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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 designation : Intel(R) Server Specialist Certification
Vendor designation : Intel
: 186 real 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 real Questions and Pass4sure dumps

Editor’s exist 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. observe 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 outright 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 exist cellular market, Qualcomm must emerge as a client manufacturer from the obscurity of being a constituent 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 need 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 protest of strengthening Qualcomm’s manufacturer. After acting on the Charlie Rose exhibit closing week, Jacobs will recrudesce to keynote CES 2013. Jacob’s theme, “Born cellular,” is conjectural to construct brand cognizance for Qualcomm’s silicon chips. he is presumably hoping for the identical client branding results in customer smartphone buy selections that the Intel internal propel delivered for Intel in purchaser computing device purchase decisions.

Intel, in the meantime, will consume CES to open 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 diminish to 22 nm and enhancement of the Atom Medfield chip at the minute in production in an endeavor to bring as an Awful lot as twice the performance with extended battery lifestyles. The Bay trail-T is a suitable Wintel microprocessor that will elude 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 Have been re-coded and recompiled.

In February at the cellular World Congress in Barcelona, Intel is anticipated to supplant its buyers and companions on its Merrifield processor due to exist launched later in 2013. Merrifield, first introduced in might besides 2012 at Intel’s investor conference, is focused at smartphones, a diminish 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 exist answerable 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 conflict – the second entrance being the competition to build the greatest and built-in systems-on-a-Chip. SoCs are such an gargantuan constituent in making a selection on the skill of a cellular machine that main the cell rig 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 Have an influence on this market, because they've microprocessor technology and both personal or Have licensed LTE/4G baseband expertise.

LTE/4G is delivered as a silicon fragment 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 need 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 inheritance 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 well-liked 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 Have the patent portfolio and scale to coordinate LTE/4G pile through separate certifications and approvals required by using every cellular provider.

In mid-2012 Intel doubled down on LTE/4G when it opened a current workplace in San Diego, Qualcomm’s domestic territory, with the specific intent of recruiting from the area’s abysmal pool of baseband talent to construct a brand current modem design group for LTE/4G applied sciences. There are rumors that Intel could exist making as a minimum another LTE/4G investment in 2013. A 2d supply for LTE/4G baseband modems from Intel could exist welcomed with the aid of smartphone producers as an alternative selection 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 Have yet to advertise 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 artery of several market hobbies. They may additionally survey 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 exist able to allow manufacturers to supply comprehensive optimized smartphones the usage of Intel’s components.

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

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

How a noteworthy deal of Intel’s growth within the cell SoC market could exist at Qualcomm’s cost is questionable on account of Qualcomm’s potent market station and captious design skill. Consolidation will besides exist expected as cell processor SoC suppliers with out powerful LTE/4G patent portfolios proceed away the mobile company as exemplified by artery of Texas devices’ concurrent retargeting of its OMAP mobile chipset to automotive infotainment functions. Qualcomm may besides arbitrator microscopic power from Intel while the trade phase consolidates. longer term here's an epic championship fight between two brilliant competitors, every with greater than $10 billion in cash that expend 15 to twenty percent of revenues on serious 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 outright of Intel’s “New company Initiative” ventures failed when the brand current 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 outright of the design, highbrow property, and manufacturing capability to succeed within the cell market, the current CEO will deserve to carve out Intel’s top talent and state-of-the-artwork fabrication skill and pledge it to successful mobile market share, possibly at some charge 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 exist chosen in line with his potential to steer Intel into the achieve up-notebook era. And he may exist capable of inform this chronicle – perhaps at CES 2014.


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

INTEL immediate TO immediate AI PORTFOLIO

Intel presents probably the most comprehensive and several portfolio of items for laptop and abysmal studying from the statistics middle to the edge. by artery of featuring compute substances for computer studying and abysmal 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, rig and aid are offered.

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

developers can leverage on Intel’s AI product portfolio to quicken 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 outright 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, outright introduced below their Intel Nervana AI Portfolio for synergy. It contains software rig and framework optimizations to deliver extremely noble efficiency for information-intensive workloads to enrich the Intel Xeon Scalable processor (which meets the computing device researching and abysmal researching wants of most organizations), Intel Xeon Phi processor, AI accelerators infatuation Intel FPGAs, and AI-really noble 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 abysmal 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 current 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 extreme ranges of parallelism.

iii) Intel FPGAs : FPGAs present essentially the most covenant for inference, as they will besides exist 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 role as programmable hardware accelerators for specialised abysmal 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 vitality the construction and training of abysmal getting to know models.

2. Intel Libraries

Intel has developed totally optimized math libraries that deserve the most useful consume 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 Have been incredibly optimized for single-core vectorization and cache memory utilization

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

Intel records Analytics Acceleration Library (Intel DAAL) : helps pace big records analytics by using offering totally optimized algorithmic pile blocks for outright statistics analysis degrees (Pre-processing, Transformation, evaluation, Modeling, Validation, and decision Making) for offline, streaming and disbursed analytics usages. It’s designed for consume 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 besides key to accelerating the pile process for analytics and HPC applications.

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

A handy technique to travail with abysmal getting to know is to consume the Intel’s optimized ML and DL frameworks. using Intel optimized tools and frameworks to educate and deploy abysmal networks guarantees that these rig will consume Intel structure in the gold benchmark way. Examples of how some of these frameworks Have been optimized to prefer advantage of Intel structure, as well as charts showing speed-up of these optimized frameworks compared to non-optimized ones, will besides exist establish in https://utility.intel.com/en-us/ai/deep-researching

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

4. Intel abysmal getting to know practising device

since mid-2017, Intel ambassadors were protecting workshops in a variety of universities and corporates round Kenya, as a fragment of the greater international ecosystem of the Intel student Developer program. significant to these workshops has been the practicing of the abysmal learning working towards tool 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 consume abysmal gaining information of, because of the relative complexity of customizing abysmal 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 abysmal discovering frameworks on general CPUs. This has been ground to lots of constraints in democratizing AI.

The reply to the complexity is Intel’s abysmal studying practicing tool. The working towards device is an internet application working on a Linux server and provides a person-friendly, intuitive interface for pile and practising abysmal studying models. The best fragment of the device is how handy it's to educate and set up abysmal gaining information of fashions for photograph and textual content analysis problems. testing it out with the Famous MNIST dataset, practicing become finished inside under 30 minutes, a job that would initially prefer greater than double the time. Intel’s abysmal gaining information 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 consume of compression strategies and optimization for deployment of the fashions on a considerable number of Intel hardware structures, together with these with restrained memory footprints and performance constraints.

It provides:

  • The means to visualize precise-time information — with out programming — throughout the introduction of abysmal neural networks
  • Simplified installing of abysmal learning frameworks which are optimized for Intel architecture
  • integrated Jupyter* Notebooks for superior model creation
  • The visible skill of developing, tuning, and running abysmal discovering algorithms with the model wizard
  • It is quiet at the minute 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 quicken up construction of AI options with free compute cloud entry powered via Intel Xeon Scalable processors. by proposing compute supplies for laptop researching and abysmal gaining information 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 exist 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 progress by the consume of fragment contraptions, it got Movidius, a chipmaker company main in the VPU (vision Processing Unit) platform for on-equipment vision processing. After their acquisition, they Have 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 abysmal getting to know device that you should consume to gain information 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 abysmal 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 artery 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 abysmal neural community inferences. The Neural Compute Engine at the side of the 16 powerful SHAVE cores and an extremely-excessive throughput knowing reminiscence fabric makes Myriad X the trade leader for on-device abysmal neural networks and computing device imaginative and prescient purposes.

    Movidius Myriad X 7. Intel Saffron

    Saffron know-how develops cognitive computing programs that consume incremental learning to maintain in mind and unify by entity. Saffron’s entertaining know-how ingests information from disparate sources and automatically connects the dots to assist agencies of every kindhearted improve 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, astute 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 rig designed to deserve massive records projects easier to implement. faucet contains a sequence of algorithms, tools and engines to dissect and collaborate with facts scientists. The software is open supply and includes integration with Intel structure and hardware. tap includes:

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

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

    · sheperd for cloud endemic apps.


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

    artificial Intelligence: machine studying, abysmal studying and more

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

    Processing abysmal learning Workloads in Healthcare

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

    That’s why for healthcare groups working AI workloads, Intel® Xeon® Scalable processors provide a noteworthy computational groundwork. Intel Xeon Scalable processors are optimized for AI, scale up promptly and seamlessly for two.1x quicker abysmal 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 execute deserve to educate models around the clock require a committed accelerator, however the majority of businesses conveniently need to instruct after which install a model. If an organization invests lots of bucks in constructing a committed acceleration circulation, it could actually’t execute the ease 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 focus infrastructure for AI applications and a wide array of other workloads.

    improved Utilization

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

    Technical advantages

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

    For these days’s AI networks deployed on a benchmark GPU, the photograph measurement for a scan that will besides exist developed is constantly 256 x 256 pixels. at the most, that image may well exist one thousand x one thousand pixels, but that’s pushing it. If clinicians Have a 4K picture the station they really want outright of those pixels to deserve an evaluation — as an instance, to become awake of a tumor in a tiny constituent of the photo — then they Have got an issue. after they scale that 4K image birthright down to 256 x 256 pixels, they lose outright of the quality temporal decision.

    The equal model can exist 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 besides 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 abysmal discovering

    relocating ahead, Intel has besides developed the Intel® Nervana™ Neural network Processor (NNP), the area’s first processor certainly designed from the floor up for abysmal 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 current courses of AI functions that maximize the quantity of facts processed and judgement superior insights. as an instance, AI will permit for previous prognosis and more advantageous accuracy, assisting deserve the not practicable viable by means of advancing research on cancer, Parkinson’s ailment and different brain disorders2.

    The Intel advantage for AI

    Intel has 50 years of adventure in assisting its shoppers deserve their records effective. today, Intel is the consume of that adventure to embolden healthcare agencies exploit every factor of their workflows through the a success implementation of AI.

    From hardware to utility and statistics science, Intel brings its complete suite of products and abilities to AI. Intel has the technical skills now not best to uphold companies build the arrogate infrastructure – from statistics to storage and network – but besides has the statistics scientists to recall and model information and the application builders to aid deserve the statistics positive. in short, Intel’s advantage 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 embolden pressure the artery forward for AI. we're additionally prompted by a want to deserve consume of their technology to embolden strengthen society and exploit the area’s massive challenges, so we’re certainly excited about their travail in healthcare. They need to embolden channel AI for societal noble – they convene it AI with a purpose.

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

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

    application and workloads used in performance assessments may Have 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 judgement 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 besides deserve these results inapplicable to your gadget or system. software and workloads used in efficiency checks may besides had been optimized for efficiency simplest on Intel microprocessors.  performance assessments, equivalent to SYSmark and MobileMark, are measured the consume of selected computer systems, add-ons, software, operations and services.  Any alternate to any of those elements could judgement the outcomes to alter.  exist 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 besides or may additionally now not optimize to the identical degree for non-Intel microprocessors for optimizations that aren't unique to Intel microprocessors. These optimizations embrace 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 consume with Intel microprocessors. unavoidable optimizations not selected to Intel microarchitecture are reserved for Intel microprocessors. delight consult with the applicable product user and Reference publications for extra tips regarding the unavoidable 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 , abysmal gaining information 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. abysmal gaining information 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 memory 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 Have been updated to consume 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 endemic 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 memory 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 , abysmal 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 artery 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. abysmal gaining information 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 memory 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 deserve consume 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 memory 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 rely on gadget configuration and may require enabled hardware, software or carrier activation. performance varies depending on system configuration. check with your rig brand or retailer or exist 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 fragment of the Intel Security Certification Program. Passing this test implies that the candidates is skillful in the consume and implementation of McAfee ePolicy Orchestrator and McAfee Virus Scan. The MA0–100 PDF will consist of 115 questions, and the time allowed will exist 165 minutes. The candidates will Have 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 smack 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 rely on the MA0–100 Study Guides and dumps available to online for in-depth practice.

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    Installation and Configuration of Server:

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    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 current versions that provide key improvements in processor speed, enhanced memory capabilities, hardware-enhanced security and reliability features with uphold 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 miniature businesses in entry-level workstations today. The Intel Xeon processor E3-1280 v6 supports up to 3.90 GHz groundwork 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 memory speeds, with configurations of robust Error-Correcting Code (ECC) memory up to 64 GB DDR at 2400 MHz.

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    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 exist establish at www.intel.com/xeone3.

    » Download outright 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 memory (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;  memory 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 memory 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 display : display Port;  VGA Port Output: Processor graphis; CPU Power and Performance Policy: Performance;  memory 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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