IBM and Xilinx, Inc., a provider of data center efficiency tools, announced a multi-year strategic collaboration to enable higher performance and energy-efficient data center applications through Xilinx FPGA-enabled workload acceleration on IBM POWER-based systems. IBM and Xilinx, through a private signed agreement and collaboration enabled by the OpenPOWER Foundation, are teaming to develop open acceleration infrastructures, software and middleware to address emerging applications such as machine learning, network functions virtualization, genomics, high performance computing and big data analytics.
Xilinx All Programmable FPGAs are designed to deliver the power efficiency that makes accelerators practical to deploy throughout the data center. As part of the IBM and Xilinx strategic collaboration, IBM Systems Group developers will create solution stacks for POWER-based servers, storage and middleware systems with Xilinx FPGA accelerators for data center architectures such as OpenStack, Docker, and Spark. IBM will also develop and qualify Xilinx accelerator boards into IBM Power Systems servers. Xilinx is developing and will release POWER-based versions of its software-defined SDAccel Development Environment and libraries for the OpenPOWER developer community.
“The combination of IBM and Xilinx provides our clients not only with a new level of accelerated computing made possible by the tight integration between IBM POWER processors and Xilinx FPGAs, but also gives them the ability to benefit directly from the constant stream of innovation being delivered by the rapidly expanding OpenPOWER ecosystem,” said Ken King, general manager of OpenPOWER for IBM.
IBM and Xilinx will also continue to further utilize IBM’s Coherent Accelerator Processor Interface (CAPI), a feature intended to provide the ability to build tightly integrated, coherent solutions right on top of the POWER architecture. For example, independent software vendors are already leveraging IBM Flash Storage attached to CAPI to create very large memory spaces for in-memory processing of analytics, enabling the same query workloads to run with 1/24 the number of servers compared to commodity x86 solutions.
To learn more about IBM, visit www.ibm.com.
To learn more about Xilinx, visit www.xilinx.com.