Inside Intel: The Race for Faster Machine Learning

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The goal is not just the fastest but the most productive machine-learning platform for researchers.

Pradeep Dubey, an Intel Fellow at the Intel Labs division

Product and Performance Information

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Benchmark results were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and "Meltdown". Implementation of these updates may make these results inapplicable to your device or system.

Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance tests, such as SYSmark* and MobileMark*, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit https://www.intel.in/benchmarks.

https://www.intel.in/content/www/in/en/benchmarks/server/xeon-phi/xeon-phi-competitive-performance.html
Configuration information: One 2-socket Intel® Xeon® processor E5-2697 v4 (45M cache, 2.3GHz, 18 cores), memory 128GB vs one NVIDIA* Tesla K80 GPUs, NVIDIA CUDA* 7.5.17 (Driver 352.39), ECC enabled, persistence mode enabled.