks scale well with GPU accelerators and system bandwidth. The combination of NVIDIA Tesla P100 GPUs connected to each other and the POWER8 CPU by NVIDIA NVLink makes the new IBM S822LC for HPC a very powerful platform for deep-learning training.
Performance in Minutes of Tesla M40/PCIe system vs Tesla P100/NVLink System
“Last week, our deep learning performance team reached a new milestone. When I last wrote about this workload three weeks ago, we had successfully trained AlexNet, a common benchmark for deep-learning training, to 50 percent accuracy in 1 hour, 44 minutes. This week, we’re seeing results that have reduced the training time in the same test to 57 minutes; under an hour! This training data was collected with the latest system firmware along with system and software optimizations to tune overall S822LC system performance. As we continue to better understand the capabilities of the S822LC for HPC, we’ll continue to share progress on how we are fine-tuning the system for today’s deep-learning demands.”
