{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Database","url":"https://skillfed.io/packages/category/database/3"}],"enrichment":{"capability":"libcudf-cu12 is a GPU-accelerated C++ library providing Apache Arrow-compliant data structures and fundamental algorithms for tabular data processing on NVIDIA CUDA 12 GPUs.","skillfed_tags":["gpu-accelerated","rapids","arrow-columnar"],"use_cases":["Accelerate large-scale data aggregations and groupby operations on GPU for analytics workflows","Process multi-gigabyte parquet or CSV files faster than CPU alternatives by keeping data on GPU memory","Build GPU-accelerated ETL pipelines using libraries built on top of libcudf-cu12's core algorithms","Integrate GPU tabular processing into Spark jobs via plugins built on libcudf-cu12","Support GPU-native SQL engines and data processing frameworks that depend on libcudf-cu12"],"what_it_does":"libcudf-cu12 is the low-level C++ foundation of the RAPIDS GPU data processing suite, providing Apache Arrow-compliant columnar data structures and core algorithms optimized for NVIDIA CUDA 12 GPUs. It handles the heavy lifting for tabular operations\u2014reading, filtering, aggregating, and transforming data\u2014directly on GPU memory, bypassing CPU bottlenecks. The library depends on libkvikio-cu12, librmm-cu12, nvidia-libnvcomp-cu12, nvidia-nvjitlink-cu12, and rapids-logger to function.\n\nThe package targets data scientists and engineers working with large tabular datasets who need GPU acceleration. Installation requires a compatible NVIDIA GPU, CUDA 12 runtime, and Linux with specific glibc versions; the wheel distribution is architecture-specific (aarch64 and x86_64). Requires Python 3.11 or later.","worth_installing":"Yes, if you have an NVIDIA GPU with CUDA 12 and need GPU-accelerated tabular data processing. The library is actively maintained, permissively licensed under Apache-2.0, and has no known vulnerabilities. Install friction is moderate (GPU-specific wheels, glibc version constraints, Python 3.11+), but manageable for teams with GPU infrastructure. Most users will install it indirectly as a dependency rather than directly."},"id":"libcudf-cu12","links":{"html":"https://skillfed.io/packages/libcudf-cu12","md":"https://skillfed.io/packages/libcudf-cu12.md","pypi":"https://pypi.org/project/libcudf-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"libcudf-cu12","python_support":"supports_current","summary":"cuDF - GPU Dataframe (C++)"},"popularity":{"monthly_downloads":469527,"position":6488,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}