When working with large datasets, the performance of your data processing tools becomes critical. Polars, an open-source library for data manipulation known for its speed and efficiency, offers a GPU-accelerated backend powered by cuDF that can significantly boost performance. However, to fully leverage the power of the Polars GPU backend, it��s essential to optimize the data loading process��
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The NVIDIA Grace CPU Superchip delivers outstanding performance and best-in-class energy efficiency for CPU workloads in the data center and in the cloud. The benefits of NVIDIA Grace include high-performance Arm Neoverse V2 cores, fast NVIDIA-designed Scalable Coherency Fabric, and low-power high-bandwidth LPDDR5X memory. These features make the Grace CPU ideal for data processing with��
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In the webinar on January 28th, you��ll get an inside look of the new GPU engine to learn how Polars�� declarative API and query optimizer enable seamless GPU acceleration.
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The RAPIDS v24.10 release takes another step forward in bringing accelerated computing to data scientists and developers with a seamless user experience. This blog post highlights the new features including: NetworkX accelerated by RAPIDS cuGraph is now GA in the 24.10 release beginning with NetworkX 3.4. This release adds GPU-accelerated graph creation, a new user experience��
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Polars, one of the fastest-growing data analytics tools, has just crossed 9M monthly downloads. As a modern DataFrame library, it is designed for efficiently processing datasets that fit on a single machine, without the overhead and complexity of distributed computing systems that are required for massive-scale workloads. As enterprises grapple with complex data problems��ranging from��
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