IBM and NVIDIA Collaborate on New GPU-accelerated Libraries

IBM announced that it plans to incorporate the new RAPIDS open source software into its enterprise-grade data science platform for on-premises, hybrid, and multicloud environments. With the vast portfolio of deep learning and machine learning solutions, IBM believes it is best positioned to bring this open-source technology to data scientists regardless of their preferred deployment model.

"IBM has a long collaboration with NVIDIA that has shown demonstrable performance increases leveraging IBM technology, like the IBM POWER9 processor, in combination with NVIDIA GPUs," said Bob Picciano, Senior Vice President of IBM Cognitive Systems. "We look to continue to aggressively push the performance boundaries of AI for our clients as we bring RAPIDS into the IBM portfolio.

RAPIDS will help bring GPU acceleration capabilities to IBM offerings that take advantage of open source machine learning software including Apache Arrow, Pandas and scikit-learn. Immediate, wide ecosystem support for RAPIDS comes from key open-source contributors including Anaconda, BlazingDB, Graphistry, NERSC, PyData, INRIA, and Ursa Labs.

"IBM and NVIDIA's close collaboration over the years has helped leading enterprises and organizations around the world tackle some of the world's largest problems," said Ian Buck, vice president and general manager of Accelerated Computing at NVIDIA. "Now, with IBM taking advantage of RAPIDS open-source libraries announced today by NVIDIA, GPU accelerated machine learning is coming to data scientists, helping them analyze big data for insights faster than ever possible before.