Damodar Sahasrabudhe
About Damodar Sahasrabudhe
Damodar Sahasrabudhe is a Member of Technical Staff specializing in performance optimizations for scientific computing and machine learning. He has extensive experience in high-performance computing and has worked with various organizations, including Deloitte and Cerebras Systems.
Work at Cerebras Systems
Damodar Sahasrabudhe has been employed at Cerebras Systems as a Member of Technical Staff since 2021. In this role, he focuses on performance optimizations for scientific computing, machine learning, and deep learning codes. His work involves developing efficient programming models for GPUs and multithreaded environments, particularly for next-generation supercomputers. This position is based in Sunnyvale, California, where he contributes to advancing high-performance computing applications.
Education and Expertise
Damodar Sahasrabudhe holds a Doctor of Philosophy (PhD) in Computer Science from the University of Utah, which he completed from 2015 to 2021. Prior to this, he earned a Bachelor of Engineering (B.E.) in Computer Science from the Maharashtra Institute of Technology between 2002 and 2006. His academic background supports his specialization in performance optimizations and system integrations for scientific computing and machine learning.
Background
Before joining Cerebras Systems, Damodar Sahasrabudhe gained experience in various roles. He worked as a Graduate Research Assistant at the University of Utah from 2016 to 2021, where he focused on research in computer science. He also served as a Technical Architect at Deloitte from 2011 to 2015 in Hyderabad, India, leading teams to deliver software development projects. His earlier experience includes a position as a Senior Software Engineer at Wipro Technologies from 2006 to 2011 and a research internship at Sandia National Laboratories in 2018.
Achievements in High-Performance Computing
Damodar Sahasrabudhe has made significant contributions to high-performance computing applications. He has experience scaling the Uintah framework on thousands of CPUs and GPUs, which enhances the performance of scientific computing tasks. His work also includes developing multilingual applications, emphasizing performance tuning and system integrations, which are critical for efficient computational processes.