Allard Hendriksen
Senior Developer Technology Engineer at NVIDIA
Munich, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolAllard Hendriksen is a Senior Developer Technology Engineer based in Munich with a decade of experience applying advanced numerical methods and machine learning to real-world systems. Holding an MSc and PhD in Applied Mathematics from Leiden, he transitioned academic expertise in computational imaging into production-grade GPU-accelerated solutions at NVIDIA. He contributes to high-performance open-source projects like RAPIDS (RAFT), where his CUDA kernel optimizations and algorithmic improvements yield measurable speedups across GPU architectures. Comfortable bridging theory and engineering, he has a track record of squeezing performance from low-level code while keeping algorithms robust for deployment. Colleagues describe him as a detail-oriented problem solver who brings academic rigor to practical system constraints.
10 years of coding experience
4 years of employment as a software developer
MSc Applied Mathematics, MSc Applied Mathematics at Leiden UniversityCalifornia Institute of TechnologyGymnasium - Natuur en Techniek & Natuur en Gezondheid, Gymnasium - Natuur en Techniek & Natuur en Gezondheid at Christelijk Gymnasium Sorghvliet
Github Skills (17)
c-language10
atomics10
machine-learning10
gpu10
performance-optimization10
atomic10
cuda10
cprogramming-language10
data-structure9
algorithm9
algorithms9
linear-algebra9
data-structures9
sparse8
nearest-neighbor-search8
Programming languages (9)
Github contributions (5)
NVIDIA/raft
Jul 2022 - Jan 2023
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Role in this project:
ML Engineer
ML EngineerContributions:135 reviews, 6 commits, 33 PRs in 6 months
Contributions summary:Allard primarily focuses on optimizing and improving CUDA-accelerated algorithms within the RAPIDS AI ecosystem. Their contributions include implementing warp-aggregated atomic increments, which enhance performance in filtering operations. Additionally, they've replaced existing functions with faster equivalents and added launch bounds to kernel functions, specifically targeting the `adj_to_csr_kernel` function, indicating a focus on performance and compatibility in GPU computing contexts. They also addressed the performance of fusedL2NN when data is skinny, resulting in a performance improvement across GPU architectures.
cudainformation-retrievalmachine-learningraftanns
ahendriksen/cone_balls
Nov 2018 - Aug 2019
Contributions:1 release, 30 commits, 15 pushes in 8 months
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