AMD EPYC™ processor for Data Analytics Support Grows in Hadoop Ecosystem

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The revolutionary AMD EPYC™ system-on-chip has been creating significant momentum in the industry this year. It is truly exciting to see that it has been adopted by major server vendors and cloud service providers. The EPYC™ 7601 processor offers the industry's highest x86 core count[i], largest memory capacity[ii], most memory bandwidth[iii] and greatest I/O density[iv] with AMD EPYC has been helping customers' performance reach impressive heights. Offering a choice in x86 architecture, and the flexibility, performance and security for the evolving needs of modern data center applications, EPYC™ processors deliver more performance per dollar versus the competition[v]. Software partnerships are critical to bringing that potential to anyone who wants to leverage its unique blend of performance and features. In the past few years, big data analytics has become mainstream across all industries - both on-premises and cloud hosted - and is being used to uncover hidden patterns and correlations, market trends and customer preferences that can help organizations make more-informed business decisions. Hadoop and real-time analytics are a major part of the revolution, and today we are announcing the expansion of our software ecosystem collaboration with MapR, a major software player in this space.

We are also announcing certified reference architectures – single socket and dual socket - for Hadoop providing the performance and scalability requirements needed to maximize the investment in big data analytics. The "no compromise" single-socket design ensures you are only paying for the processing power the application needs. Single-socket servers support all of the I/O and memory bandwidth available to a dual-socket server without the extra cost. The versatile dual-socket design offers the highest AMD EPYC™ core density and memory capacity, enabling our highest performance. A reference architecture is now available with MapR.

"The entry of EPYC™ processors into the market is helping drive new ways of thinking about simultaneous analytics as data happens, allowing companies to create new, intelligent and impactful applications," said Geneva Lake, VP of Worldwide Alliances and Channels of MapR. "As the industry's leading data platform for AI and Analytics, we are excited to collaborate with AMD to offer high performance EPYC™ processor solutions to our customers."

The advent of big data revolutionizes analytics and data science by allowing enterprises to store, access and analyze massive amounts of data of almost any type from any source. The AMD EPYC processor family has arrived in market at the perfect time as an underlying hardware solution that provides the perfect mix of flexibility and scalability of resources required. I look forward to continuing to work with the Hadoop ecosystem to bring EPYC™ processors to their customers and to end-users.

Raghu Nambiar is corporate vice president of design engineering for AMD. His/her postings are his/her own opinions and may not represent AMD's positions, strategies or opinions. Links to third party sites are provided for convenience and unless explicitly stated, AMD is not responsible for the contents of linked sites and no endorsement is implied. GD-5

AMD EPYC and MapR Data Platform


[i]

AMD EPYC 7601 processor includes up to 32 CPU cores versus the Xeon Platinum 8180 processor with 28 CPU cores. NAP-43

[ii]

A single AMD EPYC™ 7601 processor offers up to 2TB/processor (x 2 = 4TB), versus a single Xeon Platinum 8180 processor at 768Gb/processor (x 2 = 1.54TB). NAP-44

[iii]

AMD EPYC™ 7601 processor supports up to 8 channels of DDR4-2667, versus the Xeon Platinum 8180 processor at 6 channels of DDR4-2667. NAP-42

[iv]

1 x EPYC 7601 CPU in HPE Cloudline CL3150, Ubuntu 17.04 4.10 kernel (Scheduler changed to NOOP, CPU governor set to performance), 256 GB (8 x 32GB 2Rx4 PC4-2666) memory, 24 x Samsung pm1725a NVMe drives: FIO v2.16 (4 jobs per drive, IO depth 16, 128k block size) Average BW 53.30 GB/s on 100% Bandwidth Test (Average IOPs 426,000). Each run was done for 30 seconds with a 10 second ramp up using 16 NVMe drives. NAP-25

[v]

Estimates based on SPECrate®2017_int_base using the GCC-02 v7.2 compiler. AMD-based system scored 196 in tests conducted in AMD labs using an "Ethanol" reference platform configured with 2 x AMD EPYC 7601 SOCs ($4200 each at AMD 1ku pricing), 512GB memory (16 x 32GB 2R DDR4 2666MHz), Ubuntu 17.04, BIOS 1002E. Intel-based Supermicro SYS-1029U-TRTP server scored 169.8 in tests conducted in AMD labs configured with 2 x Xeon 8160 CPUs ($4702 each per ark.intel.com), 768GB memory (24 x 32GB 2R DDR4 2666MHz), SLES 12 SP3 4.4.92-6.18-default kernel, BIOS set to Extreme performance setting. NAP-57

DISCLAIMER

The information contained herein is for informational purposes only and is subject to change without notice. While every precaution has been taken in the preparation of this document, it may contain technical inaccuracies, omissions and typographical errors, and AMD is under no obligation to update or otherwise correct this information. Advanced Micro Devices, Inc. makes no representations or warranties with respect to the accuracy or completeness of the contents of this document, and assumes no liability of any kind, including the implied warranties of noninfringement, merchantability or fitness for particular purposes, with respect to the operation or use of AMD hardware, software or other products described herein. No license, including implied or arising by estoppel, to any intellectual property rights is granted by this document. Terms and limitations applicable to the purchase or use of AMD's products are as set forth in a signed agreement between the parties or in AMD's Standard Terms and Conditions of Sale. GD-18

©2018 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, EPYC and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used in this publication are for identification purposes only and may be trademarks of their respective companies.


This blog post was published August 09, 2018.
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