II-NEW: GEARS - An Infrastructure for Energy-Efficient Big Data Research on Heterogeneous and Dynamic Data

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Big data technologies have been successfully applied to many disciplines for knowledge discovery and decision making, but the further growth and adoption of the big data paradigm face several critical challenges. First, it is challenging to meet the performance needs of modern big data problems which are inherently more difficult, e.g., learning of heterogeneous and imprecise data, and have more stringent performance requirements, e.g., real-time analysis of dynamic data. Second, power consumption is becoming a serious limiting factor to the further scaling of big data systems and the applications that it can support. These challenges demand a new type of big data systems that incorporate unconventional hardware capable of accelerating data processing and accesses while lowering the system's power consumption. Therefore, this project is developing the needed computational infrastructure to support GEARS (an enerGy-Efficient big-datA Research System) for studying heterogeneous and dynamic data using heterogeneous computing and storage resources. GEARS is a one-of-kind, energy-efficient big-data research infrastructure based on cohesively co-designed software and hardware components. It enables a variety of important studies on heterogeneous and dynamic data and advances the scientific knowledge in computer science as well as other data-driven disciplines. It enhances the training of a large body of undergraduate and graduate students, including many from underrepresented groups, by supporting unique research and education activities. Finally, it also benefits the society by contributing new open-source solutions and with potential commercial applications in support of heterogeneous and dynamic data analysis. The hardware of GEARS includes a cluster of data nodes equipped with heterogeneous processors and storage devices and fine-grained power management capability. The software is developed upon widely-used big data frameworks to support unified programming across CPUs, GPUs, and FPGAs and transparent data access across a deep storage hierarchy integrating DRAM, NVM, SSD, and HDD. GEARS also enables novel systems and algorithms research on learning heterogeneous and dynamic data, including (1) new algorithm partitioning and scheduling schemes for using heterogeneous accelerators and optimizing the performance and energy efficiency of big data tasks; (2) new I/O scheduling and data staging strategies for performance and energy efficiency of the deep big-data storage hierarchy; (3) multi-phase, out-of-core decomposition techniques for large-scale tensors; (4) real-time visual analytics system that links streaming media with simulations for anticipatory analytics; (5) multi-modal deep learning methods with heterogeneous social data; (6) new computational tools for real-time analysis of social unrest using social media; (7) scalable, adaptive, and interactive team detection and assemble system for designing high-performing teams using big network data; (8) rare category analysis and heterogeneous learning algorithms for fast and accurate rare event discoveries with large and heterogeneous social data; and (9) new distributed machine learning framework for learning semantic knowledge from Web-scale images/videos with incomplete/noisy textual annotations. All project results will be shared with the broader community via the project website (http://gears.asu.edu). Publications will be listed on the website with links to their publishers. Data and software downloads will listed on the website with instructions on how to use them. Source code will be hosted on GitHub and a direct link to the repository will also be listed on the project website.