000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7
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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 conclude some type of identification and manipulation of information. So it is with the power techniques profits model I do collectively a month ago in the wake of IBM reporting its monetary effects for the fourth quarter.
I didn't in fact imply to bag 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 expand prices are entertaining for month-to-month and 12 months-to-12 months comparisons for a company that does trade in lots of currencies around the globe, nevertheless it doesn’t basically divulge 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 vitality techniques were in 2009, and i converted the consistent forex boom costs that IBM supplies each quarter with the as-said figures, which might be 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 diminutive 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 couple of discrete and significant ways, some of which are less complicated to guesstimate than others.
the primary means it changed into diffident is barely that it became simply too low for the exterior sales. no longer a whole lot, however a tremendous volume that requires the model to be 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 fancy $1.78 billion. That may moreover no longer sound fancy a edifying deal, however it is an eleven p.c incompatibility in the model, and i pride myself on being inside 5 percent or less in most issues. but this is very tough to conclude within the absence of information, and every lone i can articulate is that I believe it is more redress now in line with remarks and unique statistics.
however that isn't every lone the vitality 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 moreover “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 fancy the DS8800 chain disk/flash hybrid arrays, and software-defined storage fancy Spectrum Scale (GPFS) and Lustre parallel file methods as well as a number of object, key/price, and cache storage engines. lower back in the day, IBM used to provide tips about how a mighty deal of its as-mentioned revenues came from servers, storage, and chip manufacturing, however it not does this. It does talk about expand in storage hardware, so that you can slump forward from the historic facts to the brand unique and win a search for at to determine how an abominable lot vigour programs iron, and its cost, is underpinning quite a lot of IBM storage. it is complicated to assert with any precision, but the power systems factor of storage looks to be somewhere north of $200 million in 2018 – my pot 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 fraction 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 materiel servers and internal storage-related power programs revenues flaunt to be collectively:
these storage-related vigour systems earnings are fancy 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 fancy 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 fancy apt 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 be 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 edifying 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 compress during the world technology services enterprise. And when GTS buys iron to better power materiel for shoppers, here is no longer protected within the externally said figures. it is difficult to determine how an abominable lot power materiel GTS consumes, and at what fee, but perquisite here’s what they are able to say. IBM could invent 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 edifying market value to sustain away from the appearance of impropriety. in case you look 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 couple of hundred million greenbacks in vigour systems iron turned into “bought” by course of GTS for outsourcing contracts eventual year. So perhaps the “true” revenues for vigour systems hardware is greater fancy $2.3 billion, and with might be 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 look fancy this:
this is a larger enterprise than many could hold anticipated, and it is ecocnomic and growing. It can be 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 pecuniary Rebound
large Blue gains, Poised For The Power9
The vitality 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 every lone cloud platforms. This “AI far and wide” strategy applies to IBM’s storage manner as smartly.
In December, IBM announced a storage materiel 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 vitality 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 flash with a further main server dealer as a result of they desire their storage to be any status there’s AI and any status 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, every lone 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 trade 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 infraction you,” Herzog spoke of. “but once they infraction you, it would inevitable be 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 moreover hold NTT Cloud in Japan, you might moreover hold Alibaba in China, you may moreover 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 relish the wave. And that’s what every lone and sundry is coping with.”
Watch the comprehensive video interview under, and be positive to win a search for 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 hold editorial control over content on theCUBE or SiliconANGLE.)picture: SiliconANGLE considering you’re perquisite here …
… We’d fancy to divulge you about their mission and the course you could champion us fulfill it. SiliconANGLE Media Inc.’s trade 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 dash banner promoting, because they wish to preserve their journalism open, devoid of move or the need 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 — win lots of complicated work, time and funds. conserving the high-quality immoderate 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 dash with them were constructing laptop researching frameworks to transpose every lone 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 very time, IBM became trying to transparent up a special difficulty, naming developing a question-answer system that could anthropomorphize the search engine. This endeavor became referred to as venture Blue J internal of IBM (not to be puzzled with the open source BlueJ built-in development 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 fancy 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 noted in detail here eight years ago. The Apache UIMA stack became the key a fraction 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 invent Watson a product on the market, IBM offered it as a provider, and pumped the QA device complete of records to win 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 fancy 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 extraordinary 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 information of space. It is not transparent 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 eventual bringing together every lone 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 moreover be dash on private datacenters and in different public clouds anyway the one that IBM runs. To be actual, the Watson capabilities as well because the PowerAI machine gaining information 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 be do 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 fraction of the mashup of those tools, the PowerAI stack that makes a speciality of abysmal getting to know, GPU-accelerated machine gaining information of, and scaling and disbursed computing for AI, is being made a core fraction 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 development 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 color and equity detection. The software previously known as PowerAI and PowerAI enterprise will continue to be developed by the Cognitive techniques division. The Watson division, in case you don't look to be usual with IBM’s organizational chart, is fraction 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 noiseless occur earlier than the conclusion of the 12 months. nonetheless it is competitively priced to expect that IBM will tune up every lone of this software to dash on pink Hat commercial enterprise Linux and its own KVM digital machines and OpenShift implementation of Kubernetes and then propel definitely complicated.
it is likely profitable to evaluate what PowerAI is every lone about after which pomp 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 trade clients that hold deployed PowerAI tools to dash machine researching frameworks on its energy programs iron, and clearly GPU-accelerated systems fancy the power AC922 materiel 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 conclude AI work. here's a edifying looking edifying 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 hold used vitality 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 great innovation with PowerAI is what's known as colossal model help, which makes utilize 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 be loaded into the system. every lone of the PowerAI code is open source and dispensed as code or binaries, and so far only on power processors. (We suspect IBM will slump agnostic on this eventually, considering the fact that Watson tools should dash on the great 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 fraction 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 moreover added what it calls the abysmal getting to know influence module, which includes materiel 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 utilize 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 moreover be used to label realistic 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 every lone the SnapML framework that got here out of IBM’s analysis lab in Zurich this is providing a major efficiency capabilities on vitality iron compared to working frameworks fancy Google’s TensorFlow. here's surely a greater complete stack for computer discovering, including Watson Studio for developing fashions, the distinguished 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 relieve 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 trade licenses and pricing every lone over the first quarter, however after that PowerAI commercial enterprise may be 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 fraction in Jeopardy, is coming again domestic to Power9 with production machine discovering functions in the enterprise. They aren't transparent if IBM will present an identical allotted machine discovering capabilities on non-vigor machines, nevertheless it appears viable that is valued clientele wish to dash the Watson stack on premises or in a public cloud, it will ought to. vitality 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 reminiscence bandwidth, and coherent reminiscence throughout CPUs and GPUs, that may additionally not be as a mighty deal of an repercussion 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 status of HPC (and now AI) utilize in life sciences.
Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s apt 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 speedily networking has become distinguished (no dumbfound 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. fraction One, presented here, examines core infrastructure issues around processor choices, heterogeneous architecture, network bottlenecks (and solutions), and storage technology. fraction Two, scheduled for next week, tackles the AI’s trajectory in life sciences and the increasing utilize of cloud computing in life sciences. In terms of the latter, you may be chummy with NIH’s STRIDES (Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability) program which seeks to nick costs and ease cloud access for biomedical researchers.
HPCwire: Let’s tackle the core compute. eventual year they touched potential surge of processor diversity (AMD, Intel, Arm, Power9) and certainly AMD seems to hold advance on strong. What’s your win on changes in core computing landscape?
Chris Dagdigian: I can be quick and dirty. My view in the commercial and pharmaceutical and biotech space is that, aside from things fancy 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 twinge during the AMD/Intel wars and it would win something of huge significance to invent them shift again. In commercial biopharmaceutical and biotech there’s not a lot of exciting stuff going on in the CPU set.
The only other thing that’s exciting 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 fraction of my community (biomedical researchers in enterprise) where the users don’t even know what CPU their code is running on. That’s particularly apt for things fancy AWS batch, and AWS Lambda (serverless computing services) and that sort of stuff running in the cloud. I deem I’ll desist here are articulate on the commercial side they are leisurely and conservative and it’s noiseless an Intel world and the cloud is hiding a lot of the apt CPU stuff particularly as people slump serverless.
Aaron Gardner: That’s an exciting 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 articulate moreover that the surge of informatics as a service and workflows as a service is going to abstract things even more. It’s relatively simple today to dash most code with some smooth 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 fancy that.
We talked eventual 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 every lone the different types of heterogeneous compute services that span and champion the workload dash on that system. They definitely hold seen movement towards that model. If you search for at every lone Cray’s announcements in the eventual 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 very infrastructure, we’re seeing companies fancy Cray and others gearing up for a heterogeneous future where they are going to champion multiple processor architectures and optimize for multiple processor architectures as well as accelerators, CPUs and GPUs, and hold it every lone 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 unique stuff just to try it or to remark if it will be three percent faster because the differences gained in information generation at this point in life science for those three percent are not ground breaking – it’s fine to wait a diminutive while. Those days, however, are dwindling because of the amount of data being generated and the urgency with which it has to be processed and moreover the backlog of data that has to be 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 simple version of Linux, they are not optimizing for that; the exceptions to that are some of the built in math libraries that can be taken odds of on the Intel system platform, some of the data offloading for stirring 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 kindhearted of research you are doing.
HPCwire: It sounds a diminutive fancy 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 fancy a lot of the future architectures. AMD is coming out with for better reminiscence bandwidth to manipulate things fancy PCIe links, having unique interconnects between CPUs, and moreover the connection to the motherboard. One of the tremendous bottlenecks Intel noiseless has to resolve is how conclude you bag 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 noiseless hold to bag 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 reminiscence access, stirring away from NUMA (nonuniform memory) to a central reminiscence controller with uniform latency across dies. This is really distinguished when you hold up to 64 cores per socket. stirring back towards a more propitious reminiscence access model on a per node design smooth I deem is really going to relieve 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 conclude 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 invent much of a play outside of just simple HPC. The other thing I meant to bring up is a status 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 conclude 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 eventual 18 months because when you search for 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 type plays such as in the cloud where any odds in efficiency is going to be appreciated. So I deem they hold been strategic [in choosing target markets] and we’ll remark over the next couple of years how it plays out. I deem they are at the flash not in a status where the client needs to specify a inevitable processor. They are going to remark the integrators influence here, what they elect to do together in their heterogeneous HPC systems portfolio, influence what CPUs people bag and that may really outcome the winners and losers over time.
ARM they remark continue to grow but not explosively and I’d articulate Power is certainly interesting. Having the great Power systems at the top of the TOP500 has really validated Power9 for utilize in capability supercomputing. How those are used though versus the GPUs for target workloads is interesting. In common they may be headed to a future where the CPU is used to turn on the GPU for inevitable workloads. Nvidia would probably favor that model. It’s just very exciting the interplay between CPU and GPU; it really does hold to conclude with whether you are accelerating a minute number of codes to the nth degree or you are trying to hold more diverse application champion which is where multiple CPU and GPU architectures are going to be needed.
Ari Berman: Using GPUs is noiseless a huge thing for lots of different reasons. At the flash 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 moreover down into phylogenetic inference, special inheritance, things fancy that. There are many mighty applications for realistic processors, but really I would accord with others that it really boils down to system processors and GPUs at the flash in life sciences. I did hear anecdotally from a couple of folks in the industry that were using the IBM Q cloud just to try quantum [computing], just to remark how it worked with really lofty smooth genomic alignment and they kindhearted of got it to travail and I’ll leave it at that.
HPCwire: They probably don’t dedicate enough coverage to networking given its importance driven by huge datasets and the surge of edge computing. What’s the status of networking in life sciences?
Chris Dagdigian: In pharmaceuticals and biotech, Ethernet rules the world. The lofty accelerate low latency interconnects are noiseless in niche environments. When they conclude remark 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 articulate networking accelerate is now the most censorious 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 lone biggest rate limiting bottleneck for HPC in pharma and biotech. Combine with that the cost lofty accelerate Ethernet has not gone down as speedily as the cost of commoditization in storage and compute. So they are in this double whammy world where they desperately need speedily networks.
The corporate networking people are fairly smug about the 10 gig and 40 gig links they hold in the datacenter core whereas they need 100 gig networking going outside the datacenter, 100 gig going outside the building, sometimes they need 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 remark the insane data movement that they hold to conclude 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 argument does not cruise in enterprise because there is a tremendous installed base. So I am in the snare 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 accord networking is one of the major challenges. Depending on what granularity you are looking at, I deem most of the HPCwire readers will reliance a lot about interconnects on clusters. Starting there, I would articulate they are seeing a fairly even distribution of simple Ethernet on the back End because of vendors fancy Arista for instance, which is producing more affordable 100 gig low latency Ethernet that can be do on the back End so you don’t hold to conclude the whole RDMA versus TCP/IP dance necessarily. But most clusters are noiseless using InfiniBand on their back end.
In life sciences I would articulate that they noiseless remark 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 mighty effect, and they fancy it a lot, but not really so much in life sciences. I’d articulate the accelerate 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 Hurt them. I conclude deem the unique interconnects fancy 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 win their security enough out of the course or optimize it enough so that people can effectively utilize the cloud for data-intensive applications, that getting the data there is impossible. You can utilize the cloud but only if the data is already there. That’s a huge problem.
Internally, a lot of organizations hold moved to burning spots of 100 gig to be able to slump data effectively between datacenters and from external data sources but a lot of 10 gig noiseless predominates. I’d articulate 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 figure factors hold shrunk.
Going back to the cluster back end. In life sciences the understanding lofty performance networking on the back End of a cluster is really distinguished isn’t necessarily for inter-process communications, it’s for storage delivery to nodes. Almost every implementation has a great parallel distributed file system where every lone of the data are coming from at one point or another. You hold to bag them to the CPU and that backend network needs to be optimized for that traffic.
Aaron Gardner: That’s a common case in the life sciences. They primarily search for at storage performance to bring data to nodes and even to slump between nodes versus message passing for parallel applications. That’s starting to shift a diminutive bit but that’s traditionally been how it is. They usually hold 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 speedily fabric for internode communications for great scale parallel jobs and then having a storage fabric that was either brought to every lone of the nodes or quasi shunted into the other fabric using IO router nodes.
“One of the things that is very exciting with Cray announcing Slingshot is the talent to speak both an internal low latency HPC optimized protocol as well as Ethernet, which in the case of HPC storage removes the need for IO router nodes, instead allowing the HCA (host channel adapters) and switching to manipulate the load and protocol translation and every lone of that. Depending on how transparent and simple it is to implement Slingshot at the minute 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 do 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 elect from, and the actual act of storing the data is relatively straightforward. However, no one has centrally cracked the how they manage it, how conclude they understand what we’ve got on disk, how conclude they carefully curate and maintain that stuff. Overwhelmingly the dominant storage pattern in my world is if they are not using a parallel files system for accelerate 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 be seen as dinosaurs or being placed on a 3-year or 4-year upgrade cycle.
The other thing is there’s noiseless a lot of interest in hybrid storage, storage that spans the cloud and can be replicated into the cloud. The technology is there but in many cases the pipes are not. So it is noiseless relatively difficult to either synchronize or replicate and maintain a consistent storage namespace unless you are a really solid organization with really speedily pipes to the outside world. They noiseless remark the problems of lots of islands of storage. The only other thing I will articulate is I am known for maxim the future of scientific data at ease belongs in an kick store, but that it’s going to win a long time to bag there because they hold so many dependencies on things that expect to remark files and folders. I hold customers that are buying petabytes of network attached storage but at the very time they are moreover buying petabytes of kick storage. In some cases they are using the kick storage natively; in other cases the kick storage is their data continuity or backup target.
In terms of file system preference, the commercial world is not only conservative but moreover incredibly concerned with admin cross and value so almost universally it is going to be a mainstream option fancy GPFSsupported by DDN or IBM. There are lots of really exciting alternatives fancy BeeGFS but the issue really is the enterprise is nervous about fancy unique technologies, not because of the fancy unique technologies but because they hold to bring unique people in to conclude the reliance and feeding.
Aaron Gardner: Some of the challenges with how they remark storage deployed across life science organizations is how near to the bottom hold they been driven. With traditional supercomputing, you’re trying to bag the fastest storage you can, and the most of it, for the least amount of money. The champion needed is not the primary driver. In HPC as a whole, Lustre and GPFS/Spectrum Scale are noiseless the predominate players in terms of parallel file system. The exciting stuff over the eventual year or so has been Lustre trading hands (from Intel to DDN). With DDN leading the charge, the ecosystem is noiseless being kept open and I deem carefully crafted so other vendors can provide solutions independently from DDN. They conclude remark IBM stepping up Spectrum Scale performance and Spectrum Scale 5offering a lot of edifying features proven out and demonstrated on the peak and Sierra type systems, making Spectrum Scale every bit as pertinent as it ever was.
As far as performant parallel file systems there are exciting alternatives. There is more presence and momentum behind BeeGFS than they hold seen in prior years. They remark some adoption and clients interested in trying and adopting it but the number deployments in production and at a great scale is noiseless pretty limited.
These days kick storage is seen more fancy a tap that you turn on and you are getting your kick storage through AWS or Azure or GCP. If you are buying it for on-premise, there’s diminutive differentiation seen between kick vendors. That’s the perception at least. They are seeing interest in what they summon next generation storage systems and file systems – things like WekaIO that provide NVMe over fabrics (NVMeOF) on the front End and export their own NVMeOF indigenous file system as opposed to cache storage. This removes the need to utilize something fancy Spectrum Scale or Lustre to provide the file system and can drain chilly data to kick storage either on premise or in the cloud. They conclude remark that as a viable model stirring forward.
I would add articulate that speaking to NVME over fabrics in general; that it seems to be growing and becoming established as most of the unique storage vendors coming on the scene are currently architecting that way. That’s edifying in their book. They certainly remark performance advantages but it really matters how it’s done—it is distinguished that the software stack driving the NVME media has been purpose built for NVME over fabrics or at least significantly redesigned. Something ground up fancy WekaIO or VAST will effect very well. On the other hand you could elect 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 remark much benefit.
Couple of other quick notes. It seems fancy 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 fancy the IO500 we’ve seen an uptick in participation, both from national labs as well as vendors and other organizations. The eventual 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 conclude 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 advance at the problem from different angles you could search for 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 slump 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 fraction Two (HPC in Life Sciences fraction 2: Penetrating AI’s Hype and the Cloud’s Haze)
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 compass 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 be censorious to ensure the secure rush of data from the edge to the platform. At another level, providing suitable processing and storage capabilities, and enabling the utilize 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 irony for many of their customers in multiple sectors. We’re continuing to invent it easier for customers to innovate and meet their growing IoT trade needs and we’re delighted to welcome Asavie IoT Connect on AWS Marketplace to relieve customers quickly and securely deploy IoT solutions.”
Brendan Carroll, CEO with industrial IoT sensor manufacturer, EpiSensor said, “Our global customers rely on the calibre of their products to continually monitor and provide insights on their industrial processes, 24/7. In turn they rely on their suppliers Asavie and AWS to provide the resilient, secure connectivity and storage services to enable us to fulfill their exacting service smooth 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 pertinent 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 slump 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.
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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 knowing 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 follow @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/
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For AsavieHugh Carroll, Asavie, + 353 1 676 3585/+353 087 136 9869 email@example.comAnne Marie McCallion, ReturnPR +353 86 8349329 firstname.lastname@example.org
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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 endeavor 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 be too expensive, according to a 2018 survey commissioned by digital security provider Gemalto.
Currently, only 48% of IoT project owners can remark 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 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 unique event or change in status has to be verified by 51% of the members of the group participating in a particular ledger, it should be viable to trail an individual component from point of sale to the original manufacturer to verify whether it’s been tampered with.
Blockchain moreover can be used to track and verify sensor data, avert 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 utilize of blockchain in both technical and pecuniary 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 measure solution to secure IIoT/IoT assets.
Any security may be better than none, but some of the more Popular 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 search for at it carefully, know what you’re trying to accomplish and what the security smooth is,” Chen said. “Public blockchain is mighty for things fancy 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 bag more than 50% to cooperate. Securing IIoT devices, even across a supply chain, is going to be a lot smaller group, which wouldn’t be much reassurance that something was accurate. And meanwhile, we’re noiseless trying to motif out how to conclude 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 status of an IoT device is probably not a edifying utilize 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 need 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 need that smooth 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 invent a process more secure by removing the need for trust, argues security guru Bruce Schneier, CTO of IBM Resilient.
Blockchain reduces the amount of reliance they hold to do in humans and requires that they reliance computers, networks and applications that may be lone points of failure. By contrast, a human-driven legal system has many potential points of failure and recovery. One can invent the other more efficient, but there’s no understanding to assume that simply shifting reliance to machines, regardless of context or attribute of execution, will invent anything better, Schneier wrote.
Public-ledger verification methods can be 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 need of standards, performance issues and regulatory doubt every lone 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 noiseless plenty of leeway for innovation and improvement.IoT Device Security Makes leisurely ProgressWhile attention is being paid to security in IoT devices, noiseless more must be done.Are Devices Getting More Secure?Manufacturers are paying more attention to security, but it’s not transparent whether that’s enough.Why The IIoT Is Not SecureDon’t blame the technology. This is a people problem.
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CPP-Institue [2 Certification Exam(s) ]
CPP-Institute [1 Certification Exam(s) ]
CSP [1 Certification Exam(s) ]
CWNA [1 Certification Exam(s) ]
CWNP [13 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [9 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [21 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [129 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [40 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [13 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [750 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1532 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
Juniper [64 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [24 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [8 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [69 Certification Exam(s) ]
Microsoft [374 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [39 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [279 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [12 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [134 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
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) ]
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