Srinivas Singanaboina
Graduate Research Assistant
Baton Rouge, Louisiana, United States
Summary
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RockstarSrinivas Singanaboina is a C++ and high-performance computing engineer with seven years of experience focused on parallelism, performance tuning, and backend systems. As a Graduate Research Assistant at LSU's Center for Computation & Technology and a former NERSC intern, he applies research-grade techniques to optimize real-world libraries and benchmarks. Srinivas has contributed substantial refactors and efficiency improvements to HPX—the C++ Standard Library for Parallelism and Concurrency—and to RAPIDS cuDF, adding GPU-aware API enhancements and migrating benchmarks to nvbench. He combines deep algorithmic changes (e.g., replacing tag_invoke usage and leveraging cuco::static_set) with pragmatic benchmarking and performance engineering. Equally comfortable mentoring in open-source programs like Google Summer of Code, he bridges academic research and production-oriented HPC software.
7 years of coding experience
1 year of employment as a software developer
Github Skills (17)
algorithm10
algorithms10
benchmark10
c-language10
parallel10
cudf10
benchmarking10
data-structure10
c1710
gpu10
performance-optimization10
cuda10
data-structures10
cpp10
cprogramming-language10
Programming languages (7)
Github contributions (5)
TheHPXProject/hpx
Feb 2021 - Jan 2023
The C++ Standard Library for Parallelism and Concurrency
Role in this project:
Back-end Developer
Back-end DeveloperContributions:33 reviews, 210 commits, 42 PRs in 1 year 10 months
Contributions summary:Srinivas's contributions focus on refactoring and improving the efficiency of the algorithms implemented in the HPX library, which is a C++ Standard Library for Parallelism and Concurrency. Their work primarily involved replacing calls to the `tag_invoke` functions to refactor and modernize the existing C++ code base. This included the modification of various algorithms in copying the function, changing the namespace, as well as the creation and improvement of the existing functions.
concurrencycppc-plus-plusdistributedcross-platform
NVIDIA/cudf
Apr 2024 - Jul 2025
cuDF - GPU DataFrame Library
Role in this project:Back-end Developer / Performance Engineer
Back-end Developer / Performance EngineerContributions:29 reviews, 12 PRs, 1 branch in 1 year 3 months
Contributions summary:Srinivas's contributions center on enhancing the performance and functionality of the cuDF library, specifically within the realm of reduction operations. They added a stream parameter to several public rolling APIs. The user refactored distinct algorithms to leverage cuco::static_set for optimized performance. Furthermore, the user migrated benchmarks to nvbench, reducing test case complexity and enhancing the benchmarking process for conditional join operations.
cudfdataframegpurapidsarrow
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