Buckle your seatbelt. We are in the middle of a historic moment. A decade ago, seemingly in a galaxy far, far away, there used to the universe where we would need to program a computer so that it knew how to do things. We are however in a rouge reality now where machines can learn from experience. In fact, just last week, I was reading an article from MIT Technology Review, where a Machine Learning algorithm simply listens to Bach, then writes its own music in the same style. R2D2 would be proud.
In just the past three years, there has been a lot of advancements like this in the field of Machine Learning. One of the main reasons for this was companies like Facebook, Baidu, Microsoft and others are open sourcing their machine learning software to foster collaboration and to unlock the vast possibilities for humans to more effectively innovate with machines. [Off topic: Did you know Mellanox was the 6th most active contributor (by lines changed) in Linux 4.8 kernel?]
To that end, last week Mellanox hosted a workshop on Machine Learning to nurture a vibrant ML and big data community and to help foster collaboration to speed innovation with attendees from more than 15 companies including Tencent, Alibaba, Baidu, JD, Didi, Meituan, Sensetime, Face++, Horizon, Hisense, Xiaomi, Meizu, PerfXlab, Novumind and Momenta.
The full day workshop had an exciting agenda with speakers from Baidu, JD, ICT, NVidia, PerfXLab and Mellanox, all with the aim of helping attendees learn how to tap into the power of Big Data and Machine Learning with RDMA, GPU Direct Technology, rCUDA, Machine Learning Use cases and optimization.
The workshop kicked off with presentation from Eyal Waldman, CEO of Mellanox, who highlighted the new possibilities of Machine Learning with Intelligent Network.
This was followed by a presentation from Yunquan Zhang from ICT, leader of China HPC and Big Data Community. He mapped out the landscape of HPC and Big Data/Machine Learning trends in China. One of the interesting trends was that 60 percent of Top100 machines in China are from Web2.0 companies running HPC applications and growing faster than traditional scientific computing labs and government organizations.
Chuan Lu from NVidia presented how NVidia’s GPU Technology accelerates deep learning and how GPUDirect RDMA, by jointly partnering with Mellanox, helps them achieve it.
Later Jie Zhou from Baidu introduced how RoCE (RDMA over Converged Ethernet) accelerates Baidu’s deep learning framework. The key takeaway from his presentation was how migrating from standard TCP/IP to RDMA helped Baidu reduce the computing and communication runtime rate from 1:3 to 1:1. This was by far the greatest achievement for Paddle’s speedup.
Yu Chen from JD introduced how Machine Learning and Mellanox’s Infiniband helps them drive their new business model for intelligent customer service. Their entire ML system was based on a cluster of 20 GPU servers interconnected with Infiniband fabrics.
Xianyi Zhang from PerfXLab introduced an optimized deep learning performance with their software.
Finally, Qingchun Song from Mellanox rounded out the day with a talk on how intelligent network helps accelerate big data and Machine Learning applications including the RDMA acceleration for Spark and Tensorflow.
With the grand success of our first workshop, we look forward to hosting many more to help nurture and foster a collaborative effort to help advance the field of Machine Learning and Artificial Intelligence. Today, we live in a semantic economy where everything is interconnected and businesses maintain their edge over others by creating new informational values from machine learning. Power no longer resides at the top of the heap, but rather the center of intelligent networks. May the force of Machine Learning and AI be with you!