AMD introduces first server GPU

AMD's first server GPU the FirePro-S9170.
AMD’s first server GPU the FirePro S9170.

Chip maker AMD has announced the new FirePro S9170 server GPU, which the vendor claims is the world’s first and fastest 32GB single-GPU server card for DGEMM heavy double-precision workloads1, with support for OpenCL 2.0.

Based on the second-generation AMD graphics core next (GCN) GPU architecture, this new addition to the AMD FirePro server GPU family is capable of delivering up to 5.24 TFLOPS of peak single precision compute performance while enabling full throughput double precision performance, providing up to 2.62 TFLOPS of peak double precision performance.

Designed with compute-intensive workflows in mind, the AMD FirePro S9170 server GPU is ideal for data centre managers who oversee clusters within academic or government bodies, oil and gas industries, or deep neural network compute cluster development.

“AMD is recognised as an HPC industry innovator as the graphics provider with the top spot on the November 2014 Green500 List. Today the best GPU for compute just got better with the introduction of the AMD FirePro S9170 server GPU to complement AMD’s impressive array of server graphics offerings for high performance compute environments,” said Sean Burke, corporate vice president and general manager, AMD Professional Graphics Group. “The AMD FirePro S9170 server GPU can accelerate complex workloads in scientific computing, data analytics, or seismic processing, wielding an industry-leading 32GB of memory. We designed the new offering for supercomputers to achieve massive compute performance while maximising available power budgets.”

“There are some HPC workloads which require as much data as possible to stay resident on the device, and so the 32GB of memory provided by AMD FirePro S9170, the largest available on a single GPU, will enable the acceleration of scientific calculations that were previously impossible,” said Simon McIntosh-Smith, head of the Microelectronics Research Group at the University of Bristol. “For example, our new OpenCL version of the SNAP transport code from Los Alamos National Laboratory needs to keep as much data resident on the device as possible, and so the 32GB of memory will let us run problems of a much more interesting size faster than ever before.”

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