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IBM Spectrum Software helps ease adoption and production of parallel processing and clustered computing
IBM is announcing new software to deliver faster time-to insight for high performance data analytics (HPDA) workloads, such as Spark, Tensor Flow and Caffé, for AI, machine learning and deep learning. Based on the same software which will be deployed for the Department of Energy’s CORAL Supercomputer Project at both Oak Ridge and Lawrence Livermore, IBM’s new software will provide these capabilities for enterprises running HPDA workloads.
New to this launch is Deep Learning Impact (DLI), a set of software tools to help users develop AI models with the leading open source deep learning frameworks, like TensorFlow and Caffe. The DLI tools complement the PowerAI deep learning enterprise software distribution. The new release of IBM Spectrum LSF Suites combines powerful workload management and reporting with a new intuitive user interface providing simple and flexible access. Finally, the latest version of IBM Spectrum Scale software provides support to move workloads such as unified file, object and HDFS from where it is stored to where it is analyzed.
Through these new software offerings IBM plans to deliver users a set of comprehensive, open offerings developed to help speed adoption and production of parallel processing and clustered computing through offerings designed to:
- Simplify deployment – cluster virtualization to make many systems work together as one with easy-to-deploy packages that support multiple applications, users and departments on shared compute and data services designed to rapidly manage clusters for HPC and deep learning/machine learning.
- Deliver AI for the enterprise – from centralized management and reporting, multi-tenant access and end-to-end security, to full IBM support and services, our offerings have been designed to offer the level of function and support expected in an enterprise data center.
- Ease adoption of cognitive workloads – end users can access and use cluster resources across more applications, lowering the need for specialized cluster knowledge.
- Provide elasticity to hybrid cloud – simplifying cloud usage in distributed clustered environments with automated workload-driven cloud provisioning and de-provisioning, so you only pay for what you use, and intelligent workload and data transfer to and from the cloud, making cloud usage transparent to the end user.
- Open to the latest technology – IBM Storage software supports the latest open source tools and protocols, allowing the future adoption of containers, Spark, or other modules like deep learning.
Clients that benefit from accelerated computing can realize significant performance benefits by deploying systems uniquely optimized for accelerated workloads. The IBM Spectrum Computing family is designed to support hybrid architectures to effectively match specific workloads to specific platforms for optimal performance. As such, it offers broad support for mixed environments including both x86 and IBM POWER platforms, and it is also ready for the next generation of IBM Power Systems, which is expected to deliver even greater performance benefits.
The applications of this technology are far-reaching. From media, entertainment and security surveillance to manufacturing and healthcare industries, these solutions are capable of combing through petabytes of data to fuel innovation and uncover insights. Through parallel processing compute resources, users can quickly perform tasks such as identifying a car with a specific license plate, cross-reference a supply chain and weather forecasts or search for an anomaly across medical records.
The latest version of IBM Spectrum LSF Suites becomes generally available in late November. New versions of IBM Spectrum Conductor with Spark, Deep Learning Impact and IBM Spectrum Scale will be available in early December.
For more about IBM Spectrum Computing please visit: https://www.ibm.com/spectrum-computing.
For more about IBM Spectrum Storage please visit: https://www.ibm.com/storage/spectrum.