If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

In this special guest feature, Rob Farber from TechEnablement writes that modernized code can deliver significant speedups on machine learning applications. Benchmarks, customer experiences, and the technical literature have shown that code modernization can greatly increase application performance on both Intel Xeon and Intel Xeon Phi processors. Colfax Research recently published a study showing that image tagging performance using the open source NeuralTalk2 software can be improved 28x on Intel Xeon processors and by over 55x on the latest Intel Xeon Phi processors (specifically an Intel Xeon Phi processor 7210). For the study, Colfax Research focused on modernizing the C-language Torch middleware while only one line was changed in the high-level Lua scripts. NeuralTalk2 uses machine learning algorithms to analyze real-life photographs of complex scenes and produce a correct textual description of the objects in the scene and relationships between them (e.g., "a cat is sitting on a couch", "woman is holding a cell phone in her hand", "a horse-drawn carriage is moving through a field", etc.) Captioned examples are show in the figure below.

Intel (NASDAQ:INTC) told technology developers few days ago that the company is planning to bring to the market a new version of its Xeon Phi processor (code named Knights Mill) next year with special features supporting artificial intelligence ("AI"). The ultimate goal of scientists with AI is making the computer to mimic human brain. Since the basic advantage of a human brain over a computer is that the human brain can perform extensive parallel processing, primary requirement for running AI workloads is massive amount of parallel processing. The question is can the Xeon Phi of future offer this kind of parallel processing? According to a recent research conducted by researchers at the University of Massachusetts Dartmouth, modern budget GPUs are capable of offering similar application boosts compared to high-end GPUs in HPC (high performance computing) despite having lower floating point precision capabilities.

Intel (NASDAQ:INTC) and NVIDIA (NASDAQ:NVDA) are both betting that deep learning is going to fuel everything from cloud computing to driverless cars -- and the rivalry is already starting to heat up. Intel said recently that its new Knights Landing Xeon Phi processors are faster and can be scaled better than graphics processing units (GPUs) that NVIDIA makes. The company wrote a blog post in response, saying that it needed to correct a few of Intel's "mistakes," and took a jab at the company, saying, "It's understandable that newcomers to the field may not be aware of all the developments that have been taking place in both hardware and software." Essentially, NVIDIA said that Intel's data was 18 months old, and pointed out that the new data (available to anyone) shows that NVIDIA's Maxwell GPUs are actually 30% faster than Intel's Xeon Phi. The company also highlighted that its new Titan X server with its advanced Pascal architecture is actually about two times faster than Intel's new processors, and its DGX-1 supercomputer is is five times faster.

Intel practically owns the business of selling chips for data center servers. IDC pegs its share of the market at 99%. But Intel doesn't have such a strong grip on the latest, and hottest, slice of the market: artificial intelligence. It faces stiff competition from graphics chip expert Nvidia, whose graphics cards are currently the most popular for powering deep learning neural networks that perform mainstay artificial intelligence tasks like image recognition, voice recognition and natural language processing. Hoping to push back against Nvidia's inroads, Intel announced on Wednesday a new server processor tailored for artificial intelligence, the third-generation Xeon Phi, code-named "Knights Mill."

Data scientists and deep and machine learning researchers rely on frameworks and libraries such as Torch, Caffe, TensorFlow, and Theano. Studies by Colfax Research and Kyoto University have found that existing open source packages such as Torch and Theano deliver significantly faster performance through the use of Intel Scalable System Framework (Intel SSF) technologies like the Intel compiler and performance libraries for Intel Math Kernel Library (Intel MKL), Intel MPI (Message Passing Interface), and Intel Threading Building Blocks (Intel TBB), and Intel Distribution for Python (Intel Python). Andrey Vladimirov (Head of HPC Research, Colfax Research) noted that "new Intel SSF hardware and software in combination with code modernization delivered an observed 50x machine learning performance improvement in our case study". In the Colfax Research and Kyoto case studies as well as general Python scientific computing benchmarks, results run up to two orders of magnitude (100x) faster as a result of using Intel SSF technologies. Python is a powerful and popular scripting language that provides fast and fundamental tools for machine learning and scientific computing through popular packages such as scikit-learn, NumPy and SciPy.

Benchmarks are an important tool for measuring performance, but in a rapidly evolving field it can be difficult to keep up with the state of the art. Recently Intel published some incorrect "facts" about their long promised Xeon Phi processors. Few fields are moving faster right now than deep learning. Today's neural networks are 6x deeper and more powerful than just a few years ago. There are new techniques in multi-GPU scaling that offer even faster training performance.

With the general availability of the "Knights Landing" Xeon Phi many core processors from Intel last month, some of the largest supercomputing labs on the planet are getting their first taste of what the future style of high performance computing could look like for the rest of us. We are not suggesting that the Xeon Phi processor will be the only compute engine that will be deployed to run traditional simulation and modeling applications as well as data analytics, graph processing, and deep learning algorithms. But we are suggesting that this style of compute engine – it is more than a processor since it includes high bandwidth memory and fabric interconnect adapters on a single package – is what the future looks like. And that goes for Knights family processors and co-processors as well as the "Pascal" and "Volta" accelerators made by Nvidia, the Sparc64-XIfx and ARM chips that will be used in the used in the Post-K system in Japan made by Fujitsu, the Matrix2000 DSP accelerator being created by China for one of its pre-exascale systems, or the CPU-GPU hybrids based on its "Zen" Opterons that AMD is cooking up for supercomputing systems in the United States and, with licensing partners, in China. During the recent ISC16 supercomputing conference in Frankfurt, Germany, Intel gathered up the executives in charge of some of the largest supercomputing facilities on the planet who are also – not coincidentally, but absolutely intentionally – also early adopters of the Knights Landing Xeon Phi and, in some cases, the Omni-Path interconnect that is a kicker to Intel's True Scale InfiniBand networking.