π Sandeep is passionate about high-performance accelerated computing on GPUs using CUDA, leveraging NVIDIA's parallel programming platform. He is also deeply involved in competitive programming π§© and regularly participates in contests while solving data structures and algorithms problems π». He enjoys working on exciting projects and is always open to collaboration π€. Feel free to reach out if you'd like to work with him! He has recently completed his undergraduate degree in Computer Science and Engineering from IIT Palakkad. π
Our research focuses on accelerating well-known algorithms by harnessing the power of GPU parallelism. We are optimizing reduction operations for large arrays (size β₯ 108) using CUDA programming, achieving an 18% speedup compared to NVIDIA's Thrust library. These gains come from innovative strategies we're excited to publish in upcoming papers. π
Current work includes optimizing SSSP implementations on GPUs (static and dynamic settings) and advancing the PageRank algorithm through high-performance computing techniques. π‘
Cloud-based collaboration platform with version control and user management features.
View on GitHubAn optimized Skip List data structure in C++, designed to outperform std::set for certain operations.
View on GitHubCompared the properties of random graph models like the ER (ErdΕsβRΓ©nyi) and BA (BarabΓ‘siβAlbert) models with real-world networks such as Facebook.
View on GitHubUsing FPGA and Verilog, designed a chip for a 24-hour clock and alarm system with a Zybo board, LEDs, and logic gates.
View on GitHubBuilt a spam filter using a probabilistic approach with a naive Bayes classifier, trained on data to accurately detect spam messages.
View on GitHub