Anil Gaihre

Anil Gaihre

HPC Engineer at NVIDIA

About me

My name is Anil Gaihre, and I work as a High-Performance Computing (HPC) Engineer at NVIDIA. I hold a Ph.D. in Computer Engineering from Stevens Institute of Technology. My expertise includes parallel algorithms and accelerated computing on multi-core architectures, with research spanning graph analytics, scalable linear solvers, and genomics.

Professional Experience

Currently, I leverage my skills in parallel computing and GPU optimization at NVIDIA to tackle complex challenges associated with accelerating genomics tool Parabricks. My journey in high-performance computing began right after my undergraduate studies when I worked at E&T Nepal Pvt. Ltd., developing CFD simulation tools for Honda R&D Asia-Pacific using NVIDIA GPUs. This industrial experience laid the foundation for my academic pursuits, leading to research positions at the University of Massachusetts Lowell and Stevens Institute of Technology. The pinnacle of my academic journey was my Ph.D. at Stevens, focusing on “Accelerating Data-Analytical Algorithms on GPUs.” During this time, I also collaborated with Lawrence Berkeley National Laboratory, where I worked on pioneering GPU-friendly parallel algorithms for sparse linear solvers. This combination of industry and academic experiences has shaped my expertise in GPU-accelerated computing, spanning from CFD simulations to advanced data analytics and sparse linear algebra.

Research Focus

My doctoral research, titled “Accelerating Data-Analytical Algorithms on GPUs,” has contributed significantly to the field of high-performance computing. Key areas of my work include:

  • GPU-accelerated algorithms for graph traversal for sparse matrix operations
  • Scalable solutions for top-k computations and symbolic factorization
  • Applications in cryptocurrency analysis and blockchain technology

Publications and Achievements

My research has resulted in several high-impact publications in prestigious conferences and journals in the field of high-performance computing and big data analytics. Notable works include:

  • XBFS: Exploring runtime optimizations for breadth-first search on GPUs
  • gSoFa: Scalable Sparse Symbolic LU Factorization on GPUs
  • Dr. Top-k: A delegate-centric approach for Top-k computations on GPUs

For a complete list of my publications, please visit my Google Scholar profile.

Skills and Expertise

  • High-Performance Computing
  • Parallel Algorithm Design
  • GPU Programming (CUDA)
  • Graph Analytics/Computing
  • Sparse Linear Algebra
  • Genomics
  • Big Data Processing
  • Blockchain Technology

I am passionate about pushing the boundaries of what’s possible in parallel algorithms and GPU acceleration. I’m always open to collaborations and discussions in these exciting fields.