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ISS-003 Intel(R) Server Specialist Certification

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ISS-003 exam Dumps Source : Intel(R) Server Specialist Certification

Test Code : ISS-003
Test cognomen : Intel(R) Server Specialist Certification
Vendor cognomen : Intel
: 186 existent Questions

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Intel Intel(R) Server Specialist Certification

Intel, Qualcomm Hit CES: As mobile Chip combat Heats Up, Tech Giants Will Collide | killexams.com existent Questions and Pass4sure dumps

Editor’s subsist aware: Steve Patterson’s observations and writing are according to his 20 years working within the primordial ooze of birth-americain Boston and the San Francisco Bay enviornment. keep him on Twitter @stevep2007.

at the start of CES 2013, Qualcomm has an enviable predominant market place, owning half the mobile processor shipments and basically complete the LTE/4G baseband chipset shipments. but it has limited brand recognition compared to the Android smartphones it powers. To cement its dominance within the turning out to subsist cellular market, Qualcomm must emerge as a client manufacturer from the obscurity of being a factor in smartphones sold below other organizations’ brands. otherwise, it will lose its expense top rate and precious result in competing cell processor producers.

Intel, against this, has been rewarded with brand preference within the notebook and server market after decades of funding in inventive advertising that influenced consumers’ buy selections in response to laptop company’s option of Intel’s processor in preference to the brand’s brand. Intel, late to the cell processor market, now exigency to radically change its technologies into cell product shipments to counter stalled Wintel revenues. because the chart under from Kleiner Perkins accomplice Mary Meeker illustrates, Intel should aggressively pursue the mobile market and compete with Qualcomm.

intels dilemma

Qualcomm CEO Paul Jacobs has begun 2013 with a transparent objective of strengthening Qualcomm’s manufacturer. After acting on the Charlie Rose exhibit closing week, Jacobs will revert to keynote CES 2013. Jacob’s theme, “Born cellular,” is reputed to construct brand cognizance for Qualcomm’s silicon chips. he is presumably hoping for the selfsame client branding results in customer smartphone buy selections that the Intel internal crusade delivered for Intel in purchaser computing device purchase decisions.

Intel, in the meantime, will disburse CES to launch writing its cell story. nowadays, after a yr of training, Intel announced the Bay path-T microprocessor and the Bay path-T pills that producers are setting up for both home windows eight and Android. The Bay trail-T is a subside to 22 nm and enhancement of the Atom Medfield chip at the flash in production in an endeavor to bring as an needy lot as twice the performance with extended battery lifestyles. The Bay trail-T is a suitable Wintel microprocessor that will precipitate home windows 8 and any windows app as neatly and Android and Android apps. this is an competencies over the ARM powered Microsoft floor that runs a Microsoft variant referred to as home windows RT that only runs windows purposes that beget been re-coded and recompiled.

In February at the cellular World Congress in Barcelona, Intel is anticipated to replace its buyers and companions on its Merrifield processor due to subsist launched later in 2013. Merrifield, first introduced in might also 2012 at Intel’s investor conference, is focused at smartphones, a subside to 22 nm with efficiency improvements corresponding to the Bay path-T. Intel has begun production of its LTE/4G baseband chip, according to Intel’s spokesman Jon Carville.

also expected within the first quarter in education of Intel’s mobile rally is the announcement of a successor to retiring CEO Paul Otellini who should subsist accountable for main Intel into the cellular period.

A Two-front warfare: constructing customer company awareness and the fastest, Lowest-power SoCs

Most observers define the mobile gadget combat in terms of the Apple, Android, and Samsung manufacturers, but it surely is in fact a two-entrance combat – the second entrance being the competition to build the greatest and built-in systems-on-a-Chip. SoCs are such an gargantuan factor in making a choice on the skill of a cellular machine that main the cell outfit industry is synonymous with leading SoC design. although there are lots of cellular processor SoC rivals, there are best a few, together with Intel, NVidia, Qualcomm, Samsung, and upstart MediaTek, that may beget an influence on this market, because they've microprocessor technology and both personal or beget licensed LTE/4G baseband expertise.

LTE/4G is delivered as a silicon fraction called a baseband modem that pushes SoC designers to the bounds of innovation to design and fabricate for low-vigour consumption functions, comparable to cellular instruments. it is a captious upgrade to 3G that cellular users exigency to develop into permanently untethered.

Intel has moved without detain to backfill its LTE/4G intellectual property and engineering design ability. closing year Intel obtained Infineon wireless, a fabless semiconductor baseband modem industry with LTE/4G patents and a long legacy of providing baseband modems to handset and base-station producers. With the acquisition, Intel gained a product portfolio, design capability, and patents that Hermann Eul, vice chairman and Popular manager of the Intel mobile and Communications neighborhood, describes as a “profound portfolio of patent protection.” via acquisition, Intel has joined Qualcomm and Samsung because the only baseband modem suppliers that beget the patent portfolio and scale to coordinate LTE/4G edifice through part certifications and approvals required by using every cellular provider.

In mid-2012 Intel doubled down on LTE/4G when it opened a modern workplace in San Diego, Qualcomm’s domestic territory, with the specific intent of recruiting from the area’s deep pool of baseband talent to construct a brand modern modem design group for LTE/4G applied sciences. There are rumors that Intel could subsist making as a minimum another LTE/4G investment in 2013. A 2d supply for LTE/4G baseband modems from Intel could subsist welcomed with the aid of smartphone producers as an alternative choice to Qualcomm’s near monopoly of LTE/4G modems and should palliate deliver constraints.

Intel has verified that it will possibly construct a competitive smartphone SoC. The Intel Atom Medfield processor powers the Motorola Droid Razr i. It changed into established by using The Verge towards the Qualcomm Snapdragon S4 powered Droid Razr M that's constructed with practically an identical design apart from the cell processor SoCs. The Intel-powered Razor i confirmed equal or greater application compatibility and energy efficiency. The Razr i’s efficiency and Intel’s seven design wins, together with Lenovo, are exquisite.

but whereas Intel’s skill to convey first-rate processor SoC designs is undeniable, Qualcomm’s predominant position suggests that smartphone manufacturers want suppliers that present each processor SoCs and baseband modems. providing LTE/4G to smartphone producers within the first half of 2013 is a crucial milestone for Intel.

Qualcomm poses big challenges because it has already delivered its Snapdragon processor with an LTE/4G baseband modem integrated on a separate chip. in keeping with Qualcomm VP of advertising Tim McDonough, “Qualcomm is on its 2d release of LTE/4G and sampling its third liberate while rivals beget yet to broadcast or carry a primary liberate.”

Intel’s Response to Qualcomm’s Integrations of Processor and LTE/4G

Intel separates its creation of LTE/4G and Atom processors. Eul believes it is just too early in the evolution of Intel’s LTE/4G to combine it on a separate chip with an Atom processor since the two applied sciences will iterate at different quotes precipitated by pass of part market hobbies. They may additionally espy Intel’s LTE/four modems built-in with a future Atom SoC when an extra shrink to 14 nm takes location someday after 2014.

To shut the hole, Intel will apply to smartphones and tablets the big programs-stage design capability that it honed when almost monopolizing the X86 pc and server business. Intel will carry smartphone reference designs to subsist able to allow manufacturers to supply comprehensive optimized smartphones the usage of Intel’s components.

Intel also obtained provider certification and approval expertise and relationships with its acquisition of Infineon. Eul’s strategy is to acquire each the add-ons and reference designs in the course of the prolonged and intricate cell carrier certification and acceptance manner and partake the certifications with smartphone manufacturers to shorten the time to marketplace for a brand modern smartphone with the aid of at least six months. according to the context of Eul’s feedback, Intel might even disburse its big provide-chain relationships to deliver an Intel-branded smartphone that cuts approval times.

the brand modern CEO must Lead Intel Into The post-workstation era

How a much deal of Intel’s growth within the cell SoC market could subsist at Qualcomm’s price is doubtful on account of Qualcomm’s potent market spot and captious design skill. Consolidation will also subsist expected as cell processor SoC suppliers with out powerful LTE/4G patent portfolios Go away the mobile company as exemplified by pass of Texas devices’ contemporary retargeting of its OMAP mobile chipset to automotive infotainment functions. Qualcomm may also arbiter dinky power from Intel while the trade angle consolidates. longer term here's an epic championship fight between two brilliant competitors, every with greater than $10 billion in cash that disburse 15 to twenty percent of revenues on sober scientific R&D.

Intel’s failure to exploit the cell SoC market has often drawn criticism. however Intel has produced few early subscale successes. Willy Shih, a Professor of administration on the Harvard company school, has studied Intel and informed us that almost complete of Intel’s “New company Initiative” ventures failed when the brand modern ventures could not scale to match the profitability or volumes of the core x86 microprocessor business. The cellular processor and communications enterprise has the attributes that meet Intel’s expectations for boom and scale. but since Intel has complete of the design, highbrow property, and manufacturing capability to succeed within the cell market, the modern CEO will deserve to carve out Intel’s top talent and state-of-the-artwork fabrication competence and pledge it to successful mobile market share, possibly at some pervade to the core computer microprocessor company.

in view that a further boom market of the magnitude that Intel should reignite augment isn't obvious, the next CEO may subsist chosen in line with his potential to steer Intel into the attach up-notebook era. And he may subsist capable of inform this epic – perhaps at CES 2014.


The comprehensive Developer’s e-book to Intel AI materials & tools (Portfolio) | killexams.com existent Questions and Pass4sure dumps

INTEL wait TO wait AI PORTFOLIO

Intel presents probably the most comprehensive and part portfolio of items for laptop and deep studying from the statistics middle to the edge. by pass of featuring compute substances for computer studying and deep getting to know practising and inference compute needs, Intel is enabling clients to birth exploring AI innovation without making their own investments in compute resources up front. apart from cloud compute components, frameworks, outfit and aid are offered.

Down the highway, the Intel Nervana hardware will deliver unparalleled deep discovering performance and effectivity that dwarf customary-purpose GPUs.

developers can leverage on Intel’s AI product portfolio to precipitate up their AI options:

1. Intel Xeon Processors

Intel processors gasoline an unlimited majority of servers in facts centers (>97%). In a market for therefore-called “accelerator” chips, Intel at complete times bursts myths on practicing and inference on CPUs lagging in the back of GPUs. Intel has a portfolio of AI expertise for the records center, complete introduced below their Intel Nervana AI Portfolio for synergy. It contains software outfit and framework optimizations to deliver extremely kindly efficiency for information-intensive workloads to enrich the Intel Xeon Scalable processor (which meets the computing device researching and deep researching wants of most organizations), Intel Xeon Phi processor, AI accelerators enjoy Intel FPGAs, and AI-really kindly Intel Nervana™ expertise (codenamed Lake Crest).

i) Intel Xeon Scalable Processors : Designed with advanced AI and HPC capabilities, including gigantic raises in reminiscence and that i/O bandwidth, facts scientists are capable of release insights faster from facts-intensive workloads. It has the means to accelerate product innovation with its developments across compute, storage, reminiscence and i/O, so they can vastly strengthen AI. It gives you as much as 2.2X higher deep learning practising and inference performance over prior generations. these days, Intel introduced the provision of the Intel Xeon processor E3–1200 v6 product family unit, which contains eight modern models that supply key improvements in processor pace, stronger reminiscence capabilities, hardware-stronger security and reliability elements with aid for the latest Intel images.

ii) Intel Xeon Phi Processors : Intel’s upcoming technology of Intel Xeon Phi processors (codenamed Knights Mill) will convey tremendously quicker throughput for training complicated neural networks for workloads that require dense compute and extremely exorbitant ranges of parallelism.

iii) Intel FPGAs : FPGAs present essentially the most plight for inference, as they will also subsist upgraded while within the box and will provide low latencies if observed on the aspect alongside a CPU, equivalent to in an independent automobile. Intel Arria and Stratix FPGAs duty as programmable hardware accelerators for specialised deep researching inference processing when combined with Intel Xeon processors.

in the course of the Intel scholar Developer program, builders can gain direct entry to their own illustration on the Intel® Xeon Phi™ powered AI cluster, to punch the construction and training of deep getting to know models.

2. Intel Libraries

Intel has developed totally optimized math libraries that build the most useful disburse of the a few households of Intel processors. these optimized math libraries are the groundwork for larger-degree tools and frameworks that are used to remedy ML and DL complications throughout different domains.

Intel Math Kernel Library (Intel MKL) : contains a set of highly-optimized efficiency primitives for DL purposes, as well as services which beget been incredibly optimized for single-core vectorization and cache reminiscence utilization

Intel Math Kernel Library for deep Neural Networks (Intel MKL-DNN) : This library gives optimized deep neural network primitives for speedy integration into DL frameworks. It includes extremely vectorized and threaded edifice blocks for enforcing convolutional neural networks with C and C++ interfaces. useful links to win complete started on MKL-DNN:

Intel records Analytics Acceleration Library (Intel DAAL) : helps pace big records analytics by using offering totally optimized algorithmic edifice blocks for complete statistics analysis degrees (Pre-processing, Transformation, evaluation, Modeling, Validation, and conclusion Making) for offline, streaming and disbursed analytics usages. It’s designed for disburse with accepted statistics systems including Hadoop, Spark, R, and MATLAB for incredibly efficient records entry.

three. Intel Optimized Frameworks

utility innovation is key to taking talents of features in underlying hardware. it is also key to accelerating the edifice process for analytics and HPC applications.

beyond silicon, Intel is committed to optimizing probably the most established analytics, computing device & deep studying frameworks together with Tensorflow, Caffe, Torch, Theano, and Spark-based mostly MLlib and BigDL, so that they precipitate most trustworthy on Intel architecture (IA).

A handy technique to travail with deep getting to know is to disburse the Intel’s optimized ML and DL frameworks. using Intel optimized tools and frameworks to educate and deploy deep networks guarantees that these outfit will disburse Intel structure in the gold measure way. Examples of how some of these frameworks beget been optimized to acquire handicap of Intel structure, as well as charts showing speed-up of these optimized frameworks compared to non-optimized ones, will also subsist organize in https://utility.intel.com/en-us/ai/deep-researching

The frameworks disburse the performance of the libraries described above. They permit developers to operate practising and inference in a enormously efficient fashion using Intel processors.

4. Intel deep getting to know practising device

since mid-2017, Intel ambassadors were protecting workshops in a variety of universities and corporates round Kenya, as a fraction of the greater international ecosystem of the Intel student Developer program. significant to these workshops has been the practicing of the deep learning working towards utensil from Intel, which to pretense the least is heaven-sent for any developer or information scientist. And there are a lot of reasons to it.

Most records scientists and builders don’t yet disburse deep gaining knowledge of, because of the relative complexity of customizing deep discovering models for his or her personal problems, the challenges in installing and the usage of the necessary frameworks, and the low performance of open supply deep discovering frameworks on generic CPUs. This has been ground to lots of constraints in democratizing AI.

The reply to the complexity is Intel’s deep studying practicing tool. The working towards device is an internet application working on a Linux server and provides a person-friendly, intuitive interface for edifice and practising deep studying models. The best fraction of the device is how handy it's to educate and set up deep gaining knowledge of fashions for photograph and textual content analysis problems. testing it out with the renowned MNIST dataset, practicing become finished inside under 30 minutes, a job that would initially acquire greater than double the time. Intel’s deep gaining knowledge of SDK includes tools for light information preprocessing and model compression, presenting an end-to-conclusion solution. in addition, it helps scale-out on assorted computer systems for training, in addition to the disburse of compression strategies and optimization for deployment of the fashions on a considerable number of Intel hardware structures, together with these with restrained reminiscence footprints and performance constraints.

It provides:

  • The means to visualize precise-time information — with out programming — throughout the introduction of deep neural networks
  • Simplified installing of deep learning frameworks which are optimized for Intel architecture
  • integrated Jupyter* Notebooks for superior model creation
  • The visible competence of developing, tuning, and running deep discovering algorithms with the model wizard
  • It is soundless at the flash in beta edition, even though it is confidently solid gauging from previous practicing and inference moves.

    For a comprehensive setting up ebook, check out Marvin Ngesa’s weblog.

    5. Intel Nervana DevCloud

    Intel Nervana DevCloud is a cloud-hosted hardware and application platform for developers, information scientists, researchers, lecturers and startups to learn, sandbox and precipitate up construction of AI options with free compute cloud entry powered via Intel Xeon Scalable processors. by proposing compute supplies for laptop researching and deep gaining knowledge of practicing and inference compute wants, Intel is enabling users to delivery exploring AI innovation with out making their own investments in compute components up entrance.

    It may subsist accessible to 200,000 developers, including startups from Intel’s Nervana AI Academy, via a simple registration procedure.

    6. Movidius Neural Compute Stick

    As Intel pushes forward with desktop getting to know development by the disburse of fraction contraptions, it got Movidius, a chipmaker company main in the VPU (vision Processing Unit) platform for on-equipment vision processing. After their acquisition, they beget introduced an array of products that provide power to inference on the facet:

    i) Movidius Neural Compute Stick (NCS): The Movidius (NCS) is a tiny fanless deep getting to know device that you should disburse to gain knowledge of AI programming at the edge. As Intel states, the Movidius NCS is “the area’s first self-contained AI accelerator in a USB structure,” and is designed to permit host instruments to technique deep neural networks natively — or in other words, at the edge. In turn, this provides builders and researchers with a low power and competitively priced formulation to augment and optimize quite a few offline AI functions. below the hood, the Movidius NCS works by pass of translating a standard, educated Caffe-based convolutional neural network (CNN) into an embedded neural community that then runs on the VPU.

    Movidius Neural Compute Stick

    ii) Myriad X: Myriad X is the primary VPU to characteristic the Neural Compute Engine — a dedicated hardware accelerator for deep neural community inferences. The Neural Compute Engine at the side of the 16 powerful SHAVE cores and an extremely-excessive throughput bright reminiscence fabric makes Myriad X the trade leader for on-device deep neural networks and computing device imaginative and prescient purposes.

    Movidius Myriad X 7. Intel Saffron

    Saffron know-how develops cognitive computing programs that disburse incremental learning to withhold in wit and unify by entity. Saffron’s entertaining know-how ingests information from disparate sources and automatically connects the dots to assist agencies of every benevolent ameliorate determination-making. It captures the connections between information points at the entity stage and stores these connections in an associative reminiscence.

    The entertaining bit is that when computer discovering logics and Saffron’s reasoning techniques are fused, ingenious local analytics becomes viable within the cyber web of issues.

    8. relied on Analytics Platform

    trusted Analytics Platform (faucet) is a group of open supply outfit designed to build massive records projects easier to implement. faucet contains a sequence of algorithms, tools and engines to analyze and collaborate with facts scientists. The software is open supply and includes integration with Intel structure and hardware. tap includes:

    · a knowledge layer that contains Apache Hadoop and Spark optimized for Intel hardware.

    · An open analytics layer that can assist predictive application programming interfaces.

    · guide for cloud aboriginal apps.


    fact Sheet: Unleashing the energy of artificial Intelligence in Healthcare with Intel applied sciences | killexams.com existent Questions and Pass4sure dumps

    artificial Intelligence: machine studying, deep studying and more

    artificial intelligence (AI) is now complete around us, as machines increasingly profit the capability to feel, learn, reason, act and adjust in the precise world. plenty of the AI highlight is focused on deep discovering, a department of desktop discovering that makes disburse of neural networks to comprehend complicated and unstructured records (working towards) and disburse that knowing to classify, respect and process modern inputs (inference). deep getting to know is offering breakthroughs in areas enjoy photo awareness, speech cognizance, natural language processing and other intricate initiatives.

    Processing deep learning Workloads in Healthcare

    Deep gaining knowledge of workloads require an incredible quantity of computational vigour to precipitate advanced mathematical algorithms and technique tall quantities of records. whereas GPUs beget been used for some deep gaining knowledge of working towards purposes, they don’t necessarily beget a efficiency aspect for deep studying inference. In healthcare in specific, there are lots of examples of applications that precipitate more advantageous on CPUs than GPUs.

    That’s why for healthcare groups working AI workloads, Intel® Xeon® Scalable processors provide a much computational groundwork. Intel Xeon Scalable processors are optimized for AI, scale up promptly and seamlessly for two.1x quicker deep discovering performance over previous generations1, and present server-classification reliability and workload flexibility1.

    Versatility

    Many corporations which are enforcing AI don’t should coach models 24/7. those who achieve deserve to educate models around the clock require a committed accelerator, however the majority of businesses conveniently exigency to instruct after which install a model. If an organization invests lots of bucks in constructing a committed acceleration circulation, it could actually’t achieve the repose with that infrastructure beyond working towards – it is going to just sit idle.

    Intel Xeon Scalable processors are extra agile. they are designed to flex with enterprise needs to assist the workload needs of the moment, enabling companies to leverage their facts headquarters infrastructure for AI applications and a wide array of other workloads.

    improved Utilization

    Most groups beget as a minimum 35 percent free utilization of processing capability, if now not extra. This means they've can subsist obtaining more suitable ROI from their infrastructure investments. With Intel Xeon Scalable processors, they can leverage this unused competence to invoke AI while soundless meeting the needs of different functions.

    Technical advantages

    GPUs are often leveraged as an accelerator, however they've obstacles when it comes to edifice models to address healthcare concerns. These barriers launch with memory. GPUs are constrained to simplest 12 to sixteen gigabytes of reminiscence on the chip itself, and that restricts the measurement and capabilities of the mannequin that can also subsist constructed. This reminiscence hassle is a selected hardship in healthcare AI work, which commonly involves edifice big fashions – for example, to assist a giant CT scan.

    For these days’s AI networks deployed on a measure GPU, the photograph measurement for a scan that will also subsist developed is constantly 256 x 256 pixels. at the most, that image may well subsist one thousand x one thousand pixels, but that’s pushing it. If clinicians beget a 4K picture the spot they really want complete of those pixels to build an evaluation — as an instance, to become alert of a tumor in a tiny factor of the photo — then they beget got an issue. after they scale that 4K image right down to 256 x 256 pixels, they lose complete of the property temporal decision.

    The equal model can subsist developed with Intel Xeon Scalable processors with out limiting the measurement and determination because the Intel Xeon Scalable household offers a terabit of on-load reminiscence. Intel Xeon processors may also manner the picture more slowly than a GPU, but when the resolution is needed for clinical imaging, Intel Xeon processors bring.

    A Processor Designed for deep discovering

    relocating ahead, Intel has also developed the Intel® Nervana™ Neural network Processor (NNP), the area’s first processor certainly designed from the floor up for deep researching. The Intel Nervana NNP promises to further enhance medical imaging and other healthcare functions.

    the usage of the Intel Nervana platform, healthcare corporations can develop thoroughly modern courses of AI functions that maximize the quantity of facts processed and cause superior insights. as an instance, AI will permit for previous prognosis and more advantageous accuracy, assisting build the not workable viable by means of advancing research on cancer, Parkinson’s ailment and different brain disorders2.

    The Intel handicap for AI

    Intel has 50 years of adventure in assisting its shoppers build their records effective. today, Intel is the disburse of that adventure to attend healthcare agencies ply every factor of their workflows through the a success implementation of AI.

    From hardware to utility and statistics science, Intel brings its plenary suite of products and abilities to AI. Intel has the technical skills now not best to advocate companies build the arrogate infrastructure – from statistics to storage and network – but also has the statistics scientists to remember and model information and the application builders to aid build the statistics positive. in short, Intel’s handicap to implement AI goes smartly beyond the CPU.

    using the Age of AI

    At Intel, they admire the age of AI is upon us, and they recognize their technologies will attend pressure the pass forward for AI. we're additionally prompted by a want to build disburse of their technology to attend strengthen society and ply the area’s massive challenges, so we’re certainly excited about their travail in healthcare. They exigency to attend channel AI for societal kindly – they summon it AI with a purpose.

    With these strategies in mind, we’re completely pleased to subsist engaged with healthcare providers and researchers. They understand that their collective travail is assisting movement the needle – with even more plight and unlimited percentages for the long run.

    Benchmark results were acquired just before implementation of recent software patches and firmware updates meant to ply exploits known as “Spectre” and “Meltdown”.  Implementation of these updates can also build these consequences inapplicable to your machine or device.

    application and workloads used in performance assessments may beget been optimized for performance simplest on Intel microprocessors.

    performance exams, reminiscent of SYSmark and MobileMark, are measured using specific computing device programs, add-ons, software, operations and functions. Any change to any of those factors could cause the consequences to vary. you should consult different assistance and efficiency checks to aid you in entirely evaluating your reflected purchases, including the performance of that product when combined with different items. For greater finished advice visit www.intel.com/benchmarks.

    1INFERENCE using FP32 Batch size Caffe GoogleNet v1 256  AlexNet 256.

    performance estimates were obtained just before implementation of fresh application patches and firmware updates intended to address exploits known as “Spectre” and “Meltdown.”  Implementation of these updates can also build these results inapplicable to your gadget or system. software and workloads used in efficiency checks may also had been optimized for efficiency simplest on Intel microprocessors.  performance assessments, equivalent to SYSmark and MobileMark, are measured the disburse of selected computer systems, add-ons, software, operations and services.  Any alternate to any of those elements could cause the outcomes to alter.  subsist sure to talk to other guidance and efficiency checks to aid you in fully evaluating your meditated purchases, together with the performance of that product when mixed with other items. For more comprehensive advice debate with http://www.intel.com/performance  supply: Intel measured as of June 2017 Optimization observe: Intel’s compilers might also or may additionally now not optimize to the selfsame degree for non-Intel microprocessors for optimizations that aren't unique to Intel microprocessors. These optimizations comprise SSE2, SSE3, and SSSE3 guideline sets and different optimizations. Intel doesn't guarantee the availability, functionality, or effectiveness of any optimization on microprocessors now not manufactured by means of Intel. Microprocessor-elegant optimizations in this product are meant for disburse with Intel microprocessors. sure optimizations not selected to Intel microarchitecture are reserved for Intel microprocessors. gladden handle with the applicable product user and Reference publications for extra tips regarding the sure guideline units coated through this observe.

    Configurations for Inference throughput

    Processor :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , turbo ON total reminiscence 376.46GB (12slots / 32 GB / 2666 MHz).CentOS Linux-7.three.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , deep gaining knowledge of Framework caffe edition: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology :googlenet_v1 BIOS:SE5C620.86B.00.01.0004.071220170215 MKLDNN: edition: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer. Measured: 1190 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, faster disabled, scaling governor set to “efficiency” via intel_pstate driver, 256GB DDR4-2133 ECC RAM. CentOS Linux liberate 7.3.1611 (Core), Linux kernel three.10.0-514.el7.x86_64. OS power: Seagate* enterprise ST2000NX0253 2 TB 2.5″ inside challenging force. performance measured with: environment variables: KMP_AFFINITY=’granularity=fine, compact,1,0‘, OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d 2.3G -u 2.3G -g efficiency. deep gaining knowledge of Frameworks: Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time –forward_only” command, practising measured with “caffe time” command. For “ConvNet” topologies, dummy dataset became used. For other topologies, records become saved on local storage and cached in reminiscence before working towards. Topology specs from https://github.com/intel/caffe/tree/grasp/fashions/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/fashions/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; info beget been updated to disburse newer Caffe prototxt structure however are functionally equivalent). GCC four.eight.5, MKLML edition 2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe, Inference & practising measured with “caffe time” command.  For “ConvNet” topologies, dummy dataset became used. For different topologies, data became st ored on aboriginal storage and cached in reminiscence before working towards  BVLC Caffe

    Configuration for practising throughput:

    Processor :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , turbo ON total reminiscence 376.28GB (12slots / 32 GB / 2666 MHz).CentOS Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , deep studying Framework caffe version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology :alexnet BIOS:SE5C620.86B.00.01.0009.101920170742 MKLDNN: version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer. Measured: 1023 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, rapid disabled, scaling governor set to “efficiency” by pass of intel_pstate driver, 256GB DDR4-2133 ECC RAM. CentOS Linux free up 7.three.1611 (Core), Linux kernel 3.10.0-514.el7.x86_64. OS power: Seagate* industry ST2000NX0253 2 TB 2.5″ inner difficult pressure. performance measured with: environment variables: KMP_AFFINITY=’granularity=nice, compact,1,0‘, OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d 2.3G -u 2.3G -g efficiency. deep gaining knowledge of Frameworks: Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time –forward_only” command, practising measured with “caffe time” command. For “ConvNet” topologies, dummy dataset became used. For different topologies, statistics become kept on local storage and cached in reminiscence earlier than practicing. Topology specs from https://github.com/intel/caffe/tree/grasp/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/grasp/fashions/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; info were updated to build disburse of newer Caffe prototxt format but are functionally equal). GCC four.8.5, MKLML version 2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe, Inference & practicing measured with “caffe time” command.  For “ConvNet” topologies, dummy dataset was used. For different topologies, records changed into st ored on local storage and cached in reminiscence before working towards  BVLC Caffe (http://github.com/BVLC/caffe), revision 91b09280f5233cafc62954c98ce8bc4c204e7475 (commit date 5/14/2017). BLAS: atlas ver. three.10.1.

    2Intel editorial with the aid of Brian Krzanich, chief executive officer of Intel enterprise, “asserting trade’s First Neural community Processor,” October 17, 2017.

    Intel, the Intel logo, Xeon, and Nervana are emblems of Intel company or its subsidiaries within the U.S. and/or different nations.

    Intel applied sciences’ points and merits faith on gadget configuration and may require enabled hardware, software or carrier activation. performance varies depending on system configuration. check with your outfit brand or retailer or subsist trained more at www.intel.com.


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    Overview:

    The Exam MA0–100, titled as Intel Security Certified Product Specialist Exam, forms a fraction of the Intel Security Certification Program. Passing this test implies that the candidates is skillful in the disburse and implementation of McAfee ePolicy Orchestrator and McAfee Virus Scan. The MA0–100 PDF will consist of 115 questions, and the time allowed will subsist 165 minutes. The candidates will beget to attain a score of 72% or above if they are to pass the exam and pocket the MA0–100 Certification.

    It is recommended that the candidates gain at least one year sustain in deploying McAfee ePolicy Orchestrator prior to registering for the specialist exam. If the candidates are short of time, then they may avail the option of the ePO administration training, or faith on the MA0–100 Study Guides and dumps available to online for in-depth practice.

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    Installation and Configuration of Server:

    The candidates must subsist chummy with the installation procedure (including encryption, protocols, and defaults), UI navigation, organizing browser; and configuration of user accounts, permission sets, ePO repositories, and automatic responses.

    Managing and Addressing the Issues in the Server:

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    Intel Corporation introduces the Xeon Processor E3-1200 v6 product family. (Credit: Intel Corporation)

    Today, Intel announced the availability of the Intel® Xeon® processor E3-1200 v6 product family, which includes eight modern versions that provide key improvements in processor speed, enhanced reminiscence capabilities, hardware-enhanced security and reliability features with advocate for the latest Intel graphics.

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    The Intel Xeon processor E3-1200 v6 product family delivers up to 56%1 performance improvement compared with the Intel Xeon processor E3-1200 v2, which is widely used by petite businesses in entry-level workstations today. The Intel Xeon processor E3-1280 v6 supports up to 3.90 GHz ground frequency with Intel Turbo Boost Technology 2.0 frequency up to 4.20 GHz. Additionally, the Intel Xeon processor E3-1200 v6 product family supports enhanced reminiscence speeds, with configurations of robust Error-Correcting Code (ECC) reminiscence up to 64 GB DDR at 2400 MHz.

    In addition to enhanced processor performance and advocate for rapid memory, the latest Intel® HD graphics P630 is available on select products within the Intel Xeon processor E3-1200 v6 product family. The value and performance of Intel HD graphics P630 is reflected in an up to 3X1 graphics performance improvement when compared with an Intel Xeon processor E3-1280 v2.

    The Intel Xeon Processor E3-1200 v6 product family is shipping today and available for purchase from worldwide OEM and channel providers.  More information can subsist organize at www.intel.com/xeone3.

    » Download complete images (ZIP, 3 MB)

    1Intel® Xeon® Processor E3–1275 v6 compared to Intel® Xeon® Processor E3–1275 v2

  • Up to 56% performance improvement based on on SPECfp*_rate_base2006
  • Up to 3X graphics performance improvement based on SPECViewPerf*12 GeoMean
  • SPECfp*_rate_2006:

    Baseline: Intel® C216 chipset based Intel® Xeon® workstation platform with one Intel Xeon processor E3-1275 v2 (quad-core, 3.5GHz, 8M cache), ACRVMBY1.86C.0096.P00 September 9, 2012, Intel HT Technology enabled, 16GB reminiscence (4x4GB DDR3-1600 ECC UDIMM), 2TB 7200RPM SATAIII HDD (WD2000FYYZ), RHEL v6.3 – 2.6.32-278.e16.x86_64. Compiler version 13.0.0.133 of Intel C++ Studio XE and Intel Fortran. Source: Intel internal estimated measurements April2013. Scores:  SPECfp*_base2006=65.1;  SPECint*_base2006=52.5;  SPECfp*_rate_base2006=130;  SPECint*_rate_base2006=185

    New: Intel® Xeon® Processor E3-1275 v6 (8M Cache, 3.80 GHz) on Intel® Server Board S1200SPL, CentOS 7.1.1503 (Core) Kernel 3.10.0-229.14.1.el7.x86_64 Compiler: C/C++: Version 16.0 of Intel C++ Studio XE for Linux Intel SSD DC S3710 800GB BIOS: S1200SP.86B.02.01.0037.101720160816; CPU Power and Performance Policy: Performance;  reminiscence Operating Speed: 2400;  Set Fan Profile: Performance SPECfp*_rate_base2006=203; 

    SPECViewperf* 12 GeoMean:

    Baseline: Processor Intel® Xeon® Processor E3-1275 v2 System Carlow Reference Platform GPU Intel® HD graphics P4000 Sockets 1 reminiscence 16GB (4x4GB DDR3-1600MHz ECC UDIMM) Storage  Western Digital WD2000FYYZ HDD Operating System  Windows 7 SP1 64 Bit  BIOS ACRVMBY1.86C  Graphics Driver 8.15.10.2712

    Scores: catia-04 7.8 creo-01 6.62 energy-01 0.06 maya-04 2.56 medical-01 1.29 showcase-01 4.09 snx-02  2.64 sw-03 12.89 

    New: Intel® Xeon® Processor E3-1275 v6 (8M Cache, 3.80 GHz) on Intel® Server Board S1200SPL, Windows 10 Professional 64-bit, 1x Intel® SSD 535 Series  (480GB, 2.5in SATA 6Gb/s, 16nm, MLC), Graphics Driver: 21.20.16.4550; OS Resolution: 1920×1080, BIOS: S1200SP.86B.02.01.0037.101720160816;  Primary parade : parade Port;  VGA Port Output: Processor graphis; CPU Power and Performance Policy: Performance;  reminiscence Operating Speed: 2400;  Set Fan Profile: Performance, SPECviewperf* 12          SPECviewperf v12 Geomean (R)      7.53, SPECviewperf – Catia Catia v12 (R)15.77, SPECviewperf – CreoCreo v12 (R)13.52, SPECviewperf – Energy SPECWPC 2.0 – Energy (R)0.28, SPECviewperf – Maya Maya v12 (R) 14.14,SPECviewperf – Medical Medical v12 (R)   4.28, SPECviewperf – Showcase       Showcase v12 (R) 7.91, SPECviewperf – SNX SNX v12 (R) 11.9, SPECviewperf – SW SW v12 (R) 30.58


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