Research
My research focuses on High Performance Computing (HPC), parallel programming models, and compiler validation. I work on advancing the reliability and performance of GPU computing ecosystems through comprehensive testing frameworks and innovative methodologies. My research is driven by the belief that robust, reliable software is fundamental to scientific discovery. By developing comprehensive testing frameworks and validation methodologies, I aim to ensure that the tools scientists rely on for breakthrough research are trustworthy and performant. The intersection of AI and systems software presents unprecedented opportunities to automate and improve software quality at scale.
Research Areas
GPU Computing & Compiler Validation
Developing comprehensive validation and verification frameworks for OpenACC and OpenMP GPU offloading. My work ensures reliable performance across cutting-edge architectures including NVIDIA H100, GH200, and AMD MI300A systems.
Building CI/CD pipeline for LLVM-based compilers
Advancing HPC software sustainability through automated CI/CD pipelines for LLVM-based compilers. Focus on performance optimization, cross-platform compatibility, and systematic benchmarking across diverse computing architectures.
Active Projects
OpenACC Validation and Verification (V&V) Testsuite
Leading the development of a comprehensive validation and verification testsuite for the OpenACC Programming Model. This project ensures compiler compliance and reliability across diverse GPU architectures, supporting the broader HPC community.
Stewardship for Programming Systems and Tools (S4PST)
Contributing to a predictive ecosystem for HPC software sustainability. Our CI/CD pipeline for LLVM Clang and new-Flang's OpenMP Offloading implementation runs comprehensive test suites and benchmarks on cutting-edge GPU hardware including NVIDIA H100, GH200, and AMD MI210/MI300A.
OpenMP Offloading CI/CD Framework
Comprehensive continuous integration and deployment framework for validating OpenMP GPU offloading implementations. Focuses on automated testing, performance benchmarking, and cross-platform compatibility across diverse GPU architectures.
Multi-Architecture GPU Validation Framework
Developing unified testing methodologies for GPU computing across NVIDIA (H100, GH200) and AMD (MI300A) architectures. Ensures consistent performance and reliability for HPC applications across diverse hardware platforms.