Rapid GPU Evolution at Chinese Web Giant Tencent

Like other major hyperscale web companies, China's Tencent, which operates a massive network of ad, social, business, and media platforms, is increasingly reliant on two trends to keep pace. The first is not surprising--efficient, scalable cloud computing to serve internal and user demand. The second is more recent and includes a wide breadth of deep learning applications, including the company's own internally developed Mariana platform, which powers many user-facing services. When the company introduced its deep learning platform back in 2014 (at a time when companies like Baidu, Google, and others were expanding their GPU counts for speech and image recognition applications) they noted their main challenges were in providing adequate compute power and parallelism for fast model training. "For example," Mariana's creators explain, "the acoustic model of automatic speech recognition for Chinese and English in Tencent WeChat adopts a deep neural network with more than 50 million parameters, more than 15,000 senones (tied triphone model represented by one output node in a DNN output layer) and tens of billions of samples, so it would take years to train this model by a single CPU server or off-the-shelf GPU."