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Polars releases GPU engine powered by RAPIDS cuDF, boosting data processing speeds up to 13x on NVIDIA GPUs. Now available in open beta. (Read More)
NVIDIA's RAPIDS AI enhances predictive maintenance in manufacturing, reducing downtime and operational costs through advanced data analytics. (Read More)
Learn how to build efficient recommender systems using co-visitation matrices and RAPIDS cuDF for faster data processing and improved personalization. (Read More)
NVIDIA releases RAPIDS cuDF unified memory, boosting pandas performance up to 30x on large and text-heavy datasets. (Read More)
NVIDIA explores the RAPIDS cuVS IVF-PQ algorithm, enhancing vector search performance through compression and GPU acceleration. (Read More)
Learn how to optimize the IVF-PQ algorithm for vector search performance using RAPIDS cuVS, with practical tips on tuning hyper-parameters and improving recall. (Read More)
Discover how to optimize encoding and compression for Parquet string data using RAPIDS, leading to significant performance improvements. (Read More)
NVIDIA's RAPIDS cuDF utilizes Unified Virtual Memory to boost pandas' performance by 50x, offering seamless integration with existing workflows and GPU acceleration. (Read More)
Explore how NVIDIA's RAPIDS cuDF optimizes deduplication in pandas, offering GPU acceleration for enhanced performance and efficiency in data processing. (Read More)
Explore best practices for leveraging RAPIDS and Dask in multi-GPU data analysis, addressing memory management, computing efficiency, and accelerated networking. (Read More)
Discover how NVIDIA RAPIDS and cuML enhance causal inference by leveraging GPU acceleration for large datasets, offering significant speed improvements over traditional CPU-based methods. (Read More)
NVIDIA RAPIDS 24.10 introduces GPU-accelerated NetworkX and Polars with zero code changes, enhancing compatibility with Python 3.12 and NumPy 2.x for improved data processing. (Read More)
RAPIDS cuML introduces a faster, scalable UMAP implementation using GPU acceleration, addressing challenges in large dataset processing with new algorithms for improved performance. (Read More)
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