Machine Learning: Develop Predictive Models
As machine learning becomes a must-have for business models and forecasts, it’s important to consider frameworks that can be integrated into your big data analytics environments and workflows. The Cray Urika-GX platform is a pre-integrated hardware-software platform that includes Apache Spark™, which provides a machine-learning library, and MLlib, designed for simplicity, scalability and easy integration with other tools.
The Cray Urika-GX platform is designed to accelerate Spark machine learning tasks in a very accessible format with large in-memory compute ability, up to 22 TB DRAM, and dense cores in a standard enterprise rack.
Also included in the Urika-GX system are Hadoop® and the Cray Graph Engine so you can run all your big data analytics workflows on one system and avoid the common overhead of data movement. We also leverage open standards like OpenStack, Mesos and Docker so you can be ready for data in days and then customize for future needs.
Deep Learning: Expand Your Insight
The Cray CS-Storm accelerated cluster supercomputer is purpose-built for deep learning problems that require precision and performance in an accessible format.
Cray CS-Storm 500GT System
The Cray CS-Storm 500GT configuration scales up to ten NVIDIA® Tesla® Pascal P40 or P100 GPUs or Nallatech FPGAs by leveraging the flexibility and economics of PCI Express.
Cray CS-Storm 500NX System
The Cray CS-Storm 500NX configuration scales up to eight NVIDIA Tesla Pascal P100 SXM2 GPUs using NVIDIA® NVLink™ to reduce latency and increase bandwidth between GPU-to-GPU and CPU-to-GPU communications, enabling larger models and faster results for AI and deep learning neural network training.
For deep neural network training, the XC50 system is the world’s most scalable deep learning supercomputer. The XC50 supercomputer features the Tesla P100 GPU accelerator for PCIe, which enables lightning-fast nodes to deliver the highest absolute performance for high performance computing and deep learning workloads. And optimizations such as partitioned global address space (PGAS) and Cray’s Aries™ supercomputing interconnect, with its all-to-all communications, support more parallel analytics at scale with higher throughput.
To simplify the building and deploying of deep learning environments in supercomputing, Cray provides XC customers with directive files for common deep learning tools, such as TensorFlow and Microsoft Cognitive Toolkit (previously CNTK).