Apache Spark 3.0 是采用 RAPIDS 的 GPU 加速技术 Version 3.0 是可为分析和 AI 工作负载提供完全集成和无缝的 GPU 加速的先进 Spark 版本。借助 GPU 在本地或云端利用 Spark 3.0 的强大功能，而无需更改您的代码。凭借 GPU 的突破性性能，企业和研究人员能够更频繁地训练更大的模型，最终利用 AI 的强大功能充分挖掘大数据的价值。 了解详情
RAPIDS 作为 GPU 加速数据科学平台，是由 Apache Arrow 提供动力支持的新一代计算生态系统。NVIDIA 与 Ursa Labs 携手合作，将加快 Arrow 核心库的创新步伐，并有助于在分析和特征工程工作负载方面带来重大的性能提升。 – Wes McKinney，Ursa Labs 总经理，同时也是 Apache Arrow 和 Pandas 创建者
我使用 RAPIDS XGBOOST 获得了 24 倍加速，现在借助 8 块 GPU，我可以在单个节点上运行规模超大的机器学习 (ML) 工作负载，取代原先的数百个 CPU 节点。你们使 XGBOOST 实现了神速提升，真是不可思议！ – 流媒体公司
我过去遇到的瓶颈是输入/输出 (I/O)。……输入 10 家店铺的数据（约 1 百万行）需耗时 10 分钟。借助 RAPIDS，不到 3 分钟时间，我们便能输入约 6000 家店铺的数据（数百万行）。相比之下，旧有基础设施在处理该等规模的数据时会轻易占用我们 4 天时间……简直太棒了。 – 一位拥有 6000 家门店的中端市场专业零售商
RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA collaboration with Ursa Labs will accelerate the pace of innovation in the core Arrow libraries and help bring about major performance boosts in analytics and feature engineering workloads. - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas
I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!? - Streaming Media Company
My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome. - A mid-market specialty retailer with 6000 stores
RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA collaboration with Ursa Labs will accelerate the pace of innovation in the core Arrow libraries and help bring about major performance boosts in analytics and feature engineering workloads. - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas
I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!? - Streaming Media Company
My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome. - A mid-market specialty retailer with 6000 stores