In 1971 Intel released the 4004. The 4004 was their first 4-bit single core processor, and for the next 34 years, that’s pretty much how x86 computing progressed. Sure they bumped up the architecture and speed as designs and processes improved, but one thing remained constant a single processing engine. Under pressure in the 1990s from Unix workstations driven by Reduced Instruction Set Computing (RISC) with multi-core pipeline architectures like IBM’s PowerPC, Sun’s Ultra-SPARC and the MIPS R3000 Intel began exploring multi-core architectures.
So from 1971 until 2005, every x86 processor had a single core, life was simple. Intel even provided reference designs so system builders could even put two of these processors into the same system. Sure there were fringe companies that developed Symmetrical Multi-Processing (SMP) systems (ex. Sequent & NEC) with more than two CPU sockets, but they were large frame expensive custom servers not found in general mainstream use. So if you wanted to scale out your computational capacity to tackle a tough problem, you had to rack more servers.
This single core challenge largely drove commodity Linux clustering making it all the rage by the turn of the century, particularly in high-performance computing (HPC). It wasn’t uncommon to tightly couple 1,000 or more dual socket single core systems together to tackle a tough computational problem. Government agencies leveraged large clusters to model our nuclear stockpile and computationally secure it. Auto companies crashed dozens of virtual car designs on a daily basis, and oil companies crunched seismic data to compute untapped oil reserves. Then the game changed, and x86 shifted to multi-core processors. Yesterday Intel announced the availability of their new Skylake server platform, but the industry is already refocusing on 2018’s Cascade Lake 32 core, 64 thread, server chip. So why does all this matter?
As a general rule, Google doesn’t publish or confirm the computation capacity of their data centers. If we pick a specific example, their Oregon data center, we can apply particular assumptions and project that it’s roughly 100,000 servers. Again assuming they are using dual socket eight core hyper-threaded processors this translates to 3.2 million parallel threads of computation tightly coupled in one physical location potentially addressing a single problem. Structures like this are quickly approximating what took nature millions of years to develop, an organic brain based on the neuron. Neurons on average have 7,000 connections to both local and remote neurons within their system, that’s a considerable amount of networking per single computational unit.
By contrast, the common cockroach has one million neurons, and that frog you played with as a kid sports 16 million. So if we equate a neuron to a single thread of execution on an x86 that would put a Google data center somewhere between the cockroach and a frog. If in 2018 Google were to upgrade the Oregon facility to Cascade Lakes it would still be only 12.8 million threads, still less than a frog. Given the geometric core growth though it will only be a few decades before Google is deploying data centers approaching the capability of the 86 billion neurons found in the human brain. Oh, and that’s assuming an x86 thread, and a neuron is even computationally similar.
So what is Neural Class Networking? As mentioned above a neuron on average is connected to 7,000 other neurons. Imagine if every hardware thread of execution in your server were networked to even 64 external threads of execution on related systems working on the same problem, that’s the start of neural class networking. Today we have servers with typically 32 threads of execution. Solarflare’s newest generation of XtremeScale Smart NICs provides 2,048 virtual NICs, so each thread of those 32 threads has the capability to sustain 64 dedicated hardware paths to other external threads. That’s the start of Neural Class Networking.
My first co-op job at IBM Research back in 1984 was to help roll out the IBM PC to the companies best and brightest. It wasn’t long into that position, perhaps a month, when we noticed a large number of monochrome monitors had a consistent burn-in pattern. A horizontal bar across the top, and a vertical bar on the left. Now I was not new to personal computers having purchased my own TRS-80 Model III a year earlier, but it was apparent that a vast number within Research were all being used to do the same thing. So I asked, VisiCalc (MS Excel’s great-grandfather).
Container networking is walking in the footsteps taken by virtualization over a decade ago. Still, networking is a non-trivial task as there are both underlay and overlay networks one needs to consider. Underlay Networks like a bridge, MACVLAN and IPVLAN are designed to map physical ports on the server to containers with as little overhead as possible. Conversely, there are also Overlay networks that require packet level encapsulation using technologies like VXLAN and NVGRE to accomplish the same goals. Anytime network packets have to flow through hypervisors or layers of virtualization performance will suffer. Towards that end, Solarflare is now providing the following four benefits for those leveraging containers.
Everyone hates waiting, and as such we continually improve the performance of our technology to remove this waiting, often called latency. In markets like financial trading, this latency can be monetized. Several years ago a high-frequency trading shop told me that for them one microsecond (millionth of a second) improvement translated to $60K per network port per day. How is that even possible? Well, if my stock market trade gets into the exchange before your’s I win, you lose, it’s that simple. Prior to May 2017, Solarflare had a network latency solution for electronic markets that delivered 250 nanoseconds (billionths of a second) from tick to trade. The “tick” in “tick to trade” is the market data that arrives from the exchange via a network packet.
The effectiveness of our communication as a species is one of our defining characteristics. Earlier this week while waiting in a customer’s lobby in Chicago I noticed four framed posters displaying all the hand signals used in the trading pits of four major markets. Having been focused on electronic trading for the past decade this “ancient” form of communications became an instant curiosity worthy of inspection. On reflection, I was amazed to think that trillions of dollars in transactions over decades had been conducted purely by people motioning with their hands.
players, we’ll call this the first wave. Early products by Neterion and Intel carried extremely high price tags, often approaching $10K. This lead to a flood of companies jumping into the market in an effort to secure an early mover advantage. High-Performance Computing (HPC) companies like Myricom with it’s Myrinet 2G, and Mellanox with Infiniband SDR 10G was viewed by some as possibly having a competitive advantage as they’d already developed silicon in this area. In August of 2005, I joined Myricom to help them transition from HPC to the wider Ethernet market. By March of 2006, we launched a single port 10GbE product with a $595 price point, three years accompanied by a 10X drop in market price. That year the 10GbE market had grown to 18 different companies all offering 10GbE server adapters, we’ll consider this the second wave. In my 2013 article “
The partnership of Stratus, the global standard for fault-tolerant hardware solutions, and Solarflare, the unchallenged leader in application network acceleration for financial services, at face value seems like an odd one. Stratus ‘always on’ server technology removes all single points of failure, which eliminates the need to write and maintain costly code to ensure high availability and fast failover scenarios. Stratus and high performance are rarely been used in the same sentence.
Thanks in part to IBM, no field uses acronyms like computer science. Recently I was asked to pick up product management responsibility for an evolving new line of cybersecurity products. To be relevant one needs to be current with the trendy lingo, as such here are some of the must-know acronyms. So if you’re a budding cyber security student, or hacker in training see how many of these you know off the top of your head. Note, every one of these has been checked, and the full expansion of the acronym links to the appropriate Wikipedia page. Good luck, and enjoy.