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000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7
Test Code : 000-111
Test cognomen : IBM Distributed Systems Storage Solutions Version 7
Vendor cognomen : IBM
: 269 existent Questions
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IBM IBM Distributed Systems Storage
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 sequel some kind of identification and manipulation of information. So it is with the power techniques profits model I Put collectively a month ago in the wake of IBM reporting its monetary effects for the fourth quarter.
I didn't in fact suggest to rep into it at the time. i was just going to collect a short table of the even 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. even forex extend 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 relate 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 dash techniques were in 2009, and i converted the consistent forex boom costs that IBM supplies each quarter with the as-said figures, which might breathe 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 preliminary revenue mannequin became a miniature 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 brace of distinct and significant ways, some of which are less complicated to guesstimate than others.
the primary means it changed into coy is barely that it became simply too low for the exterior sales. no longer a whole lot, however a titanic volume that requires the model to breathe adjusted for 2018 and backcast the entire means returned to 2009. My initial mannequin reckoned that external vigour systems sales (again, sense 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 fondness $1.78 billion. That may besides no longer sound fondness a profitable 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 sequel within the absence of information, and whole i can advise is that I believe it is more redress now in line with remarks and unusual statistics.
however that isn't whole the dash 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 besides “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 fondness the DS8800 sequence disk/flash hybrid arrays, and software-defined storage fondness Spectrum Scale (GPFS) and Lustre parallel file methods as well as a number of object, key/price, and obscure storage engines. lower back in the day, IBM used to provide tips about how a remarkable deal of its as-mentioned revenues came from servers, storage, and chip manufacturing, however it not does this. It does talk about extend in storage hardware, so that you can Move forward from the historic facts to the brand unusual and retract a contemplate at to determine how an terrible lot vigour programs iron, and its cost, is underpinning quite a lot of IBM storage. it is complicated to aver with any precision, but the power systems factor of storage looks to breathe somewhere north of $200 million in 2018 – my ante 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 fragment of the vigour methods enterprise in – which IBM doesn't eschew 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 outfit servers and internal storage-related power programs revenues loom to breathe collectively:
these storage-related vigour systems earnings are fondness 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 fondness 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 fondness dependable statistics, right 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 breathe added in: Strategic outsourcing contracts involving energy techniques machinery. There are some very significant agencies that hold very gigantic compute complexes in line with power iron, and in a lot of situations, they are a profitable deal larger aggregations of programs than even gadget z retail outlets have. and a lot of of those valued clientele hold IBM control these programs below an outsourcing constrict during the world technology services enterprise. And when GTS buys iron to improve power outfit for shoppers, here is no longer protected within the externally said figures. it is difficult to determine how an terrible lot power outfit GTS consumes, and at what fee, but right here’s what they are able to say. IBM could obtain 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 profitable market value to maintain away from the appearance of impropriety. in case you appear 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 brace of hundred million greenbacks in vigour systems iron turned into “bought” by course of GTS for outsourcing contracts final year. So perhaps the “true” revenues for vigour systems hardware is greater fondness $2.3 billion, and with might breathe 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 appear fondness this:
this is a larger enterprise than many could hold anticipated, and it is ecocnomic and growing. It can breathe worse. And it has been. And it is getting more suitable.
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 monetary Rebound
large Blue gains, Poised For The Power9
The dash Neine Conundrum
IBM Commits To Power9 improvements For huge vigour programs stores
an incredible focal point of the announcements from IBM Corp.’s feel convention final week worried synthetic intelligence and making it attainable across whole cloud platforms. This “AI far and wide” strategy applies to IBM’s storage manner as smartly.
In December, IBM announced a storage outfit 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 dash line of servers.
there's curiously a further fundamental AI integration within the works, as IBM continues to center of attention on the hybrid cloud. “We’re working on a third one at the minute with a further main server dealer as a result of they wish their storage to breathe any set there’s AI and any set 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, whole over the IBM deem 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 unusual 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 breach you,” Herzog spoke of. “but once they breach you, it would inescapable breathe 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 center of attention has been on software-defined storage solutions that stream and protect counsel in a wide compass of compute ecosystems, as Herzog wrote in a recent weblog submit.
“You may besides hold NTT Cloud in Japan, you might besides hold Alibaba in China, you may besides hold IBM Cloud Australia, and then you may hold 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 experience the wave. And that’s what whole and sundry is coping with.”
Watch the comprehensive video interview under, and breathe certain to retract a contemplate at more of SiliconANGLE’s and theCUBE’s insurance of the IBM feel experience. (* Disclosure: IBM Corp. backed this aspect of theCUBE. Neither IBM nor other sponsors hold editorial control over content on theCUBE or SiliconANGLE.)
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… We’d fondness to relate you about their mission and the course you could advocate 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 hold a paywall or sprint banner promoting, because they wish to preserve their journalism open, devoid of affect or the requisite 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 — retract lots of complicated work, time and funds. conserving the high-quality exorbitant requires the assist of sponsors who are aligned with their imaginative and prescient of ad-free journalism content.
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previous in this decade, when the hyperscalers and the teachers that sprint with them were constructing laptop researching frameworks to transpose whole 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 hold been trying to resolve actual company problems and addressing the wants of consumers the usage of their application.
on the same time, IBM became trying to pellucid up a special difficulty, naming developing a question-answer system that could anthropomorphize the search engine. This pains became referred to as venture Blue J internal of IBM (not to breathe puzzled with the open source BlueJ built-in progress atmosphere for Java), turned into wrapped up right 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 fondness textual content, audio, and video. This deep QA stack turned into embedded within the Watson QA device that changed into designed to play Jeopardy towards people, which they eminent in detail here eight years ago. The Apache UIMA stack became the key a fragment 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 obtain Watson a product on the market, IBM offered it as a provider, and pumped the QA device complete of records to retract 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 fondness every thing was stirring to the cloud and that the SaaS mannequin became the appropriate manner to go. IBM in no course truly talked in wonderful aspect about how DeepQA become constructed, and it has in a similar course 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 pellucid 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 final bringing together whole 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 besides breathe sprint on private datacenters and in different public clouds anyway the one that IBM runs. To breathe actual, the Watson capabilities as well because the PowerAI machine gaining knowledge of practising frameworks and adjunct tools tuned up to sprint on clusters of IBM’s vigour systems machines, are being brought collectively, and they'll breathe Put into Kubernetes containers and distributed to sprint 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 unusual 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 fragment of the mashup of those tools, the PowerAI stack that makes a speciality of deep getting to know, GPU-accelerated machine gaining knowledge of, and scaling and disbursed computing for AI, is being made a core fragment 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 deep 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 breathe developed by the Cognitive techniques division. The Watson division, in case you don't appear to breathe habitual with IBM’s organizational chart, is fragment 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 expect that IBM will tune up whole of this software to sprint on pink Hat commercial enterprise Linux and its own KVM digital machines and OpenShift implementation of Kubernetes and then shove definitely complicated.
it is likely beneficial to evaluate what PowerAI is whole about after which array 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 hold deployed PowerAI tools to sprint machine researching frameworks on its energy programs iron, and clearly GPU-accelerated systems fondness the power AC922 outfit 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 sequel AI work. here's a profitable looking profitable birth for a nascent industry and a platform that is relatively unusual to the AI crowd, but most likely not so diverse for commercial enterprise shoppers which hold used dash iron in their database and software tiers for decades.
The preliminary 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 large innovation with PowerAI is what's known as colossal model help, which makes employ 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 breathe loaded into the system. whole of the PowerAI code is open source and dispensed as code or binaries, and so far only on power processors. (We suspect IBM will fade agnostic on this eventually, considering the fact that Watson tools should sprint on the large public clouds, which with the exception now of the IBM Cloud, won't hold 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 fragment of a subscription. PowerAI enterprise adds Message Passing Interface (MPI) extensions to the laptop getting to know frameworks – what IBM calls disbursed deep 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 besides added what it calls the deep getting to know influence module, which includes outfit for managing records (such as ETL extraction and visualization of datasets) and managing neural community fashions, together with wizards that imply the course to most advantageous employ records and models. On redress of this stack, IBM’s first industrial AI software that it's selling is referred to as PowerAI vision, which may besides breathe used to label vivid 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 unusual Watson stack feels like:
As you could see, the Watson desktop researching stack helps a lot more desktop discovering frameworks, above whole the SnapML framework that got here out of IBM’s analysis lab in Zurich this is providing a major efficiency capabilities on dash iron compared to working frameworks fondness Google’s TensorFlow. here's surely a greater complete stack for computer discovering, including Watson Studio for developing fashions, the considerable 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 wait on 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 whole over the first quarter, however after that PowerAI commercial enterprise may breathe 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 fragment in Jeopardy, is coming again domestic to Power9 with production machine discovering functions in the enterprise. They aren't certain if IBM will present an identical allotted machine discovering capabilities on non-vigor machines, nevertheless it appears workable that is valued clientele wish to sprint the Watson stack on premises or in a public cloud, it will ought to. dash techniques will must stand on its own merits if that comes to move, and given the merits that Power9 chips hold with reference to compute, I/O and memory bandwidth, and coherent reminiscence throughout CPUs and GPUs, that may additionally not breathe as a remarkable deal of an impact as they might suppose. The X86 architecture will must win by itself deserves, too.
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For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, hold convened to examine the state of HPC (and now AI) employ in life sciences.
Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s dependable 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 hold long been de rigueur and recently rapid networking has become considerable (no astound given lab instruments’ prodigious output). Lastly, striding into this shifting environment is AI – deep 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 generic 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. fragment One, presented here, examines core infrastructure issues around processor choices, heterogeneous architecture, network bottlenecks (and solutions), and storage technology. fragment Two, scheduled for next week, tackles the AI’s trajectory in life sciences and the increasing employ of cloud computing in life sciences. In terms of the latter, you may breathe close with NIH’s STRIDES (Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability) program which seeks to prick costs and ease cloud access for biomedical researchers.
HPCwire: Let’s tackle the core compute. final year they touched potential surge of processor diversity (AMD, Intel, Arm, Power9) and certainly AMD seems to hold arrive on strong. What’s your retract on changes in core computing landscape?
Chris Dagdigian: I can breathe quick and dirty. My view in the commercial and pharmaceutical and biotech space is that, aside from things fondness GPUs and specialized computing devices, there’s not a lot of movement away from the mainstream processor platforms. These are people stirring in 3-to-5-year purchasing cycles. These are people who standardized on Intel after a few years of smart during the AMD/Intel wars and it would retract something of huge significance to obtain them shift again. In commercial biopharmaceutical and biotech there’s not a lot of spicy stuff going on in the CPU set.
The only other thing that’s spicy 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 fragment of my community (biomedical researchers in enterprise) where the users don’t even know what CPU their code is running on. That’s particularly dependable for things fondness AWS batch, and AWS Lambda (serverless computing services) and that sort of stuff running in the cloud. I deem I’ll desist here are advise on the commercial side they are leisurely and conservative and it’s silent an Intel world and the cloud is hiding a lot of the dependable CPU stuff particularly as people fade serverless.
Aaron Gardner: That’s an spicy point. As more clouds hold adopted the Epyc CPU, some people may not realize they are running on them when they start instances. I would advise besides that the surge of informatics as a service and workflows as a service is going to abstract things even more. It’s relatively light today to sprint most code with some flat 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 fondness that.
We talked final 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 stirring pieces and storage tiers, but the spokes are whole the different types of heterogeneous compute services that span and advocate the workload sprint on that system. They definitely hold seen movement towards that model. If you contemplate at whole Cray’s announcements in the final few months, everything from what they are doing with Shasta and Slingshot, and travail towards making the CS (cluster supercomputers) and XC (tightly coupled supercomputers) travail seamlessly, interoperably, in the same infrastructure, we’re seeing companies fondness Cray and others gearing up for a heterogeneous future where they are going to advocate multiple processor architectures and optimize for multiple processor architectures as well as accelerators, CPUs and GPUs, and hold it whole travail 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 leisurely to jump in and adopt unusual stuff just to try it or to observe if it will breathe 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 miniature while. Those days, however, are dwindling because of the amount of data being generated and the urgency with which it has to breathe processed and besides the backlog of data that has to breathe 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 sprint it on one of the main system processors and it can sprint on a customary version of Linux, they are not optimizing for that; the exceptions to that are some of the built in math libraries that can breathe taken advantage of on the Intel system platform, some of the data offloading for stirring data to and from CPUs from remote or even internally, memory bandwidth really matters a lot, and some of those things are differentiated based on what benevolent of research you are doing.
HPCwire: It sounds a miniature fondness the battle for mindshare and market share 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 fondness a lot of the future architectures. AMD is coming out with for better memory bandwidth to manipulate things fondness PCIe links, having unusual interconnects between CPUs, and besides the connection to the motherboard. One of the titanic bottlenecks Intel silent has to resolve is how sequel you rep data to and from the machine from external sources. Internally they hold optimized the bandwidth a whole lot, but if you hold huge central sources of data from parallel file systems, you silent hold to rep it in and out of that system, and there are bottlenecks there.
Aaron Gardner: With the Rome architecture stirring forward, AMD has provided a much better approach to memory access, stirring away from NUMA (nonuniform memory) to a central memory controller with uniform latency across dies. This is really considerable when you hold up to 64 cores per socket. stirring back towards a more auspicious memory access model on a per node design flat I deem is really going to wait on 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 sequel deem 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 deem people are exploring it but I don’t know that it will obtain much of a play outside of just absolute HPC. The other thing I meant to bring up is a set where I deem 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 sequel 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 deem AMD has been extremely strategic over the final 18 months because when you contemplate 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 right away. But AMD is really geared towards the economies-of-scale kind plays such as in the cloud where any advantage in efficiency is going to breathe appreciated. So I deem they hold been strategic [in choosing target markets] and we’ll observe over the next brace of years how it plays out. I deem they are at the minute not in a set where the client needs to specify a inescapable processor. They are going to observe the integrators influence here, what they choose to Put together in their heterogeneous HPC systems portfolio, influence what CPUs people rep and that may really sequel the winners and losers over time.
ARM they observe continue to grow but not explosively and I’d advise Power is certainly interesting. Having the large Power systems at the top of the TOP500 has really validated Power9 for employ in capability supercomputing. How those are used though versus the GPUs for target workloads is interesting. In generic they may breathe headed to a future where the CPU is used to whirl on the GPU for inescapable workloads. Nvidia would probably favor that model. It’s just very spicy the interplay between CPU and GPU; it really does hold to sequel with whether you are accelerating a miniature number of codes to the nth degree or you are trying to hold more diverse application advocate which is where multiple CPU and GPU architectures are going to breathe needed.
Ari Berman: Using GPUs is silent a huge thing for lots of different reasons. At the minute GPUs are hyped for AI and ML, but they hold been used extensively for a lot of the simulation space, Schrodinger suite, molecular modeling, quantum chemistry, those sorts of things, and besides down into phylogenetic inference, special inheritance, things fondness that. There are many remarkable applications for vivid processors, but really I would conform with others that it really boils down to system processors and GPUs at the minute in life sciences. I did hear anecdotally from a brace of folks in the industry that were using the IBM Q cloud just to try quantum [computing], just to observe how it worked with really lofty flat genomic alignment and they benevolent of got it to travail and I’ll leave it at that.
HPCwire: They probably don’t pledge enough coverage to networking given its weight driven by huge datasets and the surge of edge computing. What’s the state 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 sequel observe 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 advise networking hurry is now the most critical issue in my HPC world. I feel that compute and storage at petascale are largely tractable problems. stirring data at scale within an organization or outside the boundaries of your firewall to a collaborator or a cloud is the unique biggest rate limiting bottleneck for HPC in pharma and biotech. Combine with that the cost lofty hurry Ethernet has not gone down as rapid as the cost of commoditization in storage and compute. So they are in this double whammy world where they desperately requisite rapid networks.
The corporate networking people are fairly smug about the 10 gig and 40 gig links they hold in the datacenter core whereas they requisite 100 gig networking going outside the datacenter, 100 gig going outside the building, sometimes they requisite 100 gig links to a particular lab. Honestly the course that I manipulate 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 hold 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 observe the insane data movement that they hold to sequel between the laboratory instrument and a storage system. The second thing, and I hold utterly failed at it, is articulating that there are products other than Cisco in the world. That dispute does not soar 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 hold to live with it.
Ari Berman: I would conform networking is one of the major challenges. Depending on what granularity you are looking at, I deem most of the HPCwire readers will keeping a lot about interconnects on clusters. Starting there, I would advise they are seeing a fairly even distribution of absolute Ethernet on the back halt because of vendors fondness Arista for instance, which is producing more affordable 100 gig low latency Ethernet that can breathe Put on the back halt so you don’t hold to sequel the whole RDMA versus TCP/IP dance necessarily. But most clusters are silent using InfiniBand on their back end.
In life sciences I would advise that they silent observe Mellanox predominantly as the back end. I hold not seen life-science-directed organizations [use] a whole lot of Omni-Path (OPA). I hold seen it at the NSF supercomputer centers, used to remarkable effect, and they fondness it a lot, but not really so much in life sciences. I’d advise the hurry and diversity and the abilities of the Mellanox implementation could really outclass what is available in OPA today. I deem the delays in OPA2 hold harm them. I sequel deem the unusual interconnects fondness Shasta/Slingshot from Cray are paving the course 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 retract their security enough out of the course or optimize it enough so that people can effectively employ the cloud for data-intensive applications, that getting the data there is impossible. You can employ the cloud but only if the data is already there. That’s a huge problem.
Internally, a lot of organizations hold moved to red spots of 100 gig to breathe able to Move data effectively between datacenters and from external data sources but a lot of 10 gig silent predominates. I’d advise 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 rupture those out and so the figure factors hold shrunk.
Going back to the cluster back end. In life sciences the intuition lofty performance networking on the back halt of a cluster is really considerable isn’t necessarily for inter-process communications, it’s for storage delivery to nodes. Almost every implementation has a large parallel distributed file system where whole of the data are coming from at one point or another. You hold to rep them to the CPU and that backend network needs to breathe optimized for that traffic.
Aaron Gardner: That’s a common case in the life sciences. They primarily contemplate at storage performance to bring data to nodes and even to Move between nodes versus message passing for parallel applications. That’s starting to shift a miniature bit but that’s traditionally been how it is. They usually hold looked at a unique lofty performance fabric talking to a parallel files system. Whereas HPC as a whole has for a long time dealt with having a rapid fabric for internode communications for large scale parallel jobs and then having a storage fabric that was either brought to whole of the nodes or by some means shunted into the other fabric using IO router nodes.
“One of the things that is very spicy with Cray announcing Slingshot is the ability to converse both an internal low latency HPC optimized protocol as well as Ethernet, which in the case of HPC storage removes the requisite for IO router nodes, instead allowing the HCA (host channel adapters) and switching to manipulate the load and protocol translation and whole of that. Depending on how transparent and light it is to implement Slingshot at the miniature and mid-scale I deem 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 Put 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 choose from, and the actual act of storing the data is relatively straightforward. However, no one has centrally cracked the how they manage it, how sequel they understand what we’ve got on disk, how sequel they carefully curate and maintain that stuff. Overwhelmingly the predominant 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 breathe 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 breathe 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 rapid pipes to the outside world. They silent observe the problems of lots of islands of storage. The only other thing I will advise is I am known for epigram the future of scientific data at rest belongs in an demur store, but that it’s going to retract a long time to rep there because they hold so many dependencies on things that expect to observe files and folders. I hold customers that are buying petabytes of network attached storage but at the same time they are besides buying petabytes of demur storage. In some cases they are using the demur storage natively; in other cases the demur storage is their data continuity or backup target.
In terms of file system preference, the commercial world is not only conservative but besides incredibly concerned with admin tribulation and value so almost universally it is going to breathe a mainstream selection fondness GPFSsupported by DDN or IBM. There are lots of really spicy alternatives fondness BeeGFS but the issue really is the enterprise is nervous about fancy unusual technologies, not because of the fancy unusual technologies but because they hold to bring unusual people in to sequel the keeping and feeding.
Aaron Gardner: Some of the challenges with how they observe storage deployed across life science organizations is how nigh to the bottom hold they been driven. With traditional supercomputing, you’re trying to rep the fastest storage you can, and the most of it, for the least amount of money. The advocate 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 spicy stuff over the final 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 deem carefully crafted so other vendors can provide solutions independently from DDN. They sequel observe IBM stepping up Spectrum Scale performance and Spectrum Scale 5offering a lot of profitable features proven out and demonstrated on the zenith and Sierra kind systems, making Spectrum Scale every bit as apropos as it ever was.
As far as performant parallel file systems there are spicy alternatives. There is more presence and momentum behind BeeGFS than they hold seen in prior years. They observe some adoption and clients interested in trying and adopting it but the number deployments in production and at a large scale is silent pretty limited.
These days demur storage is seen more fondness a tap that you whirl on and you are getting your demur storage through AWS or Azure or GCP. If you are buying it for on-premise, there’s miniature differentiation seen between demur vendors. That’s the perception at least. They are seeing interest in what they convoke next generation storage systems and file systems – things like WekaIO that provide NVMe over fabrics (NVMeOF) on the front halt and export their own NVMeOF endemic file system as opposed to obscure storage. This removes the requisite to employ something fondness Spectrum Scale or Lustre to provide the file system and can drain unruffled data to demur storage either on premise or in the cloud. They sequel observe that as a viable model stirring forward.
I would add advise that speaking to NVME over fabrics in general; that it seems to breathe growing and becoming established as most of the unusual storage vendors coming on the scene are currently architecting that way. That’s profitable in their book. They certainly observe performance advantages but it really matters how it’s done—it is considerable that the software stack driving the NVME media has been purpose built for NVME over fabrics or at least significantly redesigned. Something ground up fondness WekaIO or VAST will effect very well. On the other hand you could choose 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 observe much benefit.
Couple of other quick notes. It seems fondness 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 fondness the IO500 we’ve seen an uptick in participation, both from national labs as well as vendors and other organizations. The final 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 hold in the past. One of the simple ways to sequel 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 arrive at the problem from different angles you could contemplate at what companies like Atavium is doing for primary storage or Igneous for secondary storage, providing the ability to tag data on ingest and the ability to Move data (policy-driven) according to tags. This is something that they hold talked about for a long time and hold helped a lot of clients tackle.”
Link to fragment Two (HPC in Life Sciences fragment 2: Penetrating AI’s Hype and the Cloud’s Haze)
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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 unusual skills around connectivity, integration, cloud and possibly analytics. On the one hand, connecting and integrating IoT endpoints, platforms and enterprise systems will breathe critical to ensure the secure stream of data from the edge to the platform. At another level, providing suitable processing and storage capabilities, and enabling the employ 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 intuition for many of their customers in multiple sectors. We’re continuing to obtain 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 wait on customers quickly and securely deploy IoT solutions.”
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[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/
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For AsavieHugh Carroll, Asavie, + 353 1 676 3585/+353 087 136 9869 firstname.lastname@example.orgAnne Marie McCallion, ReturnPR +353 86 8349329 email@example.com
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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 pains 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 breathe too expensive, according to a 2018 survey commissioned by digital security provider Gemalto.
Currently, only 48% of IoT project owners can observe 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 pilot 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 hold promoted blockchain as a course to create a tamper-proof provenance for everything from chips to whole devices. By creating an auditable history, where each unusual event or change in status has to breathe verified by 51% of the members of the group participating in a particular ledger, it should breathe workable to track an individual component from point of sale to the original manufacturer to verify whether it’s been tampered with.
Blockchain besides can breathe used to track and verify sensor data, prevent 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 employ of blockchain in both technical and monetary 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 exemplar solution to secure IIoT/IoT assets.
Any security may breathe better than none, but some of the more favorite options don’t translate well into actual IIoT-specific security, according to Michael Chen, design for security director at Mentor, a Siemens Business.
“You hold to contemplate at it carefully, know what you’re trying to accomplish and what the security flat is,” Chen said. “Public blockchain is remarkable for things fondness the stock exchange or buying a home, because on a public blockchain with 50,000 people if you wanted to cheat you’d hold to rep more than 50% to cooperate. Securing IIoT devices, even across a supply chain, is going to breathe a lot smaller group, which wouldn’t breathe much reassurance that something was accurate. And meanwhile, we’re silent trying to motif out how to sequel root of reliance 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 state of an IoT device is probably not a profitable employ 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 requisite 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 requisite that flat 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 obtain a process more secure by removing the requisite for trust, argues security guru Bruce Schneier, CTO of IBM Resilient.
Blockchain reduces the amount of reliance they hold to Put in humans and requires that they reliance computers, networks and applications that may breathe unique points of failure. By contrast, a human-driven legal system has many potential points of failure and recovery. One can obtain the other more efficient, but there’s no intuition to assume that simply shifting reliance to machines, regardless of context or property of execution, will obtain anything better, Schneier wrote.
Public-ledger verification methods can breathe 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 lack of standards, performance issues and regulatory uncertainty whole raised doubts about its usefulness as a course to manage basic security and authentication this early in the maturity of both the IIoT and blockchain.
“When they hold blockchain worked out for supply chain, we’ll probably hold the means to apply it to chips and IoT, but it probably doesn’t travail 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 knotty 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 latitude for innovation and improvement.IoT Device Security Makes leisurely ProgressWhile attention is being paid to security in IoT devices, silent more must breathe done.Are Devices Getting More Secure?Manufacturers are paying more attention to security, but it’s not pellucid whether that’s enough.Why The IIoT Is Not SecureDon’t guilt the technology. This is a people problem.