Intel’s new DL Boost for DL AI inferencing

I was at a TechFieldDay Extra with Intel Data Centric Innovation Conference last week in San Francisco. It was a lavish affair with many industry analysts in attendance besides the TFDx crew.

At the event Intel announced a number of new products including the availability of their next generation scaleable Xeon processor chips, new Optane DC PM (DIMM) and software, new Ethernet (800) NIC cards, new FPGA line (10nm) and DL (deep learning inferencing) boost functionality.

I was most interested in the DL Boost and Optane DC PM solutions. For this post I focus on DL Boost.

DL Boost for DL inferencing on Xeon

Intel’s DL Boost technology provides a new integer 8 bit precision (INT 8) matrix multiply & summation instruction which can be used to speed up DL inferencing operations. As those who have been following along with my AI-DL-machine learning (ML) blog posts (latest being Learning Machine Learning part 3), probably know, deep learning machine learning that processes data to create a neural network made up with a number of layers and a number of nodes each of which represents a floating point weight used to transform inputs into outputs.

All DL AI projects involve at least two phases: model training and model inferencing (prediction, classification, AI result, etc.). Although both of these activities involve matrix calculations, model training involves a lot more of these compute intensive operations than inferencing. In fact, while training typically is done on GPUs or other special purpose compute hardware (TPU, IPUs, etc.) inferencing can typically be done on standard off the shelf CPUs.

Historically. inferencing used floating point matrix multiplication and summation functionality ,taking input from sensors, logs, photos, etc. and performing the model logic to create an output.

Intel believes (with industry analyst agreement) that over time, 50% or more of the DL AI workload is going to involve inferencing. Hence, the focus on this end of the AI workload, at least for now.

For example, although speech recognition AI can take a long time to process audio recordings and use reinforcement learning to train a recognition model. But, once trained, you could use that recognition AI model in anything from smart speakers, to speech to text dictation machines, to voice response systems, etc. In all of these the recognition model is passed a voice recording (or voice in real time) and processes these to create a text version of the speech.

But all of this has historically been done in floating point (FP) 32 (bit precision) or FP 16. Google’s TPU is capable of doing this with less precision, but to my knowledge, up to this point, it’s always been floating point.

What is DL Boost

What Intel has done with DL Boost is to create a new X86 instruction which can perform an integer (INT) 8 (bit precision) matrix multiplication and summation with less cycles than what it took before. Intel believes if customers were to modify their trained AI neural network models to move from FP 32 (or 16) to INT 8, they could perform inferencing much faster on Xeon Cascade Lake CPUs, than they could before and not have to rely on GPUs for this activity at all.

Yes, this does require hand optimization of trained AI neural network. Some of this may be automated, but not all. Intel claims the precision loss, if done properly, is less than a few percent and it’s impact on AI inferencing correctness is negligible at best.

At the moment, for all the DL modeling I have done, i have never looked at the trained model’s weights leaving this to TensorFlow/Keras to manage for me. But I’m not creating production level DL AI systems (yet). So, I don’t know what it would take to modify my AI models to use INT 8 nor what level of degradation in correctness would ensue. But I also don’t have Cascade Lake Xeon CPUs available.

Some potential problems here:

  1. Manual activity to hand tune the INT 8 neural network is not going to be that popular, except for those organizations where inferencing requires GPUs.
  2. Most production DL AI models, undergo some form of personalization for a user or implementation instance which would require a further FP to INT conversion for each user/implementation.
  3. Most production DL AI models also undergo periodic retraining to fine tune the model with the latest data that has been accumulated. This would also require further FP to INT conversion after each training cycle.

In the end, there’s an advantage for production AI inferencing, for models that don’t require substantial retraining/personalization as they don’t change that often. And there’s a definite cost advantage to using DL Boost INT 8, for those AI inferencing that must use GPUs today to perform in real time or under other performance constraints.

But hand converting neural networks, reminds me of creating assembly code for modules that can impact performance. This is normally reserved for only a select modules or functionality that executud a lot. However, DL models are much more monolithic and by definition, less modular. Identifying which models (or model layers) within a production DL AI solution that are performance sensitive and hand optimizing them to work on CPUs rather than GPUs, seems like a hard task.

It would be better from my perspective to create a single FP 16 matrix multiplication instruction. Alternatively, create some software that would automatically convert any DL AI model (or model layer) from FP to INT 8. That way DL Boost optimization would be just another step in the model training process and could be automatically generated to see if A) it loses too much sensitivity and B) if it’s worthwhile using CPU inferencing.

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Comments?

Screaming IOP performance with StarWind’s new NVMeoF software & Optane SSDs

Was at SFD17 last week in San Jose and we heard from StarWind SAN (@starwindsan) and their latest NVMeoF storage system that they have been working on. Videos of their presentation are available here. Starwind is this amazing company from the Ukraine that have been developing software defined storage.

They have developed their own NVMe SPDK for Windows Server. Intel doesn’t currently offer SPDK for Windows today, so they developed their own. They also developed their own initiator (CentOS Linux) for NVMeoF. The target system was a multicore server running Windows Server with a single Optane SSD that they used to test their software.

Extreme IOP performance consumes cores

During their development activity they tested various configurations. At the start of their development they used a Windows Server with their NVMeoF target device driver. With this configuration and on a bare metal server, they found that they could max out the Optane SSD at 550K 4K random write IOPs at 0.6msec to a single Optane drive.

When they moved this code directly to run under a Hyper-V environment, they were able to come close to this performance at 518K 4K write IOPS at 0.6msec. However, this level of IO activity pegged 100% of 8 cores on their 40 core server.

More IOPs/core performance in user mode

Next they decided to optimize their driver code and move as much as possible into user space and out of kernel space, They continued to use Hyper-V. With this level off code, they were able to achieve the same performance as bare metal or ~551K 4K random write IOP performance at the 0.6msec RT and 2.26 GB/sec level. However, they were now able to perform only pegging 2 cores. They expect to release this initiator and target software in mid October 2018!

They converted this functionality to run under ESX/VMware and were able to see much the same 2 cores pegged, ~551K 4K random write IOPS at 0.6msec RT and 2.26 GB/sec. They will have the ESXi version of their target driver code available sometime later this year.

Their initiator was running CentOS on another server. When they decided to test how far they could push their initiator, they were able to drive 4 Optane SSDs at up to ~1.9M 4K random write IOP performance.

At SFD17, I asked what they could have done at 100 usec RT and Max said about 450K IOPs. This is still surprisingly good performance. With 4 Optane SSDs and consuming ~8 cores, you could achieve 1.8M IOPS and ~7.4GB/sec. Doubling the Optane SSDs one could achieve ~3.6M IOPS, with sufficient initiators and target cores with ~14.8GB/sec.

Optane based super computer?

ORNL Summit super computer, the current number one supercomputer in the world, has a sustained throughput of 2.5 TB/sec over 18.7K server nodes. You could do much the same with 337 CentOS initiator nodes, 337 Windows server nodes and ~1350 Optane SSDs.

This would assumes that Starwind’s initiator and target NVMeoF systems can scale but they’ve already shown they can do 1.8M IOPS across 4 Optane SSDs on a single initiator server. Aand I assume a single target server with 4 Optane SSDs and at least 8 cores to service the IO. Multiplying this by 4 or 400 shouldn’t be much of a concern except for the increasing networking bandwidth.

Of course, with Starwind’s Virtual SAN, there’s no data management, no data protection and probably very little in the way of logical volume management. And the ORNL Summit supercomputer is accessing data as files in a massive file system. The StarWind Virtual SAN is a block device.

But if I wanted to rule the supercomputing world, in a somewhat smallish data center, I might be tempted to put together 400 of StarWind NVMeoF target storage nodes with 4 Optane SSDs each. And convert their initiator code to work on IBM Spectrum Scale nodes and let her rip.

Comments?

New website monetization approaches

Historically, websites have made money by selling wares, services or advertising. In the last two weeks it seems like two new business models are starting to emerge. One more publicly supported and the other less publicly supported.

Europe’s new copyright law

According to an article I read recently (This newly approved European copyright law might break the Internet), Article 11 of Europe’s new Copyright Directive (not quite law yet) will require search engines, news aggregators and other users of Internet content to pay a “link tax” to copyright holders of anything they link to. As a long time blogger, podcaster and content provider, I find this new copyright policy very intriguing.

The article proposes that this will bankrupt small publishers as larger ones will charge less for the traffic. But presently, I get nothing for links to my content. And, I’d be delighted to get any amount – in fact I’d match any large publishers link tax amount that the market demands.

But my main concern is the impact this might have on site traffic. If aggregators pay a link tax, why would they want to use content that charges any tax. Yes at some point aggregators need content. But there are many websites full of content, certainly there would be some willing to forego tax fees for more traffic.

I also happen to be a copyright user. Most of my blog posts are from articles I read on the web. I usually link to an article in the 1st one or two paragraphs (see above and below) of a post and may refer (and link) to more that go deeper into a subject. Will I have to pay a link tax to the content owner?

How much of a link tax is anyone’s guess. I’m not sure it would amount to much. But a link tax, if done judiciously might even raise the quality of the content on the web.

Browser’s of the world, lay down your blockchains

The second article was a recent research paper (Digging into browser based crypto mining). Researchers at RWTH Aachen University had developed a new method to associate mined blocks to mining pools as a way to unearth browser-based mined crypto coins. With this technique they estimated that 1.8% of all Monero coins were mined by CoinHive using participant browsers to mine the coin or ~$250K/month from browser mining.

I see this as steeling compute power. But with that much coin being generated, it might be a reasonable way for an honest website to make some cash from people browsing their web pages. The browsing party would need to be informed of the mining operation in the page’s information, sort of like “we use cookies” today.

Just think, someone creates a WP plugin to do ETH mining and when activated, a WP website pops up a message that says “We mine coins while you browse – OK?”.

In another twist perhaps the websites could share the ETH mined on their browser with the person doing the browsing, similar to airline/hotel travel awards. Today most travel is done on corporate dime, but awards go to the person doing the traveling. Similarly, employees could browse using corporate computers but they would keep a portion of the ETH that’s mined while they browse away… Sounds like a deal.

Other monetization approaches

We’ve tried Google AdSense and other advertising but it only generated pennies a month. So, it wasn’t worth it.

We also sell research and occasionally someone buys some (see SCI Research Shop). And I do sell services but not through my website.

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Not sure a link tax will fly. It would be a race to the bottom and anyone that charged a tax would suffer from less links until they decided to charge a $0 link tax.

Maybe if every link had a tax associated with it, whether the site owner wanted it or not there could be a level playing field. Recording, paying/receiving and accounting for all these link tax micro payments would be another nightmare altogether.

But a WP plugin, that announces and mines crypto coins with a user’s approval and splits the profit with them might work. Corporate wouldn’t like it but employees would just be browsing websites, where’s the harm in that.

Browse a website and share the mined crypto coin with site owner. Sounds fine to me.

Photo Credit(s): Strasburg – European Parliament|Giorgio Barlocco

Crypto News Daily – Telegram cancels ICO…

Photo of Bitcoin, Etherium and Litecoin|QuoteInspector

Hyperloop One in Colorado?

Read a couple of articles last week (TechCrunch, ArsTechnica & Denver Post) about Colorado becoming a winner in the Hyperloop One Global Challenge. The Colorado Department of Transportation (DoT) have joined with Hyperloop One to commission a study on Hyperloop transportation across the front range, from Cheyenne, WY to Pueblo, CO.

There’s been talk forever about adding a passenger train in Colorado from Fort Collins to Pueblo but every time they look at it they can’t make the economics work. How’s this different?

Transportation and the Queen city of the Prairie

Transportation has always been important to Denver. It was the Denver Pacific railroad from Denver to Cheyenne that first linked Denver to the rest of the nation. But even before that there was a stage coach line (Leavenworth & Pike’s Peak Express) that went through Denver to reduce travel time. Denver is currently the largest city within 500 miles and the second only to Phoenix as the most populus city in the mountain west.

Denver International Airport is a major hub and the world’s sixth busiest airport. Denver is a cross road for major north-south and east-west highways through the mountain west. Both the BNSF and Union Pacific railroads serve Denver and Denver is one of the major stops on the Amtrak  passenger train from San Francisco to Chicago.

Why Hyperloop?

Hyperloop can provide much faster travel, even faster than airplanes. Hyperloop can go up to 760 mph (1200 km/h) and should average 600 mph (970 km/h) from point to point

Further, it could potentially require less security.  Hyperloop can go above or below ground. But in either case a terrorist act shouldn’t be as harmful as one on a plane thats traveling at 20 to 30,000 feet in the air.

And because it can go above or below ground it could potentially make use current transportation right of way corridors for building its tubes. Although to go west, it’s going to need a new tunnel or two through the mountains.

Stops along the way

The proposed hyperloop track will bring it through Greeley and as far west as Vail. For a total of 360 miles. Cheyenne to Pueblo have about 10 urban centers between and west of them (Cheyenne, Fort Collins, Greely, Longmont-Boulder, Denver, Denver Tech Center [DTC], West [Denver] metro, Silverthorne/Dillon, Vail, Colorado Springs and Pueblo).

Cheyenne to Pueblo is is 213 miles apart and ~3.5 hr drive with Denver at about the 1/2 way point. With Hyperloop, Denver to either location should take ~10 minutes without stops and the total trip, Cheyenne to Pueblo should be ~21 minutes.

Yes but is there any demand

I would think the way to get a handle on any potential market is to examine airline traffic between these cities. Airplanes can travel at close to these speeds and the costs are public.

But today there’s not much airline traffic between Cheyenne, Denver and Pueblo.  Flights to Vail are mostly seasonal. I could only find one flight from Denver to Cheyenne over a week, one flight between Cheyenne and Pueblo, and 16 flights between Denver and Pueblo. The airplanes used on these trips only holds 9 passengers, so maybe that would amount to a maximum of 162 air travelers a week.

The other approach to estimating potential passengers is to use highway traffic between these destinations. Yes the interstate (I25) from Cheyenne through Denver to Pueblo is constantly busy and needs another lane or two in each direction to handle peak travel. And travel to Vail is very busy during weekends. But how many of these people would be willing to forego a car and travel by Hyperloop?

I travel on tollroads to get to the Denver Airport and it’s a lot faster then traveling non-tollroad highways. But the cost for me is a business expense and it’s not that frequent. These days there’s not much traffic on my tollroad corridor and at rush hour, there’s very few times where one has to slow down. But there are plenty of people coming to the airport each day from the NorthWest and SouthEast Denver suburbs that could use these tollroads but don’t.

And what can you do in Pueblo, Cheyenne or Denver for that matter without a car. It depends on where you end up. The current stops in Denver include the Denver International Airport, DTC, or West Metro (Golden?). Denver, Golden, Boulder, Vail, Greeley and Fort Collins all have compact downtowns with decent transportation. But for the rest of the stops along the way, you will probably want access to a car to get anywhere. There’s always Uber and Left and worst case renting a car.

So maybe Hyperloop would compete for all air travel and some portion of the car travel between along the Cheyenne to Denver to Pueblo. It just may not be large enough.

Other alternative routes

Why stop at Cheyenne, what about Jackson WY or Billings MT? And why Pueblo what about Sante Fe and Albuquerque in NM. And you could conceivably go down to Brownsville, TX and extend up to Calgary and Edmonton in Alberta, Canada, if it made sense. I suppose it’s a question of how many people for what distance.

I would think that going east-west would be more profitable. Say Kansas City to Salt Lake City with Denver in between. With this corridor: 1) the distances are longer (Kansas to Salt Lake is 910 mi [~1465 km]); 2) the metropolitan areas are much larger; and 3) the air travel between them is more popular.

There are currently 10 winners for Hyperloop One’s Global Challenge Contest.  The other routes in the USA include Texas (Dallas, Houston & San Antonio), Florida (Miami to Orlando), & the midwest (Chicago IL to Columbus OH to Pittsburgh PA). But there are others in Canada and Mexico in North America and more in Europe and India.

Hyperloop One will “commit meaningful business and engineering resources and work closely with each of the winning teams/routes to determine their commercial viability.” All this means that each of the winners will be examined professionally to see if it makes economic sense.

Of the 10 winners, Colorado’s route has the least population, almost by a factor of 2. Not sure why we are even in contention, but maybe it’s the ease of building the tubes that makes us a good candidate.

In any case, the public-private partnership has begun to work on the feasibility study.

Comments?

Photo Credit(s): 7 hyperloop facts Elon Musk would love us to know, Detechter

Take a ride on Hyperloop…, Daily Mail

@hyperloop

Mesosphere, Kubernetes and the coming container orchestration consensus

Read a story this past week in TechCrunch, Mesosphere adds Kubernetes support, about how Mesosphere with their own container orchestration software (called Marathon) will now support Google Kubernetes clusters and container orchestration services.

Mesosphere uses their own DC/OS (data center/operating system) to provide service discovery, resource management and networking for container cluster deployments across multiple machines.

DC/OS sounds similar to Kubo discussed in last week’s post, VMworld2017 forecast, cloudy with high chance of containers. Although Kubo was an open source development led by Pivotal to run Kubernetes clusters.

Kubernetes (and Docker) wins

This is indicative of the impact Kubernetes cluster operations is having on the container space.For now, the only holdout in container orchestration without Kubernetes is Docker with their Docker Swarm Engine.

Why add Kubernetes when Mesosphere already had a great container cluster orchestration service? It seems as the container market is maturing, more and more applications are being developed for Kubernetes clusters rather than other container orchestration software.

Although Mesosphere is the current leader in container orchestration both in containers run and revenue (according to their CEO), the move to Kubernetes clusters is likely to accelerate their market adoption/revenues and ultimately help keep them in the lead.

Marathon still lives on

It turns out that Marathon also orchestrates non-container application deployments.

Marathon can also support statefull apps like database machines with persistent storage (unlike Docker containers, stateless apps). These are closer to more typical enterprise applications. This is probably why Mesosphere has done so well up to now.
Marathon also supports both Docker and Mesos containers. Mesos containers depend on Apache Mesos, a specially developed distributed system’s kernel based on Linux for containers.

So Mesosphere will continue to fund development and support for Marathon, even while it rolls out Kubernetes. This will allow them to continue to support their customer base and move them forward into the Kubernetes age.

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I see an eventual need for both stateless and statefull apps in the enterprise data center. And that might just be Mesosphere’s key value proposition – the ability to support apps of the future (containers-stateless) and apps of today (statefull) within the same DC/OS.

Picture credit(s): Enormous container ship by Ruth Hartnup

TPU and hardware vs. software innovation (round 3)

tpu-2At Google IO conference this week, they revealed (see Google supercharges machine learning tasks …) that they had been designing and operating their own processor chips in order to optimize machine learning.

They called the new chip, a Tensor Processing Unit (TPU). According to Google, the TPU provides an order of magnitude more power efficient machine learning over what’s achievable via off the shelf GPU/CPUs. TensorFlow is Google’s open sourced machine learning  software.

This is very interesting, as Google and the rest of the hype-scale hive seem to have latched onto open sourced software and commodity hardware for all their innovation. This has led the industry to believe that hardware customization/innovation is dead and the only thing anyone needs is software developers. I believe this is incorrect and that hardware innovation combined with software innovation is a better way, (see Commodity hardware always loses and Better storage through hardware posts).
Continue reading “TPU and hardware vs. software innovation (round 3)”

Learning to live with lattices or say goodbye to security

safe 'n green by Robert S. Donovan (cc) (from flickr)
safe ‘n green by Robert S. Donovan (cc) (from flickr)

Read an article the other day in Quantum Magazine: A tricky path to quantum encryption about the problems that will occur in current public key cryptology (PKC) schemes when quantum computing emerges over the next five to 30 years.  With advances in quantum computing our current PKC scheme that depends on the difficulty of factoring large numbers will be readily crackable. At that time, all current encrypted traffic, used by banks, the NSA, the internet, etc. will no longer be secure.

NSA, NIST, & ETSI looking at the problem

So there’s a search on for quantum-resistant cryptology (see this release from ETSI [European Telecommunications Standard Institute], this presentation from NIST [{USA} National Institute of Standards &Technology], and this report from Schneier on Security on NSA’s [{USA} National Security Agency] Plans for Post-Quantum world ). There are a number of alternatives being examined by all these groups but the most promising at the moment depends on multi-dimensional (100s of dimensions) mathematical lattices.

Lattices?

According to Wikipedia a lattice is a 3-dimensional space of equidistant points. Apparently, for security reasons, they had to increase the number of dimensions significantly beyond 3.

A secret is somehow inscribed in a route (vector) through this 500-dimensional lattice between two points: an original  point (the public key) in the lattice and another arbitrary point, somewhere nearby in the lattice. The problem from a cryptographic sense is that finding a route, in a 500 dimensional lattice, is a difficult task when you only have one of the points.

But can it be efficient for digital computers of today to use?

So the various security groups have been working on divising efficient algorithms for multi-dimensional public key encryption over the past decade or so. But they have run into a problem.

Originally, the (public) keys for a 500-dimensional lattice PKC were on the order of MBs, so they have been restricting the lattice computations to utilize smaller keys and in effect reducing the complexity of the underlying lattice. But in the process they have now reduced the security of the lattice PKC scheme. So they are having to go back to longer keys, more complex lattices and trying to ascertain which approach leaves communications secure but is efficient enough to implement by digital computers and communications links of today.

Quantum computing

The problem is that quantum computers provide a much faster way to perform certain calculations like factoring a number. Quantum computing can speed up this factorization, by on the order of the square root of a number, as compared to normal digital computing of today.

Its possible that similar quantum computing calculations for lattice routes between points could also be sped up by an equivalent factor.  So even when we all move to lattice based PKC, it’s still possible for quantum computers to crack the code hopefully, it just takes longer.

So the mathematics behind PKC will need to change over the next 5 years or so as quantum computing becomes more of a reality. The hope is that this change will will at least keep our communications secure, at least until the next revolution in computing comes along, or quantum computing becomes even faster than that envisioned today.

Comments?

EMCWorld2015 day 1 news

We are at EMCWorld2015 in Vegas this week. Day 1 was great with new XtremIO 4.0, “The Beast”, new enhanced Data Protection, and a new VCE VxRACK converged infrastructure solution announcements. Somewhere in all the hoopla I saw an all flash VNXe appliance and VMAX3 with a cloud storage tier but these seemed to be just teasers.

XtremIO 4.0

The new hardware provides 40TB per X-brick and with compression/dedupe and the new 8-Xbrick cluster provides 320TB raw or 1.9PB effective capacity. As XtremIO supports 150K mixed IOPS/XBrick, an 8-Xbrick cluster could do 1.2M IOPS or with 250K read IOPS/Xbrick that’s 2.0M IOPS.

XtremIO 4.0 now also includes RecoverPoint integration. (I assume this means they have integrated the write splitter directly into XtremIO that way you don’t need the host version or the switch version of the write splitter.)

The other thing XtremIO 4.0 introduces is non-disruptive upgrades. This means that they can expand or contract the cluster without taking down IO activity.

There was also some mention of better application consistent snapshots, which I suspect means Microsoft VSS integration.

XtremIO 4.0 is a free software upgrade, so the ability to scale up to 8-Xbricks and non-disruptive cluster changes, and RecoverPoint integration can all be added to current XtremIO systems.

Data Protection

EMC introduced a new top end DataDomain hardware appliance the DataDomain 9500, which has 1.5X the performance (58.7TB/hr) and 4X the capacity (1.7PB) of their nearest competitor solution.

They also added a new software feature (from Maginetics) called CloudBoost™.  CloudBoost allows Networker and Avamar to backup to cloud storage. EMC also added Microsoft Ofc365 cloud backup to Spannings previous Google Apps and SalesForce cloud backups.

VMAX3 Protect Point was also enhanced to provide native backup for Oracle, Microsoft SQL Server, and IBM DB2 application environments. ProtectPoint offers a direct path between VMAX3 and  DataDomain appliances and can speed up backup performance by 20X.

EMC also announced Project Falcon which is a virtual appliance version of DataDomain software

VCE VxRACK

This is a rack sized, stack of VSPEX Blue appliances (a VMware EVO:RAIL solution) with new software to bring the VCE useability and data center scale services to a hyper-converged solution. Each appliance is a 2U rack mounted compute intensive or storage intensive unit. The Blue appliances are configureed in a rack for VxRACK and with version 1 you can use VMware or KVM as a chose your own hypervisor. Version 2 will come out later this year and will be based on a complete VMware stack known as EVO: RACK.

Storage services are supplied by EMC ScaleIO. You can purchase a 1/4 rack, 1/2  rack or full rack which includes top of rack networking. You can also scale out by adding more full racks to the system. EMC said that it can technically support 1000s of racks VSPEX Blue appliances for up to ~38PB of storage.

The significant thing is that the VCE VxRACK supplies the VCE customer experience, in a hyper converged solution. However, the focus for VxRACK is tier 2 applications that don’t have a need for the extremely high availability, low response times and high performance of tier 1 applications that run on their VBLOCK solutions (with VNX, VMAX or XtremIO storage).

VMAX3

They had a 5th grader provision an VMAX3 gold storage (LUN) and convert it to a diamond storage (LUN) in 20.48 seconds. It seemed pretty simple to me but the kid blazed through the screens a bit fast for me to see what was going on. It wasn’t nearly as complex as it used to be.

VMAX3 also introduces CloudArray™, which uses FastX storage tiering to cloud storage (using onboard TwinStrata software). This could be used as a tier 3 or 4 storage. EMC also mentioned that you can have an XtremIO (maybe an Xbrick) behind a VMAX3 storage system. VMAX3’s software rewrite has separated data services from backend storage and one can see EMC rolling out different backend storage (like cloud storage or XtremIO) in future offerings.

Other Notes

There was a lot of discussion about the “Information Generation” a new customer for IT services. This is tied to the 3rd platform transformation that’s happening in the industry today. To address this new world IT needs to have 5 attributes:

  1. Predictively spot new opportunities for services/products
  2. Deliver a personalized experience
  3. Innovate in an agile way
  4. Develop trusted programs/apps Demonstrate transparency & trust
  5. Operate in real time

David Goulden talked a lot about what this all means and I encourage you to take a look at the video stream to learn more.

Speaking of video last year was the first year there were more online viewers of EMCWorld than actual participants. So this year EMC upped their game with more entertainment value. The opening dance sequence was pretty impressive.

A lot of talk today was on 3rd platform and the transition from 2nd platform. EMC says their new products are Platform 2.5 which are enablers for 3rd platform. I asked the question what the 3rd platform storage environment looks like and they said scale-out (read ScaleIO) converged storage environment with flash for meta-data/indexing.

As the 3rd platform transforms IT there will be some customers that will want to own the infrastructure, some that will want to use service providers and some that will use public cloud services. EMC’s hope is to capture those customers that want to own it or use service providers.

Tomorrow the focus will be on the Federation with Pivotal and VMware being up for keynotes and other sessions. Stay tuned.