NVIDIA/CUDALibrarySamples
May 2021 – Jul 2026
9 Months active
Languages Used
C++CMakeMarkdownCCUDAPython
Technical Skills
C++C++ DevelopmentCUDAImage Processingimage processingCMake
Zoheb Khan
PROFILE
Zoheb Khan
Over nine months, contributed to NVIDIA/CUDALibrarySamples by developing and enhancing GPU-accelerated image processing workflows, focusing on robust support for formats like JPEG2000, TIFF, GeoTIFF, and PFM. Leveraged C++, CUDA, and CMake to implement features such as ROI-based decoding, multi-image TIFF handling, and floating-point pixel data paths, while optimizing memory management for large files. Improved build system reliability across CUDA versions and streamlined library detection for NVTIFF and NVJPEG2K. Maintained and updated documentation, clarified onboarding, and deprecated unsupported Python bindings to reduce maintenance overhead, ensuring the repository remains maintainable, portable, and ready for diverse research and production use-cases.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
19Total
Bugs
2
Commits
19
Features
12
Lines of code
7,945
Activity Months9
Your Network
1934 peopleSame Organization
@nvidia.com1884
Work History
July 2026
1 Commits • 1 Features
Jul 1, 2026July 2026: Focused on expanding format interoperability in the NVIDIA CUDALibrarySamples by delivering PFM file format support for the nvJPEG2000 sample decoder/encoder. This work enables FP16/FP32 floating-point pixel data paths via NLT type 3 and prepares the samples for broader research and benchmarking use-cases.
1 Commits • 1 Features
Jul 1, 2026July 2026: Focused on expanding format interoperability in the NVIDIA CUDALibrarySamples by delivering PFM file format support for the nvJPEG2000 sample decoder/encoder. This work enables FP16/FP32 floating-point pixel data paths via NLT type 3 and prepares the samples for broader research and benchmarking use-cases.
July 2026
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for NVIDIA/CUDALibrarySamples focused on strengthening the build system to reliably integrate NVJPEG2K and NVTIFF across CUDA major versions. Implemented version-aware packaging support and updated detection logic to accommodate NVTIFF 0.6.0 directory changes, reducing build-time friction and maintenance burden for downstream consumers. No major bug fixes were required this month; the improvements center on robustness, portability, and future-ready tooling.
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for NVIDIA/CUDALibrarySamples focused on strengthening the build system to reliably integrate NVJPEG2K and NVTIFF across CUDA major versions. Implemented version-aware packaging support and updated detection logic to accommodate NVTIFF 0.6.0 directory changes, reducing build-time friction and maintenance burden for downstream consumers. No major bug fixes were required this month; the improvements center on robustness, portability, and future-ready tooling.
October 2024
2 Commits • 2 Features
Oct 1, 2024In October 2024, NVIDIA/CUDALibrarySamples delivered a focused set of features to accelerate ROI-based image processing and simplify Python bindings, aligning with maintainability and performance goals. Key outcomes include a new ROI-based TIFF image decoding sample using nvTIFF, and the deprecation/removal of the nvTIFF Python interface to reduce maintenance burden and user confusion. These changes support faster ROI-enabled TIFF workflows, lower memory footprints for large images, and clearer, more actionable guidance for developers integrating CUDA-based image processing. Technologies demonstrated include nvTIFF, ROI decoding, CUDA sample development, and API deprecation/maintenance practices.
2 Commits • 2 Features
Oct 1, 2024In October 2024, NVIDIA/CUDALibrarySamples delivered a focused set of features to accelerate ROI-based image processing and simplify Python bindings, aligning with maintainability and performance goals. Key outcomes include a new ROI-based TIFF image decoding sample using nvTIFF, and the deprecation/removal of the nvTIFF Python interface to reduce maintenance burden and user confusion. These changes support faster ROI-enabled TIFF workflows, lower memory footprints for large images, and clearer, more actionable guidance for developers integrating CUDA-based image processing. Technologies demonstrated include nvTIFF, ROI decoding, CUDA sample development, and API deprecation/maintenance practices.
October 2024
November 2023
2 Commits • 1 Features
Nov 1, 2023Month: 2023-11 | NVIDIA/CUDALibrarySamples: Enhanced large-image support in nvJPEG2000 with improved memory management for large files, BMP output fixes, and new PPM export capability for images larger than 4GB. This enables scalable processing of high-resolution images, reduces edge-case failures, and expands downstream compatibility for imaging pipelines.
November 2023
2 Commits • 1 Features
Nov 1, 2023Month: 2023-11 | NVIDIA/CUDALibrarySamples: Enhanced large-image support in nvJPEG2000 with improved memory management for large files, BMP output fixes, and new PPM export capability for images larger than 4GB. This enables scalable processing of high-resolution images, reduces edge-case failures, and expands downstream compatibility for imaging pipelines.
August 2023
1 Commits • 1 Features
Aug 1, 2023August 2023 monthly summary for NVIDIA/CUDALibrarySamples: Delivered a NVTIFF Library Search Path Compatibility Enhancement to simplify the NVTIFF search path and improve compatibility with binary archives, reducing integration friction for downstream projects and CI pipelines. The change is implemented via a focused commit and aligns with cross-platform binary-archive workflows.
1 Commits • 1 Features
Aug 1, 2023August 2023 monthly summary for NVIDIA/CUDALibrarySamples: Delivered a NVTIFF Library Search Path Compatibility Enhancement to simplify the NVTIFF search path and improve compatibility with binary archives, reducing integration friction for downstream projects and CI pipelines. The change is implemented via a focused commit and aligns with cross-platform binary-archive workflows.
August 2023
July 2023
4 Commits • 2 Features
Jul 1, 2023For 2023-07, delivered two key features in NVIDIA/CUDALibrarySamples: (1) GeoTIFF decoding sample (GPU-accelerated) for nvTIFF, with CMake config, usage README, and main C++ decoding logic; improved options and output formats for geospatial workflows. (2) nvTIFF Decode-Encode multi-image TIFF support, enabling handling of multi-image TIFFs with varying dimensions and updated writing/decoding logic. Commits: cf7c04448bb71a9d3a94fd3e60b23ccf862f2b22; 7b335d85ff32cccf22d8749436aa47215aafab24; 68e43b2e721f29c6365c6fb572a18cf1b4ad02dd; 650f73e79b1af4760bbd7c1f7720c0e8f94a2409.
July 2023
4 Commits • 2 Features
Jul 1, 2023For 2023-07, delivered two key features in NVIDIA/CUDALibrarySamples: (1) GeoTIFF decoding sample (GPU-accelerated) for nvTIFF, with CMake config, usage README, and main C++ decoding logic; improved options and output formats for geospatial workflows. (2) nvTIFF Decode-Encode multi-image TIFF support, enabling handling of multi-image TIFFs with varying dimensions and updated writing/decoding logic. Commits: cf7c04448bb71a9d3a94fd3e60b23ccf862f2b22; 7b335d85ff32cccf22d8749436aa47215aafab24; 68e43b2e721f29c6365c6fb572a18cf1b4ad02dd; 650f73e79b1af4760bbd7c1f7720c0e8f94a2409.
March 2023
1 Commits • 1 Features
Mar 1, 2023Concise monthly summary for 2023-03 focusing on the NVIDIA/CUDALibrarySamples repository. Delivered enhancements to the nvjpeg2k sample to improve JPEG 2000 decoding and broaden support for additional image formats and precision handling, aligning with the v0.7 release. No major bugs reported or fixed for this repo this month. The work extends image processing capabilities for diverse pipelines and demonstrates strong CUDA/C++ sample design, maintainability, and release readiness.
1 Commits • 1 Features
Mar 1, 2023Concise monthly summary for 2023-03 focusing on the NVIDIA/CUDALibrarySamples repository. Delivered enhancements to the nvjpeg2k sample to improve JPEG 2000 decoding and broaden support for additional image formats and precision handling, aligning with the v0.7 release. No major bugs reported or fixed for this repo this month. The work extends image processing capabilities for diverse pipelines and demonstrates strong CUDA/C++ sample design, maintainability, and release readiness.
March 2023
October 2021
3 Commits • 1 Features
Oct 1, 2021October 2021: Delivered a JPEG2000 sample encoder using nvJPEG2000, improved build reliability for nvJPEG2K components, and clarified onboarding docs to reduce setup friction. These efforts demonstrably enhanced the library’s practical capabilities and reduced time-to-first-run for users.
October 2021
3 Commits • 1 Features
Oct 1, 2021October 2021: Delivered a JPEG2000 sample encoder using nvJPEG2000, improved build reliability for nvJPEG2K components, and clarified onboarding docs to reduce setup friction. These efforts demonstrably enhanced the library’s practical capabilities and reduced time-to-first-run for users.
May 2021
3 Commits • 2 Features
May 1, 2021May 2021 focused on expanding image decoding capabilities and stability for NVIDIA/CUDALibrarySamples. Delivered two major feature areas and associated improvements, broadening media format support, enhancing output fidelity, and strengthening downstream rendering reliability. Key outcomes include verbose image decoding with enhanced handling of 3- and 4-channel images and refined alpha-channel management, + 420/422 chroma subsampling support for nvjpeg2k and nvJPEG2000 with adjusted buffer allocations to ensure correct RGB output based on user flags. Bug fixes and code cleanup (CUNVJPG-393) contributed to overall reliability. Technologies demonstrated include CUDA-based image processing, nvJPEG pipelines, memory/buffer management, and logging instrumentation.
3 Commits • 2 Features
May 1, 2021May 2021 focused on expanding image decoding capabilities and stability for NVIDIA/CUDALibrarySamples. Delivered two major feature areas and associated improvements, broadening media format support, enhancing output fidelity, and strengthening downstream rendering reliability. Key outcomes include verbose image decoding with enhanced handling of 3- and 4-channel images and refined alpha-channel management, + 420/422 chroma subsampling support for nvjpeg2k and nvJPEG2000 with adjusted buffer allocations to ensure correct RGB output based on user flags. Bug fixes and code cleanup (CUNVJPG-393) contributed to overall reliability. Technologies demonstrated include CUDA-based image processing, nvJPEG pipelines, memory/buffer management, and logging instrumentation.
May 2021
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Quality Metrics
Correctness93.6%
Maintainability87.4%
Architecture90.6%
Performance87.4%
AI Usage22.2%
Skills & Technologies
Programming Languages
CC++CMakeCUDAMarkdownPython
Technical Skills
Build ConfigurationBuild SystemsC++C++ DevelopmentC++ developmentCMakeCMake configurationCUDACUDA developmentCUDA programmingDocumentationGPU programmingGeospatial AnalysisImage ProcessingImage processing
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