ARCS

RESEARCH

Our research has focused on system optimizations for interesting applications in modern eras, which include deep learning frameworks and big data analysis platforms. Our current interests include:

• Compiler Optimization for GPU/FPGA Computing
• Platform Optimization for Big Data/Machine Learning Frameworks
• System Software Design for Non-Volatile Memories
• Static/Dynamic Program Analysis for Performance and Security

Current Grants

• PF Class Heterogeneous High Performance Computer Development, National Research Foundation (NRF), 2016-2019, 2019-2021
  • In this projects, we are developing nation-wide supercomputing environment for big data analysis and deep learning applications to further enhance the researches in bio-medical, meteorological, nuclear sciences. From programming models and optimizations for GPUs and FPGAs to big data analysis and deep learning application development. Our encompasses key research in system software and collaboration research in computational science.
• Optimizing storage systems and application frameworks for next generation computing environment, Samsung Electronics, 2019-2022
  • In this project, we investigate programming environment for SmartSDD which comes with FPGA and SSD in a combined form factor.

[ Past Grants ]