Overview

The Fostering Accelerated Scientific Transformations, Education, and Research (FASTER) project is a means of financing acquisitive activity in high-performance data-analysis technologies. The platform adopts state of the art technologies such as high speed interconnect, cutting-edge CPUs and GPUs, NVMe (Non-Volatile MemoryExpress), and based storage, combined with the Innovative Liqid composable software-hardware approach.Transformative augmentations in fields related to big data practices, artificial intelligence and machine learning(AI/ML) techniques, and high-performance computing (HPC) technologies will be enabled by FASTER. In order to devise a single node, workflows on FASTER will actively integrate disaggregated GPUs and NVMe, allowing them to surpass conventional hardware limits. By supporting a technology that can effectively allot resources to support workflows, the platform removes significant congestion in research computing. The development of AI/ML models, health population informatics, genomics, bioinformatics, agricultural sciences, life sciences, oil and gas simulations, and many more are included in the transformative research projects benefiting from FASTER. Further, the program contributes to code development, education, and the workforce development goals of several National Science Foundation (NSF) Big Ideas.​​ FASTER will assist researchers in the Texas A&M University System and their collaborating institutions. Moreover, the NSF XSEDE (Extreme Science and Engineering Discovery Environment) program will allocate thirty percent of FASTER’s computing resources to researchers nationwide.

Our Purpose

In order to successfully increase participation in computing, FASTER will coordinate a triplicate effort by focusing on training, outreach, and education. FASTER will broaden participation in computing at the K-12, collegiate, and professional levels, will support existing efforts that promote STEM (Science, Technology, Engineering, and Mathematics), and is devised to deepen the involvement of conventionally underrepresented groups in computing and STEM. The findings gathered from FASTER will be shared with the community.

PI/Co-PI Team

• Honggao Liu (Principal Investigator, Texas A&M University)
• Raymundo Arroyave (Co-Principal Investigator, Texas A&M University)
• Dilma Da Silva (Co-Principal Investigator, Texas A&M University)
• Zhangyang Wang (Co-Principal Investigator, University of Texas at Austin)
• Zhe Zhang (Co-Principal Investigator, Texas A&M University)