RAPIDS による Apache Spark 3.0 の GPU アクセラレーション Apache Spark 3.0 は、分析と AI のワークロードのために完全統合されたシームレスな GPU アクセラレーションを提供する最初の Spark リリースです。オンプレミスでも、クラウドでも、コードを変更することなく GPU を利用して Spark 3.0 のパワーを活用できます。GPU の画期的な性能は、企業や研究者がより大きなモデルをより頻繁にトレーニングできるようになり、AI のパワーでビッグデータから価値を引き出すことができるようになります。 詳細を見る
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
At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads. We have multiple ongoing projects to integrate Spark better with native accelerators, including Apache Arrow support and GPU scheduling with Project Hydrogen. We believe that RAPIDS is an exciting new opportunity to scale our customers' data science and AI workloads. - Matei Zaharia, co-founder and CTO of Databricks, and the original creator of Apache Spark
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
At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads. We have multiple ongoing projects to integrate Spark better with native accelerators, including Apache Arrow support and GPU scheduling with Project Hydrogen. We believe that RAPIDS is an exciting new opportunity to scale our customers' data science and AI workloads. - Matei Zaharia, co-founder and CTO of Databricks, and the original creator of Apache Spark
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
At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads. We have multiple ongoing projects to integrate Spark better with native accelerators, including Apache Arrow support and GPU scheduling with Project Hydrogen. We believe that RAPIDS is an exciting new opportunity to scale our customers' data science and AI workloads. - Matei Zaharia, co-founder and CTO of Databricks, and founder of Apache Spark
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