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000-671 - IBM Systems Networking Sales V1 - Dump Information

Vendor : IBM
Exam Code : 000-671
Exam Name : IBM Systems Networking Sales V1
Questions and Answers : 100 Q & A
Updated On : June 14, 2019
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Test Number : 000-671
Test Name : IBM Systems Networking Sales V1
Vendor Name : IBM
Q&A : 100 Dumps Questions

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IBM Systems Networking Sales V1 certification

the place Does Nvidia Go within the Datacenter From here? | 000-671 Dumps and Real test Questions with VCE Practice Test

We should admit that it is regularly a lot more fun looking at an upstart carve out whole new slices of company, or create them out of what appears to be skinny air, in the datacenter than it's to observe how it will reply to severe competitive pressures and in some way have the capacity to hold growing to be despite that.

however honestly, it truly is the effortless part. Being definitely creative a few times or even thrice, while no longer normal, is anything that has been accomplished by way of many corporations in the six decades of business and technical information processing and visualization. Innovating to a decade is hard, for 2 or three many years is tough as hell, and doing it for longer than that – IBM has been at it for a dozen a long time now and is still within the assistance expertise video game it created greater than another firm in historical past – is particularly improbably but now not unimaginable.

we now have extraordinary admiration for these groups that find niches, create markets, and crack them vast open to advantage with revenues and gains. here's how the device – the financial one – is meant to work.

Nvidia is one such business, and it's important to bear in mind that Nvidia is comparatively new to compute however has truly changed the online game for definite forms of simulation, modeling, analytics, and machine learning workloads. Mellanox applied sciences has in a similar fashion been an innovator, commercializing the InfiniBand networking protocol – which become imagined to be a kind of time-honored switched material for every kind of purchasers and servers nevertheless it didn’t fairly work out that approach – for HPC centers and then relocating on just a few years ago to Ethernet switching. Mellanox has been a champion of offloading as plenty of the networking workload onto network interface playing cards and the switches themselves to release compute means on servers, and this has freed up untold of amounts of compute skill to do real work on many clusters over the last two a long time that InfiniBand has been shipping.

Of route, Nvidia is in the center of acquiring Mellanox for $6.9 billion, and it is that this truth as well as increasing competition from Intel and AMD and the primary awards for exascale techniques within the u.s. that has us wondering what possible paths Nvidia could take to get its datacenter enterprise turning out to be again despite each Intel and AMD moving in on its GPU acceleration turf for both HPC and AI workloads.

each Nvidia and AMD were caught a bit flatfooted by the steep decline fashionable for GPU compute for cryptocurrency mining as 2018 was winding down, and this turns out to were a much bigger part of earnings of GPU playing cards than either company had anticipated. To be fair, it's tough to music who's the use of what card for what job when the vast majority of Nvidia and AMD GPU sales are pulled via channel companions, who don’t at all times have superb visibility into customer accounts. Compute clusters doing funky stuff like possibility administration, superior analytics, laptop researching, inner most HPC, and cryptocurrency mining are exactly the types of equipment that people retain mum about. So it is challenging accountable Nvidia or AMD for no longer knowing how a good deal of this company is driven via what actual workloads and for not seeing the massive drop off in revenues from the cryptocurrency section that took place in 2018. If Intel had been delivery its eleventh generation Xe discrete GPU snap shots and compute cards two years ago, as a substitute of next year, it would have ridden up its share of the wave and would had been equally bruised up when that wave crashed on the rocks.

One needs to be philosophical about such ephemeral phenomena. groups make the funds when they could and where they could, and they do not look present clients within the mouth. So it became with Nvidia and AMD and the cryptocurrency miners and their a number of year affair with GPUs, which took place to pressure up demand for GPU compute at precisely the equal time that HPC and AI workloads accelerated by GPUs had been ramping sharply. This allowed Nvidia in selected to command a hefty top rate for its Tesla GPU accelerator playing cards in generic, which pumped up its datacenter business considerably in contemporary years. entering the methods company, the place Nvidia sells components for virtually accomplished techniques (HGX) or finished programs (DGX) has also helped accelerate Nvidia’s datacenter enterprise in contemporary years.

counting on if three fourths of the OpenPower hardware partnership – that’s IBM, Nvidia, and Mellanox, but now not together with Xilinx in this case – is able to close a deal for an exascale system with massive Blue because the major contractor, Nvidia may try to go it by myself and bid on its own for future exascale system and it could even chase sub-exascale hybrid AI/HPC system offers. Stranger things have took place, and Intel has definitely set a precedent for this, beginning manner again with the Paragon Touchstone Delta system at Caltech in 1992, ASCI crimson at Sandia countrywide Laboratories in 1996, and now the Aurora A21 gadget at Argonne countrywide Laboratory due in 2021 as the first exascale system within the united states, and one so that you can deploy a mixture of Xeon CPUs and Xe GPUs. The Aurora nodes could be connected the use of Cray’s “Slingshot” HPC-trend Ethernet interconnect, and Cray, which is being got by means of Hewlett Packard business for $1.3 billion, is the system integrator on the assignment, working with Intel. okayRidge national Laboratories, which at present is working purposes on its new “Summit” hybrid IBM Power9 CPU/Nvidia Tesla “Volta” V100 GPU accelerator, the first exascale class equipment at this lab, called “Frontier,” became now not in response to IBM’s future Power10 processor paired with a future (perhaps “Einstein”) Nvidia GPU, but somewhat a mix of Epyc CPUs and Radeon GPUs from AMD, with the nodes also linked through Cray’s interconnect and with Cray because the major contractor. neatly, quickly to be HPE.

These two machines represent about half of the $2.four billion finances for exascale methods in the united states, and there is one and doubtlessly an extra gadget in the works. The different equipment within the CORAL-2 round of funding that we find out about for certain is the “El Capitan” ATS-four successor to the “Sierra” device (additionally a hybrid Power9/Volta system) at Lawrence Livermore country wide Laboratory. because the branch of power has traditionally appreciated to spread out its risk throughout two or three different vendors and their respective architectures, El Capitan has a excessive likelihood of staying with an influence/Tesla combination, however as okayRidge’s 1.5 exaflops Frontier device, slated for late 2021 or early 2022, shows, there isn't any warranty during this market and LLNL pointed out as tons as Sierra became being prepped for roll out on the end of 2017. but the expectation back in late 2017, as Bronis de Supinski, chief expertise officer at Livermore Computing, the IT arm of the nuclear research lab, changed into for the El Capitan machine to offer GPU acceleration of some style.

it's feasible that El Capitan can be in keeping with that Power10/Einstein combo and likely with 400 Gb/sec InfiniBand interconnect from Mellanox and for this reason provide the OpenPower companions some cash to cover construction fees. (most of the $600 million or so dedicated to an exascale gadget is used up buying elements to build the laptop, with some leftover for application construction, chassis and enclosure design, preservation, and we presume a tiny little bit of income.) but given what came about with the Summit and Sierra machines, which have been each in line with IBM’s vigour AC922 server with distinctive GPU and leading memory configurations (however the same CPU and network interface playing cards), it is now equally possible that Lawrence Livermore will follow suit and deploy a clone of or variant of the Frontier gadget in response to AMD compute engines and Cray interconnect.

should that happen, this is able to definitely be a huge blow to IBM, Nvidia, and Mellanox. Nvidia GPUs are getting used within the pre-exascale “Perlmutter” NERSC-9 laptop at Lawrence Berkeley country wide Laboratory, which is being developed through Cray for $146 million and which makes use of “Rome” Epyc CPUs from AMD; Cray’s Slingshot interconnect is getting used right here, too. It appears most unlikely that the NERSC-10 exascale comply with-on, due in 2024 or so, would shift to Power10 processors and a future InfiniBand interconnect and use future Nvidia GPUs, however anything is viable even it if is not in all likelihood. NERSC has picked a different CPU after which accelerator structure for the past 4 machines, so there's precedent. lots depends upon how well AMD’s CPUs and GPUs and Cray’s Slingshot interconnect works – and what AMD and HPE want to charge for an exascale supercomputer.

For the second, Nvidia can count number on its Tesla GPU accelerators being the engines of option for machine gaining knowledge of training, no count number what host CPU is deployed to manage the work that those GPUs use, and to nonetheless be the dominant accelerator used for normal HPC simulation and modeling applications for these locations that can make use of accelerators in preference to simply uncooked CPU clusters. this is true peculiarly as a result of the entire key computing device getting to know frameworks and the core business and open supply HPC applications have all been tweaked to aid the CUDA parallel programming environment created via Nvidia to manage the work on the GPU accelerators. whereas AMD has ROCm choice for its CPU-GPU combinations, and Intel is engaged on OneAPI to make a development atmosphere that can span CPUs, GPUs, and FPGAs, for now OpenMP, OpenACC, and CUDA are getting used in quite a lot of methods to develop and tune hybrid CPU and Tesla accelerator applications, and Nvidia has tuned up all the a number of math libraries needed for their functions to run well on GPUs, and so Tesla GPUs stay the de facto accelerator for these agencies that need more than just what a straight CPU can carry in terms of compute and reminiscence bandwidth.

here is, of course, the key driver to Nvidia’s datacenter business. particularly talking – and in opposition t some very hard compares last yr when Nvidia booked earnings of GPUs for the Summit and Sierra supercomputers, the hyperscalers had been spending like tipsy sailors, and cryptocurrency mining had no longer dropped – the datacenter community at Nvidia is doing fairly well. The hyperscalers took a pause within the first quarter of fiscal 2020 resulted in April, and as we have stated time and again, spending via HPC associations and with the aid of hyperscalers and cloud builders is choppy, and sometimes the curves build to new highs and infrequently they mix to dive to lows. here is a type of times, and even nonetheless, Nvidia’s datacenter company turned into only off 9.6 p.c to $634 million. Gaming GPU revenue, by way of sharp contrast, were down 38.eight percent to $1.06 billion; knowledgeable visualization products basically had a 6 p.c boost to $266 million, and self-driving car initiatives drove Nvidia’s car earnings up 14.5 percent.

curiously, Nvidia is starting to get some traction with its Tesla T4 GPU accelerators, whose “Turing” GPUs may also be used for computer studying inference at prices and efficiency and thermals that are competitive with FPGAs and may also be used for practising in a pinch and for visualization with dynamic ray tracing as neatly. (The Tensor Core dot product engines are the important thing there.) In a convention name with Wall road analysts lately, Nvidia chief monetary officer Colette Kress said that “the contribution of inference to our datacenter revenue is now smartly into the double-digit p.c.” name it $sixty five million out of $634 million, just for fun. That ability with out the T4 accelerators and the power for more desirable bang for the buck on inference, the datacenter enterprise would have been off around 19 p.c. all of the principal clouds and server OEMs are assisting T4 playing cards for inference workloads.

It continues to be to be considered if Nvidia can own computer gaining knowledge of inference as it does laptop gaining knowledge of training. Most laptop getting to know inference is being executed on CPUs nonetheless, however here is an inefficient and costly technique to do inference (at least at hyperscale) and intensely enormous organizations which are leaders in making use of computing device discovering are looking an offload mannequin to inference engines that cost plenty less and burn even less juice than modestly sized GPUs and FPGAs. Intel’s Nervana unit in addition to Wave Computing, and Graphcore are taking aim at FPGAs and CPUs for inference, but Intel has to be careful to now not denigrate its personal CPUs and FPGAs because it pushes Nervana devices.

With computer discovering practicing, everybody is gunning for Nvidia’s Tesla GPU accelerators, and at a time when Nvidia is being mainly quiet about its future roadmaps. Graphcore, Nervana, and Wave Computing are obtainable, pushing the heftier of their instruments for machine discovering training, and a few of the folks at Google who labored on the Tensor Processing Unit (TPU) task have left to kind Groq, which seeks to create and commercialize a clone of the TPU. Google claims that its TPU three.0 AI engine can do each inference and practicing on the identical equipment.

All of these agencies, plus myriad others chasing the nascent but possibly hockey sticking inference markets, are going to put an immense volume of force on Nvidia. but, then once again, Nvidia is used to power. It’s now not like competing with AMD in pictures and wedging its method into the supercomputer area changed into a stroll in the park.

The decent information is that for the moment, GPUs are the simplest useful accelerators for certain accelerated HPC simulation and modeling applications, and given the virtually four hundred functions that can also be GPU accelerated, this may still deliver a gradual – if bumpy – salary flow for Nvidia’s datacenter enterprise. Hyperscalers and cloud builders could be purchasing Tesla accelerators for HPC and computer getting to know working towards workloads, and there might be u.s.and downs right here. The cloud builders in specific should be averse to the usage of a really good device aimed simply at desktop discovering because they should create infrastructure that will also be repurposed on a whim. a tool that can do virtual computer infrastructure, visualization, database acceleration, HPC simulation and modeling, and machine researching practicing and inference in a pinch will trump a device that simply does laptop getting to know practising on the clouds – even if it costs a top class. Cloud builders need flexibility – however not at any charge, of route.

When Nvidia became the most effective online game in town, it might – and did – cost a premium for its Tesla GPU accelerators. With AMD getting its CPU and GPU acts together and Intel making an attempt to ruin into the discrete GPU market with more earnestness than it did with “project Larrabee” (which spawned the Knights family unit of many-cored processors that had some play in HPC and AI) so decades ago, Nvidia is not going to be in a position to command the premiums it has loved. here is going to get more durable, now not more straightforward, and notwithstanding the correct line grows, it may be tricky to grow working revenue for the datacenter unit.

Mellanox will snap in, hopefully by the conclusion of this fiscal 12 months in January 2020, to bolster the datacenter neighborhood’s numbers at Nvidia, to roughly whatever thing on the order of $1 billion a year, carved up in the uneven ways that hyperscalers, cloud builders, and HPC centers spend on server adapters and switches (or OEMs and ODMs spend on the chips to make these contraptions). Mellanox finished up its first quarter in March, and didn't dangle its common conference will Wall street where it goes over the diverse aspects of its business. this is about all we know:

Mellanox revenues rose by using 21.6 % to $305 million in the first quarter, and net income rose by means of 28.5 p.c to $48.6 million. Mellanox ended the quarter with $533 million in cash, which ends up going again into Nvidia’s pocket as soon as the acquisition is executed. We don't have any thought how well or poorly any Mellanox items did, as a result of Mellanox did not host its call with Wall street, but we suspect that InfiniBand sales are flat sequentially as they have got been for the previous few quarters and Ethernet earnings proceed to rise. by the way, that is essentially the most revenue that Mellanox has ever posted in 1 / 4, however has been twice as ecocnomic – for a quick time again in 2012 – earlier than. Add Mellanox to the datacenter enterprise at Nvidia, and you've got an organization that sells round $4 billion a yr in stuff, even with the downdraft that Nvidia is coping with.

It is still not clear exactly how Nvidia will make use of Mellanox, but a couple of issues are clear. One, AMD is again within the CPU and GPU video game and its products are successful deals at the hyperscalers, cloud builders, and HPC facilities of the area and agencies gained’t be that a long way behind. Two, Intel is getting its manufacturing act together and it has a brand new crew this is working to get more advantageous CPUs and now new GPUs within the box to enrich its Altera FPGAs. a few Arm server chip carriers – notably Marvell and Ampere – are preserving the drive on Intel and AMD a little and there are upstarts coming out of the woodwork going after the computing device discovering money with really expert ASICs that promise improved efficiencies than GPUs or FPGAs can carry.

That leaves the OpenPower founders – IBM, Nvidia, Mellanox, and Xilinx – as a form of counterbalance to Intel and AMD. In early 2017, we contemplated why IBM should still actually purchase Nvidia, Mellanox, and Xilinx to build momentum for a modern methods business with the entire points mandatory. IBM’s vigour techniques enterprise generates north of $2 billion a yr, and so does Xilinx, and in case you add in the $four billion or so from the datacenter businesses of Nvidia and Mellanox, here's a much better counterbalance to the hegemony of Intel and the rising AMD. If Nvidia wants to hold its personal Switzerland fame with its OEM and ODM partners, it may need to suppose about working with IBM and Xilinx to create a partnership that can take down exascale-classification deals as well as sell accessories to those OEMs and ODMs.

we have idea, and proceed to consider, that in a world accelerated with the aid of FPGAs and GPUs, IBM should still cut the vector engines out of a Power9 or Power10 chip and simply create a screaming quickly serial engine with big amounts of memory bandwidth and lots of ports to offload work to those accelerators. IBM and Nvidia have worked out coherency across the CPU and GPU, and the FPGA can be woven into this, too. These are true advantages.

There can be time still to install the OpenPower company earlier than it is too late. it will ought to be known as some thing distinctive, of course. Spectrum is an evident name that each Mellanox and IBM use in their high efficiency products, and it will basically sell the spectrum of compute and networking. These companies can pool their wares a little greater intensely as a substitute of wasting cash purchasing each other.

It’s a notion.


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