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Rong Zhou in the news



3 Myths Dispelled About GPU & Machine Learning
19 November 2015 | AlwaysOn Blogs
by Rong Zhou, PARC

This article was originally featured in CIO Review.

With open-source big data frameworks such as Apache Hadoop and Spark in the spotlight, most people are probably unfamiliar with the concept of using GPUs (graphics processing units) in either big data or analytics-rich applications. 9 out of 10 cases, the acronym is mentioned in the context of display hardware, video games, or how supercomputers can be built these days. For serious IT managers or data scientists, GPUs may seem too exotic to be the hardware of choice for big data infrastructure.


Big Data
3 November 2015 | CIO Review
by Rong Zhou, PARC (contributed article)

It’s true that GPUs are not as easy to program as their CPU counterparts, due to their unconventional processor designs, says Rong Zhou, senior researcher and Manager of the High-Performance Analytics area of the Interaction and Analytics Laboratory at PARC.

At PARC, we are researching ways to automatically generate optimized GPU code from high-level specifications of the algorithm with little knowledge about the underlying hardware. Once completed, it will enable fast GPU programming and real-time big data analytics running on top of a wide array of GPUs, each of which can have different hardware characteristics such as their compute capabilities, the number of streaming multiprocessors (SMs), the number of registers per SM and etc. In the long run, we would like to support other forms of accelerator-based big data analytics besides GPU, including those based on Intel Xeon Phi coprocessors.


FRACSAT: An integrated Lifecycle Decision Support Toolkit for Fractionated Spacecraft Architectures
13 May 2011 | SpaceRef
by Ames Research Center

"PARC and its partners will design, develop, and deliver an integrated lifecycle decision-support toolkit for fractionated spacecraft architectures. When completed, the FRACSAT toolkit will enable space mission designers to rapidly generate feasible mission architectures, select optimal design solutions given programmatic uncertainty, justify the business case using mission-relevant cost and benefit metrics, and adapt to unforeseen events or changes during the program lifecycle for maximum mission impact."