RAPIDS로 GPU 가속화된 Apache Spark 3.0 Apache Spark 3.0은 분석 및 AI 워크로드에 완전히 통합되고 원활한 GPU 가속화를 제공하는 Spark의 첫 번째 릴리스입니다. 코드를 변경하지 않고 온 프레미스 또는 클라우드에서 GPU와 함께하는 Spark 3.0의 성능을 활용해보십시오. GPU의 획기적인 성능은 엔터프라이즈와 연구진에게 더 큰 모델을 더욱 자주 트레이닝하는 역량을 제공하여 궁극적으로 AI 성능을 활용한 빅 데이터의 가치를 확대합니다. 자세히 알아보기
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