Niranjan Artal
System Software Engineer at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolNiranjan Artal is a systems software engineer with 12 years of experience building and optimizing low-level software for embedded and high-performance platforms, currently based in California and working at NVIDIA. He has deep kernel and platform expertise from porting Tegra-based Chromebooks to ChromeOS, maintaining display drivers, bootloaders (U-Boot, coreboot), and automating build and test flows across multiple Linux variants. Niranjan is also an active open-source contributor to GPU-accelerated data projects—adding memory and timing metrics to NVIDIA’s Spark-RAPIDS and implementing ORC reader bindings and tests for cuDF—demonstrating a blend of performance tuning and data-format interoperability work. His background includes production support and automation in large enterprise environments and a strong academic foundation with an MS in Computer Science (GPA 3.83). Colleagues rely on him for pragmatic solutions that span driver integration to developer tooling, and he often surfaces non-obvious performance regressions by instrumenting precise memory and timing metrics.
12 years of coding experience
3 years of employment as a software developer
MS, Computer Science, 3.8, MS, Computer Science, 3.8 at North Carolina State UniversityBE, Computer Science, BE, Computer Science at B.V.B College of Engineering and Technology
Github Skills (15)
dataframes10
gpu-programming10
apache-spark10
cudf10
c-language10
cprogramming-language10
gpu10
performance-optimization10
orc10
dataframe10
java9
data-engineering9
big-data9
javas9
cuda8
Programming languages (4)
Github contributions (5)
NVIDIA/spark-rapids
Oct 2019 - Jan 2023
Spark RAPIDS plugin - accelerate Apache Spark with GPUs
Role in this project:
Back-end Developer
Back-end DeveloperContributions:500 reviews, 216 commits, 229 PRs in 3 years 3 months
Contributions summary:Niranjan contributed to implementing and refining peak memory utilization metrics within the `nvidia/spark-rapids` repository, which focuses on accelerating Apache Spark with GPUs. They added code to compute aggregate time and memory usage, and also updated the GpuColumnVector class. Furthermore, the user incorporated metrics into GpuSortExec, GpuCoalesceBatches, GpuShuffledHashJoinExec, and GpuBatchScanExec to improve the performance of the join and coalesce operators. These additions and modifications indicate a focus on performance optimization within the GPU-accelerated Spark environment.
gpu-accelerationgpuapachebig-dataspark
rapidsai/cudf
Jul 2019 - Dec 2020
cuDF - GPU DataFrame Library
Role in this project:Back-end Developer
Back-end DeveloperContributions:80 reviews, 50 commits, 24 PRs in 1 year 5 months
Contributions summary:Niranjan's contributions primarily focused on implementing an ORC reader and related functionalities for the cuDF library. This involved adding Java bindings for ORC file reading, including options for NumPy type promotion and timestamp handling. They implemented and addressed comments while contributing to the test suites for the ORC reader to validate the correctness. This suggests a focus on data loading and file format support within the cuDF library.
cudadataframe-librarydata-analysiscppcudf
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