2017 promises to be an exciting year for servers and the competitiveness of compute offerings. This year will see this scope of impact not only include enterprise datacenters and the public cloud, but extend to the emergence of “edge computing”. Edge computing is defined as compute required to deal with data at or near the point of creation. Among other things, these devices will include the “ocean” of remote, smart sensors, commonly included in internet of things (IoT) discussions.
Here is a list of a few things to we’ll see concerning specific CPUs.
It should come as no surprise that Intel continues to dominate (>99%) the server market but is under enormous pressure on all fronts. Xeon and its evolution continue to be their compute vanguard. Xeon-Phi (and now the addition of Nervana) make up their engines for high-performance computing / machine learning. Phi has seen some success, but it isn’t clear yet how Nervana offerings will materialize.
Advanced Micro Devices AMD has their best shot in years for fielding an Intel competitor that just about everyone (except perhaps Intel) is eager to see. If the AMD Zen server CPU is simply good enough (meaning, it shows up, works and has at least some performance value), it will take market share simply by being an x86 competitor. AMD is encouraged by early indicators. They also have their ATI GPGPU technology which will provide additional opportunities.
ARM Holdings will continue to dominate the mobile and embedded device space, but the fight is hard in these segments. The more likely opportunity for ARM expansion will be at the “edge” and not so much in the server space. The death of Vulcan by Avago, the acquisition of Applied Micro Circuits (APM) and their plan to find a place for X-Gene leaves the Cavium ThunderX, the “yet to be launched” Qualcomm Centriq CPU and a few other very focused ARM initiatives still standing. After years of “This is the Year for ARM Servers”, the outlook could be better, and if AMD produces a plausible Intel competitor (capable of running x86 software), it will put extreme pressure on whole ARM server CPU initiative.
OpenPOWER seems on the other hand to have a lot of momentum but to date has not significantly impacted the x86 server market. 2017 may end on a different note. OpenPOWER‘s (IBM ) willingness to embrace NVIDIA (the darling of the machine learning segment) and embed an NV-Link interface is going to play well with much of AI and HPC communities. By the end of the year, we will have seen some interesting OpenPOWER offerings emerge based on advanced silicon process technology from a variety of sources, and 2018 may see a whole different story. Especially if an embrace from Google, who has been flirting with OpenPOWER for a while now, materializes and creates a tipping point.
The real challenge to all CPUs is the way they do work. Their philosophy is built on the principle that data must come into the chip, be operated on by the chip, with results or even new data being pushed out of the chip. This whole process creates a natural bottleneck that we’ve flirted with for decades. As the magnitude and scope of data increases, something has got to give, and a favorite candidate is more parallelism. So far, this has favored GPGPUs or accelerators.
At the bigger-picture business level for datacenters and the public cloud, the real question is not so much which CPU (in fact, the business folks probably couldn’t care less), but the economics of private, public or hybrid solutions. It is safe to say enterprise computing will not disappear any time soon, and while there is much activity, the implementations and economics of hybrid solutions have proven to be difficult. According to Gartner, by 2020 more compute power will have been sold by IaaS and PaaS cloud providers than sold and deployed into enterprise datacenters. The fact that companies (especially smaller ones) are either being born in or moving to the cloud at a rapid pace is undeniable. However, NOT all are seeing the expected saving materialize from this move. 2017 will certainly see some careful thinking and maybe even some rethinking of strategy.
The explosion of data at the edge is simply going to change data processing as we know it and will create a variety of computing problems that are difficult to do in the cloud (even though the results may end up there). However, they may not be in the enterprise datacenters as we know them either, and we may find them “stuck” all over the place. For more than sixty years, we have seen compute follow the data. First from the original mainframe datacenter to the desktop, to departmental servers, into enterprise datacenters, and now significantly into the cloud. It is my opinion, that If you plan to put just your data into the cloud, economics (the cost of network usage) will drive your compute there sooner or later. You want to consider this carefully based on your actual needs and usage. There might be a better overall business outcome, depending on your size and ability to operate, in your own datacenter.
The major emerging source of data is at the edge and will drive the need for much compute there. By the way, all the CPUs mentions here should be able do the edge reasonably well so … Game on again!