What is GEARS?

GEARS is an enerGy-Efficient big-datA Research System at arizona state university, for studying heterogeneous and dynamic data by employing heterogeneous computing and storage resources and co-designing the software and hardware components of the system. GEARS is sponsored by National Science Foundation award CNS-1629888. Currently GEARS is based on the following hardware resources:

CPU

512 Intel Xeon 2.4GHz CPU Cores

GPU

10 Nvidia K40 GPUs

FPGA

16 Nallatech 385A Cards with Altera Arria 10 GX 1150 FPGAs

Storage

3TB DRAM, 576TB SAS Disks, 25.6TB NVMe SSDs

NETWORK

10Gbps Ethernet and 40Gbps Infiniband

Project Team

Ming Zhao

ASU CIDSE Faculty

Data-intensive computing and storage systems

Fengbo Ren

ASU CIDSE Faculty

Hardware acceleration and parallel
computing solutions for data analytics

Hasan Davulcu

ASU CIDSE Faculty

Data mining techniques and tools for
structuring and organizing unstructured sources

Huan Liu

ASU CIDSE Faculty

Computational methods for data mining,
machine learning, and social computing

Selcuk Candan

ASU CIDSE Faculty

Management of non-traditional,
heterogeneous, dynamic, and imprecise data

Jingrui He

ASU CIDSE Faculty

Heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning

Baoxin Li

ASU CIDSE Faculty

Statistical inference and multimedia content
indexing and analysis

Ross Maciejewski

ASU CIDSE Faculty

Geographical visualization and visual analytics

Hanghang Tong

ASU CIDSE Faculty

Large scale data mining and machine learning

Jay Etchings

RC@ASU Director

Director of Operations, ASU Research Computing

Saman Biookaghazadeh

ASU CIDSE Ph.D. Student

Big-data, heterogeneous computing and Machine Learning

Yitao Chen

ASU CIDSE Ph.D. Student

Mobile computing and machine learning

Runyu Jin

ASU CIDSE Ph.D. Student

Big-Data and High Performance Computing

Yixing Li

ASU CIDSE Ph.D. Student

Hardware acceleration for data analytics and compressed neural network

Ragini Sharma

ASU CIDSE M.S. Student

Knowledge transfer and deep neural network

Michel Roger

ASU CIDSE Ph.D. Student

Heterogenous computing

Pravin Kumar Ravi

ASU CIDSE M.S. Student

Heterogenous computing and deep learning

 
 
 

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This material is based upon work supported by the National Science Foundation under Grant No. CNS-1629888. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Contact

  • Brickyard Engineering, 699 S Mill Ave,
    Tempe, AZ, 85281
    gears.asu@gmail.com