Name: Lingda Li
Pronouns: he/him/his
Biography:
Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with recent focuses on performance simulation/modeling, memory system, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research between 2014 and 2016, He obtained PhD in computer architecture from the Microprocessor Research and Development Center, Peking University in 2014.
Institution/Lab: Brookhaven National Laboratory
Website: https://www.lilingda.com
SRP Collaboration Topic/Title: Building a Program Database to Facilitate Machine Learning-based Computing Research
Field or research area: Computer Science
Please select all the topical areas that apply to your project:
Computer Science (i.e., architectures, compilers/languages, networks, workflow/edge, experiment automation, containers, neuromorphic computing, programming models, operating systems, sustainable software); Machine Learning and AI
Brief Abstract:
Machine learning (ML) has shown great success in many domains, and increasing efforts are applying ML in the field of computer science and engineering, for instance, to design more efficient hardware and software. Due to its data driven nature, it requires thousands if not millions of programs to generate training data in ML techniques, and the computing research community does not have such a collection of programs in hand. Particularly, traditional benchmarks are short in number, and source code hosting websites (e.g., GitHub) have abundant code snippets instead of executable programs. This project aims to bridge this gap by creating a database including a large number of executable programs. The main task of this project is to collect/write programs that stretch both computing power and memory capability of modern computers, leveraging coding exercise websites such as LeetCode and CodeForces. This database will subsequently be used for program performance predictive model training and testing. Besides the main purpose mentioned above, this project will also help students sharp their programming skills and benefit their future careers in both academia and industry, giving the fact that these coding exercise websites are the de facto means to prepare technical interviews.
Desired relevant skills, background, or interests:
Major in computer science/engineering or related fields; proficiency at one or more compilable programming languages (e.g., C/C++); knowledgeable or interest to learn about computer hardware/architecture.
Other comments:
Do any special requirements apply? Permanent Resident OK; International OK
Other, specify:
Keywords:
Programming; performance analysis; machine learning
Lightning Talk Title: Building a Program Database to Facilitate Machine Learning-based Computing Research