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The evolution of cloud computing

Changes in server design will have a real-world impact on everyone who uses both traditional and new computing devices.

Martin Fink, CTO and director of HP Labs shows a Machine system on a board at HP's Discover conference.
HPE Discover / YouTube

A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry.


Servers are probably not near the top of your list for conversation topics, nor are they something most people ever think about. But there are some interesting changes happening in server design that will start to have a real-world impact on everyone who uses both traditional and new computing devices.

Everything from smart digital assistants to autonomous cars to virtual reality is being enabled and enhanced with the addition of new types of computing models and new types of computing accelerators to today’s servers and cloud-based infrastructure. This is one of the key reasons that Intel recently doubled down on its commitment to server, cloud and data-center markets as part of the company’s evolving strategy.

Until recently, virtually all the computing effort done on servers — from email and web page delivery to high-performance computing — was done on CPUs that were conceptually and architecturally similar to the ones found in today’s PCs.

Traditional server CPUs have made enormous improvements in performance over the last several decades, thanks in part to the benefits of Moore’s law. This week, Intel announced a Xeon server CPU, the E7 V4, that is optimized for analytics with 24 independent cores.

Everything from smart digital assistants to autonomous cars to virtual reality is being enabled and enhanced with the addition of new types of computing models and accelerators to today’s servers and cloud-based infrastructure.

Recognizing the growing importance of cloud-based computing models, a number of competitors have tried to work their way into the server CPU market, but at last count, Intel still owns a staggering 99 percent share. Qualcomm has announced some ARM-based server CPUs, and Cavium introduced its new Thunder X2 ARM-based CPU at last week’s Computex show in Taiwan, but both companies face uphill battles. A potentially more interesting competitive threat could come from AMD. After a several year competitive lull, AMD is expected to finally make a serious reentry into the server CPU market this fall when its new x86 core, code-named Zen (also previewed at Computex) is expected to be announced and integrated into new server CPUs.

Some of the more interesting developments in server design are coming from the addition of new chips that serve as accelerators for specific kinds of workloads. Much as a GPU inside a PC works alongside the CPU and powers certain types of software, new chips are being added to traditional servers in order to enhance their capabilities. In fact, GPUs are now being integrated into servers for applications such as graphics virtualizations and artificial intelligence. The biggest noise has been created by Nvidia with its use of GPUs and GPU-based chips for applications like deep learning. While CPUs are essentially optimized to do one thing very fast, GPUs are optimized to do lots of relatively simple things simultaneously.

Visual-based pattern matching, at the heart of many artificial intelligence algorithms, for example, is ideally suited to the simultaneous computing capabilities of GPUs. As a result, Nvidia has taken some of its GPU architectures and created the Tesla line of accelerator cards for servers.

Some of the more interesting developments in server design are coming from the addition of new chips that serve as accelerators for specific kinds of workloads.

While not as well-known, Intel has offered a line of parallel-processing optimized chips it calls Intel Phi to the supercomputing and high-performance computing (HPC) market for several years. Unlike Tesla (and forthcoming offerings that AMD is likely to bring to servers), Intel’s Phi chips are not based on GPUs but on a different variant of its own X86-based designs. Given the growing number of parallel processing-based workloads, it wouldn’t be surprising to see Intel bring the parallel computing capabilities of Phi to the more general-purpose server market in the future for machine-learning workloads.

In addition, Intel recently made a high-profile purchase of Altera, a company that specializes in FPGAs (field programmable gate arrays). FPGAs are essentially programmable chips that can be used to perform a variety of specific functions more efficiently than general purpose CPUs and GPUs. While the requirements vary depending on workloads, FPGAs are known to be optimized for applications like signal processing and high-speed data transfers. Given the extreme performance requirements of today’s most demanding cloud applications, the need to quickly access storage and networking elements of cloud-based servers is critical, and FPGAs can be used for these purposes, as well.

Many newer server workloads, such as big-data analytics engines, also require fast access to huge amounts of data in memory. This, in turn, is driving interest in new types of memory, storage and even computing architectures. These issues are at the heart of HP Enterprise’s The Machine concept for a server of the future. In the nearer term, memory architectures like the Micron and Intel-driven 3-D Xpoint technology, which combines the benefits of traditional DRAM and flash memory, will help drive new levels of real-time performance even with existing server designs.

Today’s servers have come a long way from the PC-like, CPU-dominated world of just a few years back.

The bottom line is that today’s servers have come a long way from the PC-like, CPU-dominated world of just a few years back. As we see the rise of new types of workloads, we’re likely to see even more chip accelerators optimized to do certain tasks. Google, for example, recently announced a TPU, which is a chip it designed (many believe it to be a customized version of an FPGA) specifically to accelerate the performance of its TensorFlow deep learning software. Other semiconductor makers are working on different types of specialized accelerators for applications such as computer vision and more.

In addition, we’re likely to see combinations of these different elements in order to meet the wide range of demands that future servers will face. One of Intel’s Computex announcements, for example, was a new server CPU that integrated the latest elements of its Xeon line with FPGA elements from the Altera acquisition.

Of course, simply throwing new chips at a workload won’t do anything without the appropriate software. One of the biggest challenges of introducing new chip architectures is the amount of effort it takes to write (or rewrite) code that can specifically take advantage of the new benefits the different architectures offer. This is one of the reasons x86-based traditional CPUs continue to dominate the server market. Looking forward, however, many of the exciting new cloud-based services need to dramatically scale their efforts around a more limited set of software, which makes the potential opportunity for new types of chip accelerators a compelling one.

Diving into the details of server architectures can quickly get overwhelming. However, having at least a basic understanding of how they work can help give you a better sense of how today’s cloud-based applications are being delivered, and might provide a glimpse into the applications and services of tomorrow.


Bob O’Donnell is the founder and chief analyst of Technalysis Research LLC, a technology consulting and market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. Reach him @bobodtech.

This article originally appeared on Recode.net.

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