Last week at the Google I/O conference, it was revealed the search giant has been using its own custom-built ASIC to accelerate the machine learning capabilities that now underlies much of the Google cloud. The microprocessor, known as the Tensor Processing Unit (TPU), was designed by Google engineers to speed up the TensorFlow software that the company uses to drive much of its machine learning functionality. TensorFlow began as a machine learning research project, but later moved into production, and is now deployed in an array of applications, including Google Cloud Speech, Gmail, Google Photos, and Search. According to the company, TPUs have been churning away in their production datacenters for about a year, unbeknownst to its users.
Supercomputer-maker Cray has introduced Urika-GX, the company’s newest version of its enterprise-focused data analytics product line. With an emphasis on agility, the system merges the functionality of the existing Urika-GD and Urika-XA appliances, which provide platforms for graph-based and Spark/Hadoop-based analytics, respectively. Urika-GX wraps both of these capabilites into a single box, and does so largely with standard hardware and software.
The Times of India is reporting that the country will begin building a new series of domestically produced supercomputers, the first of which will be installed in August of 2017. The Rs 4,500-crore (about $670 million) project is being managed by the Centre for Development of Advanced Computing (C-DAC).
The World Community Grid (WCG) has been recruited to tackle the Zika virus, one of the fastest-spreading and most dangerous viruses in recent memory. The project, known as OpenZika, will use WCG computing resources to identify candidate drugs that exhibit anti-viral properties against the Zika organism.