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000-111 IBM Distributed Systems Storage Solutions Version 7

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000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7

Test Code : 000-111
Test designation : IBM Distributed Systems Storage Solutions Version 7
Vendor designation : IBM
: 269 existent Questions

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IBM IBM Distributed Systems Storage

power techniques: using greater revenue Than at the start notion | killexams.com existent Questions and Pass4sure dumps

February 25, 2019 Timothy Prickett Morgan

Any model takes refinement, no matter if it is some thing a human spreadsheet jockey places collectively or it's a disbursed neural community it really is knowledgeable with laptop discovering recommendations to carry out some kind of identification and manipulation of information. So it is with the power techniques profits model I set aside collectively a month ago in the wake of IBM reporting its monetary effects for the fourth quarter.

I didn't in fact hint to net into it at the time. i was just going to collect a short table of the equable forex growth costs of the energy programs company and i simply kept going lower back in time and wondering what this data in fact supposed. equable forex multiply prices are entertaining for month-to-month and 12 months-to-12 months comparisons for a company that does commerce in lots of currencies around the globe, nevertheless it doesn’t basically counsel you the measurement of the energy programs company. As a refresher, here is what that boom chart for energy systems seems like:

So I went lower back in time and took my top-quality stab, according to assistance from the analysts at Gartner and IDC, on reckoning what the quarterly revenues for punch techniques were in 2009, and i converted the consistent forex boom costs that IBM supplies each quarter with the as-said figures, which might exist mentioned in varied currencies and converted to U.S. bucks at the conclusion of every quarter in accordance with the relative (and infrequently fluctuating) values of these currencies in opposition t the U.S. greenback.

I made what turned into an attractive first rate mannequin from this. however after getting some feedback and additionally giving it slightly more concept, I came to the conclusion that the introductory revenue mannequin became a slight brief on the external sales – which means folks that are reported as external income through IBM when it's talking to the Securities and exchange commission – in a yoke of distinct and significant ways, some of which are less complicated to guesstimate than others.

the primary means it changed into modest is barely that it became simply too low for the exterior sales. no longer a whole lot, however a substantial volume that requires the model to exist adjusted for 2018 and backcast the entire means returned to 2009. My initial mannequin reckoned that external vigour systems sales (again, significance those now not bought to other IBM divisions however these bought to conclusion clients and channel companions) in 2018 came to a tad bit more than $1.6 billion, however I reckon now that it is greater enjoy $1.78 billion. That may furthermore no longer sound enjoy a trustworthy deal, however it is an eleven p.c disagreement in the model, and i pride myself on being inside 5 percent or less in most issues. but this is very tough to carry out within the absence of information, and totality i can interpret is that I believe it is more correct now in line with remarks and unique statistics.

however that isn't totality the punch programs earnings that IBM does, and the image is more advanced, and this week I want to are trying to tackle some of that complexity to existing a more accurate photograph. apart from these external revenue of power programs gear to channel partners and users, IBM furthermore “sells” energy methods machinery to the Storage techniques unit that is a component of systems group as the foundation of a considerable number of storage arrays, just enjoy the DS8800 chain disk/flash hybrid arrays, and software-defined storage enjoy Spectrum Scale (GPFS) and Lustre parallel file methods as well as a number of object, key/price, and screen storage engines. lower back in the day, IBM used to provide tips about how a much deal of its as-mentioned revenues came from servers, storage, and chip manufacturing, however it not does this. It does talk about multiply in storage hardware, so that you can dart forward from the historic facts to the brand unique and purchase a behold at to determine how an Awful lot vigour programs iron, and its cost, is underpinning quite a lot of IBM storage. it is complicated to affirm with any precision, but the power systems component of storage looks to exist somewhere north of $200 million in 2018 – my wager is $226 million, up 15 p.c from 2017 degrees and considerably higher nonetheless than stages in 2016. In any experience, if you add that storage a partake of the vigour methods enterprise in – which IBM doesn't elude itself – then the energy methods division likely brought in whatever north of $2 billion in revenues in 2018.

here is what the chart showing exterior energy rig servers and internal storage-related power programs revenues parade to exist collectively:

these storage-related vigour systems earnings are enjoy icing on the cake, as you could see, ranging somewhere between eight % and 13 p.c of total vigour techniques earnings (with simply these two gadgets, which is not the comprehensive picture).

here's what this facts feels enjoy if you annualize it and consolidate these power systems earnings:

That offers you a stronger thought of the slope of the earnings bars. And in case you enjoy factual statistics, perquisite here is the table of the information at the back of that:

in case you wish to definitely comprehensive the photo on vigour programs hardware earnings, there's a different factor that must exist added in: Strategic outsourcing contracts involving energy techniques machinery. There are some very significant agencies that beget very gigantic compute complexes in line with power iron, and in a lot of situations, they are a trustworthy deal larger aggregations of programs than even gadget z retail outlets have. and a lot of of those valued clientele beget IBM control these programs below an outsourcing condense during the world technology services enterprise. And when GTS buys iron to improve power rig for shoppers, here is no longer protected within the externally said figures. it is arduous to determine how an Awful lot power rig GTS consumes, and at what fee, but perquisite here’s what they are able to say. IBM could originate that rate anything it desired, any quarter that it desired, so there are doubtless practices in location to examine that apparatus that GTS buys at a trustworthy market value to sustain away from the appearance of impropriety. in case you seem at the annual revenues for systems neighborhood, which comprises energy techniques and gadget z servers, operating techniques for these machines, and storage, IBM bought a complete of $eight.85 billion in hardware and operating systems, with $814 million of that being to interior IBM organizations; I reckon that most of that went to GTS for outsourcing, and further that about half went for servers, 1 / 4 went for storage, and a quarter for operating programs. It is not difficult to imagine that a yoke of hundred million greenbacks in vigour systems iron turned into “bought” by way of GTS for outsourcing contracts ultimate year. So perhaps the “true” revenues for vigour systems hardware is greater enjoy $2.3 billion, and with might exist a quarter of the $1.sixty two billion in operating programs being on vigour iron (the other three quarters comes from very lofty priced utility on gadget z mainframes), the breakdown of the $2.sixty six billion or so in energy programs earnings may seem enjoy this:

this is a larger enterprise than many could beget anticipated, and it is ecocnomic and growing. It can exist worse. And it has been. And it is getting more suitable.

linked studies

Taking At Stab At Modeling The power methods business

power methods hold transforming into To finish Off 2018

programs A vivid Spot In mixed consequences For IBM

The Frustration Of now not figuring out How we're Doing

vigor programs Posts growth in the First Quarter

IBM’s systems community On The fiscal Rebound

large Blue gains, Poised For The Power9

The punch Neine Conundrum

IBM Commits To Power9 improvements For huge vigour programs stores


Storage and AI labor collectively in IBM’s multicloud approach | killexams.com existent Questions and Pass4sure dumps

an incredible focal point of the announcements from IBM Corp.’s feel convention final week worried artificial intelligence and making it attainable across totality cloud platforms. This “AI far and wide” strategy applies to IBM’s storage method as smartly.

In December, IBM announced a storage rig co-designed with Nvidia Corp. for AI workloads and a considerable number of facts equipment, equivalent to TensorFlow. AI reference structure is additionally integrated in IBM’s punch line of servers.

there's curiously a further fundamental AI integration within the works, as IBM continues to hub of attention on the hybrid cloud. “We’re working on a third one at the jiffy with a further main server dealer as a result of they desire their storage to exist any site there’s AI and any site there’s a cloud — big, medium or small,” said Eric Herzog (pictured), chief advertising officer and vp of international storage channels at IBM.

Herzog spoke with John Furrier (@furrier) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s cellular livestreaming studio, totality over the IBM speculate event in San Francisco. They discussed IBM’s focal point on cyber resilience in its storage items and meeting customer wants in a multicloud ambiance. (* Disclosure below.)

New facets for resiliency

apart from multicloud and AI, IBM’s storage operation has additionally been focused on cyber resilience. In August, the commerce launched Cyber Incident recovery among the points included within the newest unlock of its Resiliency Orchestration platform.

the brand unique product was designed to abruptly recuperate information and functions following a cyberattack. “sure, every person is used to the ‘super wall of China’ preserving you, and then of route chasing the unhealthy guy down when they infringement you,” Herzog spoke of. “but once they infringement you, it would certain exist exceptional if every thing had records at leisure encryption.”

Enhancements to IBM’s storage portfolio over the past 12 months had been designed to accommodate client environments which are increasingly multicloud-oriented. The hub of attention has been on software-defined storage solutions that stream and protect counsel in a wide purview of compute ecosystems, as Herzog wrote in a recent weblog submit.

“You may furthermore beget NTT Cloud in Japan, you might furthermore beget Alibaba in China, you may furthermore beget IBM Cloud Australia, and then you may beget Amazon in Latin the usa,” said Herzog, who seemed on the convention wearing a symbolic Hawaiian surfer shirt. “You don’t battle the wave; you taste the wave. And that’s what totality and sundry is coping with.”

Watch the comprehensive video interview under, and exist positive to purchase a behold at more of SiliconANGLE’s and theCUBE’s insurance of the IBM feel experience. (* Disclosure: IBM Corp. backed this phase of theCUBE. Neither IBM nor other sponsors beget editorial control over content on theCUBE or SiliconANGLE.)

picture: SiliconANGLE considering you’re perquisite here …

… We’d enjoy to counsel you about their mission and the way you could back us fulfill it. SiliconANGLE Media Inc.’s commerce model is based on the intrinsic value of the content, now not advertising. in contrast to many on-line publications, they don’t beget a paywall or dash banner promoting, because they wish to preserve their journalism open, devoid of impress or the exigency to chase traffic.The journalism, reporting and commentary on SiliconANGLE — together with are living, unscripted video from their Silicon Valley studio and globe-trotting video groups at theCUBE — purchase lots of complicated work, time and funds. conserving the high-quality extravagant requires the assist of sponsors who are aligned with their imaginative and prescient of ad-free journalism content.

in case you enjoy the reporting, video interviews and different ad-free content material here, please purchase a jiffy to check out a pattern of the video content supported via their sponsors, tweet your help, and retain coming lower back to SiliconANGLE.


IBM Mashes Up PowerAI And Watson laptop discovering Stacks | killexams.com existent Questions and Pass4sure dumps

previous in this decade, when the hyperscalers and the teachers that dash with them were constructing laptop researching frameworks to transpose totality types of statistics from one format to yet another – speech to text, text to speech, image to textual content, video to text, etc – they had been doing so now not only for scientific curiosity. They beget been trying to resolve actual company problems and addressing the wants of consumers the usage of their application.

on the very time, IBM became trying to clear up a special difficulty, naming developing a question-answer system that could anthropomorphize the search engine. This exertion became referred to as venture Blue J internal of IBM (not to exist puzzled with the open source BlueJ built-in progress atmosphere for Java), turned into wrapped up perquisite into a utility stack known as DeepQA by means of IBM. It was this DeepQA stack, which changed into in keeping with the open source Hadoop unstructured facts storage and analytics engine that came out of Yahoo and yet another challenge referred to as Apache UIMA, which predates Hadoop through a number of years and which changed into designed by using IBM database specialists within the early 2000s to technique unstructured records enjoy textual content, audio, and video. This abysmal QA stack turned into embedded within the Watson QA device that changed into designed to play Jeopardy towards people, which they famed in detail here eight years ago. The Apache UIMA stack became the key a partake of the WatsonQA gadget that did natural language processing that parsed out the speech in a Jeopardy reply, transformed it to text, and fed it into the statistical algorithms to create the Jeopardy question.

Watson gained the competition towards human Jeopardy champs Brad Rutter and Ken Jennings, and a brand – which invoked IBM founder Thomas Watson and his admonition to “think” in addition to medical professional Watson, the sidekick of fictional supersleuth Sherlock Holmes – became born.

rather than originate Watson a product on the market, IBM offered it as a provider, and pumped the QA device complete of records to purchase on the healthcare, economic services, energy, advertising and media, and education industries. This turned into, most likely, a mistake, however on the time, within the wake of the Jeopardy championship, it felt enjoy every thing was touching to the cloud and that the SaaS mannequin became the commandeer manner to go. IBM in no way truly talked in astonishing aspect about how DeepQA become constructed, and it has in a similar way no longer been particular about how this Watson stack has modified over time – eight years is a very long time within the laptop gaining knowledge of space.  It is not clear if Watson is material to IBM’s revenues, but what is obvious is that desktop researching is strategic for its methods, utility, and services organizations.

So it truly is why IBM is at ultimate bringing together totality of its desktop getting to know tools and inserting them beneath the Watson brand and, very importantly, making the Watson stack attainable for purchase so it can furthermore exist dash on private datacenters and in different public clouds anyway the one that IBM runs. To exist actual, the Watson capabilities as well because the PowerAI machine gaining knowledge of practising frameworks and adjunct tools tuned up to dash on clusters of IBM’s vigour systems machines, are being brought collectively, and they'll exist set aside into Kubernetes containers and distributed to dash on the IBM Cloud private Kubernetes stack, which is accessible on X86 systems as well as IBM’s own energy iron, in virtualized or naked metallic modes. It is that this encapsulation of this unique and comprehensive Watson stack with IBM Cloud private stack that makes it portable throughout inner most datacenters and other clouds.

by the way, as a partake of the mashup of those tools, the PowerAI stack that makes a speciality of abysmal getting to know, GPU-accelerated machine gaining knowledge of, and scaling and disbursed computing for AI, is being made a core partake of the Watson Studio and Watson computing device getting to know (Watson ML) utility equipment. This integrated utility suite gives commercial enterprise facts scientists an end-to-conclusion developer tools. Watson Studio is an built-in progress atmosphere according to Jupyter notebooks and R Studio. Watson ML is a collection of machine and abysmal studying libraries and mannequin and records administration. Watson OpenScale is AI mannequin monitoring and prejudice and equity detection. The software previously known as PowerAI and PowerAI enterprise will continue to exist developed by the Cognitive techniques division. The Watson division, in case you don't seem to exist customary with IBM’s organizational chart, is partake of its Cognitive solutions community, which comprises databases, analytics equipment, transaction processing middleware, and numerous functions allotted both on premises or as a provider on the IBM Cloud.

it's unclear how this Watson stack could trade in the wake of IBM closing the pink Hat acquisition, which should silent occur earlier than the conclusion of the 12 months. nonetheless it is competitively priced to await that IBM will tune up totality of this software to dash on pink Hat commercial enterprise Linux and its own KVM digital machines and OpenShift implementation of Kubernetes and then push definitely complicated.

it is likely profitable to evaluate what PowerAI is totality about after which display how it is being melded into the Watson stack. earlier than the combination and the identify changes (extra on that in a second), here is what the PowerAI stack looked like:

in accordance with Bob Picciano, senior vp of Cognitive systems at IBM, there are more than 600 commerce clients that beget deployed PowerAI tools to dash machine researching frameworks on its energy programs iron, and clearly GPU-accelerated systems enjoy the power AC922 rig that's on the heart of the “Summit” supercomputer at o.k.Ridge national Laboratory and the sibling “Sierra” supercomputer at Lawrence Livermore countrywide Laboratory are the main IBM machines individuals are using to carry out AI work. here's a trustworthy looking trustworthy birth for a nascent industry and a platform that is relatively unique to the AI crowd, but most likely not so diverse for commercial enterprise shoppers which beget used punch iron in their database and software tiers for decades.

The introductory PowerAI code from two years ago started with models of the TensorFlow, Caffe, PyTorch, and Chainer laptop getting to know frameworks that massive Blue tuned up for its energy processors. The big innovation with PowerAI is what's known as colossal model help, which makes use of the coherency between Nvidia “Pascal” and “Volta” Tesla GPU accelerators and Power8 and Power9 processors in the IBM vigour methods servers – enabled via NVLink ports on the energy processors and tweaks to the Linux kernel – to allow tons greater neural network practicing fashions to exist loaded into the system. totality of the PowerAI code is open source and dispensed as code or binaries, and so far only on power processors. (We suspect IBM will dart agnostic on this eventually, considering the fact that Watson tools should dash on the big public clouds, which with the exception now of the IBM Cloud, won't beget energy methods accessible. (Nimbix, a professional in HPC and AI and a smaller public cloud, does present energy iron and helps PowerAI, by the way.)

underneath this, IBM has created a groundwork referred to as PowerAI business, and here's no longer open source and it is simply obtainable as partake of a subscription. PowerAI enterprise adds Message Passing Interface (MPI) extensions to the laptop getting to know frameworks – what IBM calls disbursed abysmal getting to know – as well as cluster virtualization and computerized hyper-parameter optimization options, embedded in its Spectrum Conductor for Spark (sure, that Spark, the in-memory processing framework) tool. IBM has furthermore added what it calls the abysmal getting to know influence module, which includes rig for managing records (such as ETL extraction and visualization of datasets) and managing neural community fashions, together with wizards that imply the way to most advantageous use records and models. On correct of this stack, IBM’s first industrial AI software that it's selling is referred to as PowerAI vision, which may furthermore exist used to label graphic and video statistics for practicing fashions and instantly instruct models (or augment present models supplied with the license).

So in spite of everything of the alterations, here's what the brand unique Watson stack feels like:

As you could see, the Watson desktop researching stack helps a lot more desktop discovering frameworks, above totality the SnapML framework that got here out of IBM’s analysis lab in Zurich this is providing a major efficiency capabilities on punch iron compared to working frameworks enjoy Google’s TensorFlow. here's surely a greater complete stack for computer discovering, including Watson Studio for developing fashions, the primary Watson computer getting to know stack for practicing and deploying fashions in creation inference, and now Watson OpenScale (it's mislabeled within the chart) to computer screen and aid enrich the accuracy of fashions based on how they are running in the box as they infer issues.

For the moment, there is not any trade in PowerAI commerce licenses and pricing totality over the first quarter, however after that PowerAI commercial enterprise may exist introduced into the Watson stack to add the distributed GPU laptop studying working towards and inference capabilities atop power iron to that stack. So Watson, which began out on Power7 machines taking partake in Jeopardy, is coming again domestic to Power9 with production machine discovering functions in the enterprise. They aren't obvious if IBM will present an identical allotted machine discovering capabilities on non-vigor machines, nevertheless it appears practicable that is valued clientele wish to dash the Watson stack on premises or in a public cloud, it will ought to. punch techniques will must stand on its own merits if that comes to move, and given the merits that Power9 chips beget with reference to compute, I/O and reminiscence bandwidth, and coherent reminiscence throughout CPUs and GPUs, that may additionally not exist as a much deal of an repercussion as they might suppose. The X86 architecture will must win by itself deserves, too.


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HPC in Life Sciences partake 1: CPU Choices, ascend of Data Lakes, Networking Challenges, and More | killexams.com existent questions and Pass4sure dumps

For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, beget convened to examine the condition of HPC (and now AI) use in life sciences.

Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s factual most life sciences research computing is less focused on tightly-coupled, low-latency processing (traditional HPC) and more relative on data analytics and managing (and sieving) massive datasets. But there is plenty of both types of compute and disentangling the two has become increasingly difficult. Sophisticated storage schemes beget long been de rigueur and recently enjoy a glisten networking has become primary (no surprise given lab instruments’ prodigious output). Lastly, striding into this shifting environment is AI – abysmal learning and machine learning – whose deafening hype is only exceeded by its transformative potential.

Ari Berman, BioTeam

This year’s discussion included Ari Berman, vice president and common manager of consulting services, Chris Dagdigian, one of BioTeam’s founders and senior director of infrastructure, and Aaron Gardner, director of technology. Including Dagdigian, who focuses largely on the enterprise, widened the scope of insights so there’s a nice blend of ideas presented about biotech and pharma as well as traditional academic and government HPC.

Because so much material was reviewed they are again dividing coverage into two articles. partake One, presented here, examines core infrastructure issues around processor choices, heterogeneous architecture, network bottlenecks (and solutions), and storage technology. partake Two, scheduled for next week, tackles the AI’s trajectory in life sciences and the increasing use of cloud computing in life sciences. In terms of the latter, you may exist confidential with NIH’s STRIDES (Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability) program which seeks to slice costs and ease cloud access for biomedical researchers.

Enjoy

HPCwire: Let’s tackle the core compute. ultimate year they touched potential ascend of processor diversity (AMD, Intel, Arm, Power9) and certainly AMD seems to beget near on strong. What’s your purchase on changes in core computing landscape?

Chris Dagdigian: I can exist quick and dirty. My view in the commercial and pharmaceutical and biotech space is that, aside from things enjoy GPUs and specialized computing devices, there’s not a lot of movement away from the mainstream processor platforms. These are people touching in 3-to-5-year purchasing cycles. These are people who standardized on Intel after a few years of twinge during the AMD/Intel wars and it would purchase something of huge significance to originate them shift again. In commercial biopharmaceutical and biotech there’s not a lot of racy stuff going on in the CPU set.

The only other thing that’s racy that’s happening is as more and more of this stuff goes to the cloud or gets virtualized, a lot of the CPU stuff actually gets hidden from the user. So there’s a growing partake of my community (biomedical researchers in enterprise) where the users don’t even know what CPU their code is running on. That’s particularly factual for things enjoy AWS batch, and AWS Lambda (serverless computing services) and that sort of stuff running in the cloud. I speculate I’ll desist here are interpret on the commercial side they are deliberate and conservative and it’s silent an Intel world and the cloud is hiding a lot of the factual CPU stuff particularly as people dart serverless.

Aaron Gardner: That’s an racy point. As more clouds beget adopted the Epyc CPU, some people may not realize they are running on them when they start instances. I would interpret furthermore that the ascend of informatics as a service and workflows as a service is going to abstract things even more. It’s relatively smooth today to dash most code with some flush of optimization across the Intel and AMD CPUs. But the gap widens a bit when you talk about, is the code, or portions of it being GPU accelerated, or did you switch architectures from AMD64 to Power9 or something enjoy that.

We talked ultimate year about a transition from compute clusters being a hub fed by large-spoke data systems towards a data cluster where the hub is the data lake with its various touching pieces and storage tiers, but the spokes are totality the different types of heterogeneous compute services that span and back the workload dash on that system. They definitely beget seen movement towards that model. If you behold at totality Cray’s announcements in the ultimate few months, everything from what they are doing with Shasta and Slingshot, and labor towards making the CS (cluster supercomputers) and XC (tightly coupled supercomputers) labor seamlessly, interoperably, in the very infrastructure, we’re seeing companies enjoy Cray and others gearing up for a heterogeneous future where they are going to back multiple processor architectures and optimize for multiple processor architectures as well as accelerators, CPUs and GPUs, and beget it totality labor together in a coherent whole. That’s actually very exciting, because it’s not about betting on one particular horse or another; it’s about how well you are going to integrate across architectures, both traditional and non-traditional.

Ari Berman: Circling back to what Chris said. Life sciences historically has been sort of deliberate to jump in and adopt unique stuff just to try it or to discern if it will exist three percent faster because the differences gained in knowledge generation at this point in life science for those three percent are not ground breaking – it’s fine to wait a slight while. Those days, however, are dwindling because of the amount of data being generated and the urgency with which it has to exist processed and furthermore the backlog of data that has to exist processed.

So they are not in life sciences at a point where – other than the differentiation of GPUs – applications are being designed specifically for different system processors other than for Intel. There’s some caveats to that. Normally as long as you can compile it and dash it on one of the main system processors and it can dash on a daily version of Linux, they are not optimizing for that; the exceptions to that are some of the built in math libraries that can exist taken handicap of on the Intel system platform, some of the data offloading for touching data to and from CPUs from remote or even internally, reminiscence bandwidth really matters a lot, and some of those things are differentiated based on what kind of research you are doing.

HPCwire: It sounds a slight enjoy the battle for mindshare and market partake among processor vendors doesn’t matter as much in life sciences, at least at the user level. Is that fair?

Ari Berman: Well, they really enjoy a lot of the future architectures. AMD is coming out with for better reminiscence bandwidth to exploit things enjoy PCIe links, having unique interconnects between CPUs, and furthermore the connection to the motherboard. One of the substantial bottlenecks Intel silent has to resolve is how carry out you net data to and from the machine from external sources. Internally they beget optimized the bandwidth a whole lot, but if you beget huge central sources of data from parallel file systems, you silent beget to net it in and out of that system, and there are bottlenecks there.

Aaron Gardner: With the Rome architecture touching forward, AMD has provided a much better approach to reminiscence access, touching away from NUMA (nonuniform memory) to a central reminiscence controller with uniform latency across dies. This is really primary when you beget up to 64 cores per socket. touching back towards a more auspicious reminiscence access model on a per node design flush I speculate is really going to aid provide advantages to workloads in the life sciences and that is certainly something they are looking at testing and exploring over the next year.

Ari Berman: I carry out speculate that for the first time in a while Power9 has some potential relevance, mostly because Summit and Sierra (IBM-based supercomputers) coming into play and those machines being built on Power9. I speculate people are exploring it but I don’t know that it will originate much of a play outside of just absolute HPC. The other thing I meant to bring up is a site where I speculate AMD is ahead of Intel in fab technology. AMD is already manufacturing at 7nm versus the 14nm. I thought that it was really innovative of AMD to carry out a multiple nanometer fabrication for their next release of processors where the IO core is 14nm and the processing core is 7nm because, just for power and distribution efficiency.

Aaron Gardner: In terms of market share, I speculate AMD has been extremely strategic over the ultimate 18 months because when you behold at places that got burned by AMD in the past when it exited the server market, there were not enough benefits to warrant jumping back in fully perquisite away. But AMD is really geared towards the economies-of-scale kind plays such as in the cloud where any handicap in efficiency is going to exist appreciated. So I speculate they beget been strategic [in choosing target markets] and we’ll discern over the next yoke of years how it plays out. I speculate they are at the jiffy not in a site where the client needs to specify a certain processor. They are going to discern the integrators influence here, what they pick to set aside together in their heterogeneous HPC systems portfolio, influence what CPUs people net and that may really outcome the winners and losers over time.

ARM they discern continue to grow but not explosively and I’d interpret Power is certainly interesting. Having the big Power systems at the top of the TOP500 has really validated Power9 for use in capability supercomputing. How those are used though versus the GPUs for target workloads is interesting. In common they may exist headed to a future where the CPU is used to gyrate on the GPU for certain workloads. Nvidia would probably favor that model. It’s just very racy the interplay between CPU and GPU; it really does beget to carry out with whether you are accelerating a tiny number of codes to the nth degree or you are trying to beget more diverse application back which is where multiple CPU and GPU architectures are going to exist needed.

Ari Berman: Using GPUs is silent a huge thing for lots of different reasons. At the jiffy GPUs are hyped for AI and ML, but they beget been used extensively for a lot of the simulation space, Schrodinger suite, molecular modeling, quantum chemistry, those sorts of things, and furthermore down into phylogenetic inference, special inheritance, things enjoy that. There are many much applications for graphic processors, but really I would disagree with others that it really boils down to system processors and GPUs at the jiffy in life sciences. I did hear anecdotally from a yoke of folks in the industry that were using the IBM Q cloud just to try quantum [computing], just to discern how it worked with really lofty flush genomic alignment and they kind of got it to labor and I’ll leave it at that.

HPCwire: They probably don’t devote enough coverage to networking given its jiffy driven by huge datasets and the ascend of edge computing. What’s the condition of networking in life sciences?

Chris Dagdigian: In pharmaceuticals and biotech, Ethernet rules the world. The lofty hurry low latency interconnects are silent in niche environments. When they carry out discern non-ethernet fabrics in the commercial world they are being used for parallel filesystems or in specialized HPC chemistry & molecular modeling application environments where MPI message passing latency actually matters. However I will bluntly interpret networking hurry is now the most critical issue in my HPC world. I feel that compute and storage at petascale are largely tractable problems. touching data at scale within an organization or outside the boundaries of your firewall to a collaborator or a cloud is the lone biggest rate limiting bottleneck for HPC in pharma and biotech. Combine with that the cost lofty hurry Ethernet has not gone down as enjoy a glisten as the cost of commoditization in storage and compute. So they are in this double whammy world where they desperately exigency enjoy a glisten networks.

The corporate networking people are fairly smug about the 10 gig and 40 gig links they beget in the datacenter core whereas they exigency 100 gig networking going outside the datacenter, 100 gig going outside the building, sometimes they exigency 100 gig links to a particular lab. Honestly the way that I exploit this in enterprise is I am helping research organizations become a champion for the networking groups; they traditionally are under budgeted and don’t typically beget 40 gig and 100 gig and 400 gig on their radar because you know they are looking at bandwidth graphs for their edge switches or their firewalls and they just don’t discern the insane data movement that they beget to carry out between the laboratory instrument and a storage system. The second thing, and I beget utterly failed at it, is articulating that there are products other than Cisco in the world. That controversy does not wing in enterprise because there is a tremendous installed base. So I am in the enmesh 22 of I pay a lot of money for Cisco 40 gig and 100 gig and I just beget to live with it.

Ari Berman: I would disagree networking is one of the major challenges. Depending on what granularity you are looking at, I speculate most of the HPCwire readers will custody a lot about interconnects on clusters. Starting there, I would interpret they are seeing a fairly even distribution of absolute Ethernet on the back quit because of vendors enjoy Arista for instance, which is producing more affordable 100 gig low latency Ethernet that can exist set aside on the back quit so you don’t beget to carry out the whole RDMA versus TCP/IP dance necessarily. But most clusters are silent using InfiniBand on their back end.

In life sciences I would interpret that they silent discern Mellanox predominantly as the back end. I beget not seen life-science-directed organizations [use] a whole lot of Omni-Path (OPA). I beget seen it at the NSF supercomputer centers, used to much effect, and they enjoy it a lot, but not really so much in life sciences. I’d interpret the hurry and diversity and the abilities of the Mellanox implementation could really outclass what is available in OPA today. I speculate the delays in OPA2 beget wound them. I carry out speculate the unique interconnects enjoy Shasta/Slingshot from Cray are paving the way to producing a reasonable competitor to where Mellanox is today.

Moving out from that, Chris is right. There are so many people using the cloud that don’t upgrade their internet connections to a wide enough bandwidth or purchase their security enough out of the way or optimize it enough so that people can effectively use the cloud for data-intensive applications, that getting the data there is impossible. You can use the cloud but only if the data is already there. That’s a huge problem.

Internally, a lot of organizations beget moved to tart spots of 100 gig to exist able to dart data effectively between datacenters and from external data sources but a lot of 10 gig silent predominates. I’d interpret that there is a lot of 25 gig implementations and 50 gig implementations now. 40 gig sort went by the wayside. That’s because of the 100 gig optical carriers where they are actually made up of four individual wavelinks and so what they did was to just smash those out and so the form factors beget shrunk.

Going back to the cluster back end. In life sciences the judgement lofty performance networking on the back quit of a cluster is really primary isn’t necessarily for inter-process communications, it’s for storage delivery to nodes. Almost every implementation has a big parallel distributed file system where totality of the data are coming from at one point or another. You beget to net them to the CPU and that backend network needs to exist optimized for that traffic.

Aaron Gardner: That’s a common case in the life sciences. They primarily behold at storage performance to bring data to nodes and even to dart between nodes versus message passing for parallel applications. That’s starting to shift a slight bit but that’s traditionally been how it is. They usually beget looked at a lone lofty performance fabric talking to a parallel files system. Whereas HPC as a whole has for a long time dealt with having a enjoy a glisten fabric for internode communications for big scale parallel jobs and then having a storage fabric that was either brought to totality of the nodes or to a certain extent shunted into the other fabric using IO router nodes.

“One of the things that is very racy with Cray announcing Slingshot is the talent to converse both an internal low latency HPC optimized protocol as well as Ethernet, which in the case of HPC storage removes the exigency for IO router nodes, instead allowing the HCA (host channel adapters) and switching to exploit the load and protocol translation and totality of that. Depending on how transparent and smooth it is to implement Slingshot at the tiny and mid-scale I speculate that is a potential threat to the continued prevalence of traditional InfiniBand in HPC, which is essentially Mellanox today.”

HPCwire: We’ve talked for a number of years about the revolution in life sciences instruments, and how the gush of data pouring from them overwhelms research IT systems. That has set aside stress on storage and data management. What’s you sense of the storage challenge today?

Chris Dagdigian: My sense is storing vast amounts of data is not particularly challenging these days. There’s a lot of products on the market, very many vendors to pick from, and the actual act of storing the data is relatively straightforward. However, no one has centrally cracked the how they manage it, how carry out they understand what we’ve got on disk, how carry out they carefully curate and maintain that stuff. Overwhelmingly the preeminent storage pattern in my world is if they are not using a parallel files system for hurry it’s overwhelmingly scale-out network attached storage (NAS). But they are definitely in the era where some of the incumbent NAS vendors are starting to exist seen as dinosaurs or being placed on a 3-year or 4-year upgrade cycle.

The other thing is there’s silent a lot of interest in hybrid storage, storage that spans the cloud and can exist replicated into the cloud. The technology is there but in many cases the pipes are not. So it is silent relatively difficult to either synchronize or replicate and maintain a consistent storage namespace unless you are a really solid organization with really enjoy a glisten pipes to the outside world. They silent discern the problems of lots of islands of storage. The only other thing I will interpret is I am known for maxim the future of scientific data at comfort belongs in an remonstrate store, but that it’s going to purchase a long time to net there because they beget so many dependencies on things that await to discern files and folders. I beget customers that are buying petabytes of network attached storage but at the very time they are furthermore buying petabytes of remonstrate storage. In some cases they are using the remonstrate storage natively; in other cases the remonstrate storage is their data continuity or backup target.

In terms of file system preference, the commercial world is not only conservative but furthermore incredibly concerned with admin cross and value so almost universally it is going to exist a mainstream altenative enjoy GPFSsupported by DDN or IBM. There are lots of really racy alternatives enjoy BeeGFS but the issue really is the enterprise is nervous about fancy unique technologies, not because of the fancy unique technologies but because they beget to bring unique people in to carry out the custody and feeding.

Aaron Gardner: Some of the challenges with how they discern storage deployed across life science organizations is how nearby to the bottom beget they been driven. With traditional supercomputing, you’re trying to net the fastest storage you can, and the most of it, for the least amount of money. The back needed is not the primary driver. In HPC as a whole, Lustre and GPFS/Spectrum Scale are silent the predominate players in terms of parallel file system. The racy stuff over the ultimate year or so has been Lustre trading hands (from Intel to DDN). With DDN leading the charge, the ecosystem is silent being kept open and I speculate carefully crafted so other vendors can provide solutions independently from DDN. They carry out discern IBM stepping up Spectrum Scale performance and Spectrum Scale 5offering a lot of trustworthy features proven out and demonstrated on the climax and Sierra kind systems, making Spectrum Scale every bit as apposite as it ever was.

As far as performant parallel file systems there are racy alternatives. There is more presence and momentum behind BeeGFS than they beget seen in prior years. They discern some adoption and clients interested in trying and adopting it but the number deployments in production and at a big scale is silent pretty limited.

These days remonstrate storage is seen more enjoy a tap that you gyrate on and you are getting your remonstrate storage through AWS or Azure or GCP. If you are buying it for on-premise, there’s slight differentiation seen between remonstrate vendors. That’s the perception at least. They are seeing interest in what they call next generation storage systems and file systems – things like WekaIO that provide NVMe over fabrics (NVMeOF) on the front quit and export their own NVMeOF endemic file system as opposed to screen storage. This removes the exigency to use something enjoy Spectrum Scale or Lustre to provide the file system and can drain artic data to remonstrate storage either on premise or in the cloud. They carry out discern that as a viable model touching forward.

I would add interpret that speaking to NVME over fabrics in general; that it seems to exist growing and becoming established as most of the unique storage vendors coming on the scene are currently architecting that way. That’s trustworthy in their book. They certainly discern performance advantages but it really matters how it’s done—it is primary that the software stack driving the NVME media has been purpose built for NVME over fabrics or at least significantly redesigned. Something ground up enjoy WekaIO or VAST will discharge very well. On the other hand you could pick NVME over fabrics as the hardware topology for a storage system, but if you then layer on a legacy file system that hasn’t been updated for it you might not discern much benefit.

Couple of other quick notes. It seems enjoy storage benchmarking in HPC has been receiving more attention both in terms of measuring throughput and metadata operations, with the latter being valued and seen as one of the primary bottlenecks that govern the absolute utility of a cluster. For projects enjoy the IO500 we’ve seen an uptick in participation, both from national labs as well as vendors and other organizations. The ultimate thing worth mentioning is data management. Scraping data for ML training data sets, for example, is one of the things driving us to understand the data they store better than they beget in the past. One of the simple ways to carry out that is to tag your data and they are seeing more files systems coming on the scene with a focus on tagging as a core in-built feature. So while they near at the problem from different angles you could behold at what companies like Atavium is doing for primary storage or Igneous for secondary storage, providing the talent to tag data on ingest and the talent to dart data (policy-driven) according to tags. This is something that they beget talked about for a long time and beget helped a lot of clients tackle.”

Link to partake Two (HPC in Life Sciences partake 2: Penetrating AI’s Hype and the Cloud’s Haze)


Asavie IoT Connect Service Now Available on AWS Marketplace to Expedite Enterprise IoT Projects | killexams.com existent questions and Pass4sure dumps

Asavie, a leader in secure Enterprise Mobility and Internet of Things (IoT) Connectivity,announced today that Asavie IoT Connect is now available on Amazon Web Services (AWS) Marketplace. The on-demand secure, network connectivity service enables developers to deploy IoT projects in minutes. By combining the flexibility and achieve of AWS with Asavie IoT Connect’s seamless edge-to-Cloud secure cellular network management, businesses can quickly deploy and scale their IoT projects in a trusted end-to-end environment.

Asavie IoT Connect is an on-demand, secure connectivity service designed to connect IoT edge devices to the AWS cloud. Developers can provision their IoT devices in minutes with a seamless and secure private cellular connectivity to transmit data to the Amazon Virtual Private Cloud (Amazon VPC). Asavie IoT Connect enables a completely private network, extending from edge IoT devices to AWS, that shields devices from public Internet borne cyberthreats such as malware and Distributed Denial of Service (DDoS) attacks.

The availability of such an on-demand seamless secure connection from the edge device to the cloud facilitates enterprise adoption of IoT by removing some of the complexity and skills required to manage the lifecycle of an IoT deployment. As observed by Emil Berthelsen, Snr. Director & Analyst with Gartner, “Moving deeper into IoT solutions and architectures, however, will require unique skills around connectivity, integration, cloud and possibly analytics. On the one hand, connecting and integrating IoT endpoints, platforms and enterprise systems will exist critical to ensure the secure flux of data from the edge to the platform. At another level, providing suitable processing and storage capabilities, and enabling the use of future cloud-based services, will require skills from the cloud service area.” [i ]

Garth Fort, Director, AWS Marketplace, Amazon Web Services, Inc. said, “IoT is top of intellect for many of their customers in multiple sectors. We’re continuing to originate it easier for customers to innovate and meet their growing IoT commerce needs and we’re delighted to welcome Asavie IoT Connect on AWS Marketplace to aid customers quickly and securely deploy IoT solutions.”

Brendan Carroll, CEO with industrial IoT sensor manufacturer, EpiSensor said, “Our global customers confidence on the calibre of their products to continually monitor and provide insights on their industrial processes, 24/7. In gyrate they confidence on their suppliers Asavie and AWS to provide the resilient, secure connectivity and storage services to enable us to fulfill their exacting service flush agreements across the globe.”

“The ease with which the Asavie IoT Connect service allows us seamlessly connect individual devices to the AWS cloud infrastructure allows us to scale device-based deployments anywhere in the world,” added Carroll.

Asavie CEO, Ralph Shaw said, “As an AWS IoT Competency Partner, Asavie has already demonstrated apposite technical proficiency and proven customer success, delivering solutions seamlessly on AWS. Today’s announcement builds on this foundation and expands their distribution capabilities to the enterprise market. With Asavie and AWS, enterprises can now confidently implement their IoT dart to market strategies across multiple territories.”

“By simplifying the secure integration of data from edge IoT devices to the cloud, Asavie empowers global businesses to drive increased cost savings, reduce risk and expedite their IoT implementations,” continued Shaw.

Visit Asavieat MWC onbooth7F30.

About Asavie

Asavie makes secure connectivity simple for any size of mobility or IoT deployment in a hyper-connected world. Asavie’s on-demand services power the secure and smart distribution of data to connected devices anywhere. They enable enterprise customers globally to harness the power of the internet of things and mobile devices to transform and scale their businesses. Strategic distribution and technology partners include AT&T, AWS, Dell, IBM, Microsoft, Singtel, Telefonica, Verizon and Vodafone. Asavie is an ISO 27001 certified company. For more information visit: www.asavie.com and result @Asavie on Twitter.

[i] Gartner: 2017 Strategic Roadmap for Successful Enterprise IoT Journeys - 29 November 2017 – Author Emil Berthelsen

View source version on businesswire.com: https://www.businesswire.com/news/home/20190224005118/en/

SOURCE: Asavie"> <Property FormalName="PrimaryTwitterHandle" Value="@Asavie

For AsavieHugh Carroll, Asavie, + 353 1 676 3585/+353 087 136 9869 hugh.carroll@asavie.comAnne Marie McCallion, ReturnPR +353 86 8349329 annemarie@returnpr.com

Copyright commerce Wire 2019


Blockchain May exist Overkill for Most IIoT Security | killexams.com existent questions and Pass4sure dumps

Blockchain crops up in many of the pitches for security software aimed at the industrial IoT. However, IIoT project owners, chipmakers and OEMs should stick with security options that address the low-level, device- and data-centered security of the IIoT itself, rather than the exertion to promote blockchain as a security option as well as an audit tool.

Only about 6% of Industrial IoT (IIoT) project owners chose to build IIoT-specific security into their initial rollouts, while 44% said it would exist too expensive, according to a 2018 survey commissioned by digital security provider Gemalto.

Currently, only 48% of IoT project owners can discern their devices well enough to know if there has been a breach, according to the 2019 version of Gemalto’s annual survey.

Software packages that could fill in the gaps were few and far between. This is largely because securing devices aimed at industrial functions requires more memory, storage or update capability than typical IIoT/IoT devices currently have. That makes it difficult to apply security software to networks with IIoT hardware, according to Steve Hanna, senior principal at Infineon Technologies, who co-wrote an endpoint-security best-practices lead published by the Industrial Internet consortium in 2018.

Still, the recognition is widespread that security is a problem with connected devices. Spending on IIoT/IoT-specific security will grow 25.1% per year, from $1.7 billion during 2018, to $5.2 billion by 2023, according to a 2018 market analysis report from BCC Research. Another study, by Juniper Research, predicts 300% growth by 2023, to just over $6 billion.

Since 2017, a group of companies including Cisco, Bosch, Gemalto, IBM and others beget promoted blockchain as a way to create a tamper-proof provenance for everything from chips to whole devices. By creating an auditable history, where each unique event or change in status has to exist verified by 51% of the members of the group participating in a particular ledger, it should exist practicable to track an individual component from point of sale to the original manufacturer to verify whether it’s been tampered with.

Blockchain furthermore can exist used to track and verify sensor data, obviate duplication or the insertion of malicious data and provide ongoing verification of the identity of individual devices, according to an analysis from IBM, which promotes the use of blockchain in both technical and fiscal functions.

Use of blockchain in securing IIoT/IoT assets among those polled in Gemalto’s latest survey rose to 19%, up from 9% in 2017. And 23% of respondents said they believe blockchain is an model solution to secure IIoT/IoT assets.

Any security may exist better than none, but some of the more current options don’t translate well into actual IIoT-specific security, according to Michael Chen, design for security director at Mentor, a Siemens Business.

“You beget to behold at it carefully, know what you’re trying to accomplish and what the security flush is,” Chen said. “Public blockchain is much for things enjoy the stock exchange or buying a home, because on a public blockchain with 50,000 people if you wanted to cheat you’d beget to net more than 50% to cooperate. Securing IIoT devices, even across a supply chain, is going to exist a lot smaller group, which wouldn’t exist much reassurance that something was accurate. And meanwhile, we’re silent trying to motif out how to carry out root of confidence and key management and a lot of other things that are a different and more of an immediate challenge.”

Others agree. “Using blockchain to track the current location and condition of an IoT device is probably not a trustworthy use of the technology,” according to Michael Shebanow, vice president of R&D for Tensilica at Cadence. “Public ledgers are a means of securely recording information in a distributed manner. Unless there is a defined exigency to record location/state in that manner, then using blockchain is a very high-overhead means of doing so. In general, applications probably don’t exigency that flush of authenticity check.”

Limitations of blockchainsEven the most robust public blockchain efforts are often less efficient than the solutions they replace. But more importantly, they don’t originate a process more secure by removing the exigency for trust, argues security guru Bruce Schneier, CTO of IBM Resilient.

Blockchain reduces the amount of confidence they beget to set aside in humans and requires that they confidence computers, networks and applications that may exist lone points of failure. By contrast, a human-driven legal system has many potential points of failure and recovery. One can originate the other more efficient, but there’s no judgement to assume that simply shifting confidence to machines, regardless of context or property of execution, will originate anything better, Schneier wrote.

Public-ledger verification methods can exist applied to many aspects of identity and supply chain for IIoT/IoT networks, according to a 2018 report from Boston Consulting Group. Only 25% of the applications BCG identified had completed the proof-of-concept phase, however, and problems such as faked or plagiarized approvals identified in cryptocurrency cases, a want of standards, performance issues and regulatory dubiety totality raised doubts about its usefulness as a way to manage basic security and authentication this early in the maturity of both the IIoT and blockchain.

“When they beget blockchain worked out for supply chain, we’ll probably beget the means to apply it to chips and IoT, but it probably doesn’t labor the other way,” Chen said.

The overhead required for blockchain verifications of location or status data for thousands of devices is off-putting, and it’s much easier to identify hardware using a public/private key—especially if the private key is secured by a number identified in a physically unclonable function, Shebanow agreed. “Barring a lab attack, PUF via hardware implementation makes it nearly impossible to spoof an ID, whereas software is never 100% secure. It is virtually impossible to prove that a intricate software system has no back door.”

The bottom line: Stick with root of trust, secure boot and build from there, until there’s an efficient blockchain template for IoT.

Related StoriesBlockchain: Hype, Reality, OpportunitiesTechnology investments and rollouts are accelerating, but there is silent plenty of room for innovation and improvement.IoT Device Security Makes deliberate ProgressWhile attention is being paid to security in IoT devices, silent more must exist done.Are Devices Getting More Secure?Manufacturers are paying more attention to security, but it’s not clear whether that’s enough.Why The IIoT Is Not SecureDon’t blame the technology. This is a people problem.



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Autodesk [2 Certification Exam(s) ]
Avaya [96 Certification Exam(s) ]
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mySQL [4 Certification Exam(s) ]
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Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]





References :


Dropmark : http://killexams.dropmark.com/367904/11587765
Wordpress : http://wp.me/p7SJ6L-Vv
Issu : https://issuu.com/trutrainers/docs/000-111
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weSRCH : https://www.wesrch.com/business/prpdfBU1HWO000CHAC
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publitas.com : https://view.publitas.com/trutrainers-inc/pass4sure-000-111-practice-tests-with-real-questions
Box.net : https://app.box.com/s/y8s0ia8x4a2sjctkyluk9f3zxq0es81w
zoho.com : https://docs.zoho.com/file/5ptno29d914de95544b28810055264631e8ab






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