Posts Tagged ‘cognitive computing’

Get a Next-Gen Datacenter with IBM-Nutanix POWER8 System

July 14, 2017

First announced by IBM on May 16 here, this solution, driven by client demand for a simplified hyperconverged—combined server, network, storage, hardware, software—infrastructure, is designed for data-intensive enterprise workloads.  Aimed for companies increasingly looking for the ease of deployment, use, and management that hyperconverged solutions promise. It is being offered as an integrated hardware and software offering in order to deliver on that expectation.

Music made with IBM servers, storage, and infrastructure

IBM’s new POWER8 hyperconverged solutions enable a public cloud-like experience through on-premises infrastructure with top virtualization and automation capabilities combined with Nutanix’s public and on-premises cloud capabilities. They provide a combination of reliable storage, fast networks, scalability and extremely powerful computing in modular, scalable, manageable building blocks that can be scaled simply by adding nodes when needed.

Over time, IBM suggests a roadmap of offerings that will roll out as more configurations are needed to satisfy client demand and as feature and function are brought into both the IBM Cognitive Systems portfolio and the Nutanix portfolio. Full integration is key to the value proposition of this offering so more roadmap options will be delivered as soon as feature function is delivered and integration testing can be completed.

Here are three immediate things you might do with these systems:

  1. Mission-critical workloads, such as databases, large data warehouses, web infrastructure, and mainstream enterprise apps
  2. Cloud native workloads, including full stack open source middleware, enterprise databases
    and containers
  3. Next generation cognitive workloads, including big data, machine learning, and AI

Note, however, the change in IBM’s pricing strategy. The products will be priced with the goal to remain neutral on total cost of acquisition (TCA) to comparable offerings on x86. In short, IBM promises to be competitive with comparable x86 systems in terms of TCA. This is a significant deviation from IBM’s traditional pricing, but as we have started to see already and will continue to see going forward IBM clearly is ready to play pricing flexibility to win the deals on products it wants to push.

IBM envisions the new hyperconverged systems to bring data-intensive enterprise workloads like EDB Postgres, MongoDB and WebSphere into a simple-to-manage, on-premises cloud environment. Running these complex workloads on IBM Hyperconverged Nutanix POWER8 system can help an enterprise quickly and easily deploy open source databases and web-serving applications in the data center without the complexity of setting up all of the underlying infrastructure plumbing and wrestling with hardware-software integration.

And maybe more to IBM’s ultimate aim, these operational data stores may become the foundational building blocks enterprises will use to build a data center capable of taking on cognitive workloads. These ever-advancing workloads in advanced analytics, machine learning and AI will require the enterprise to seamlessly tap into data already housed on premises. Soon expect IBM to bring new offerings to market through an entire family of hyperconverged systems that will be designed to simply and easily deploy and scale a cognitive cloud infrastructure environment.

Currently, IBM offers two systems: the IBM CS821 and IBM CS822. These servers are the industry’s first hyperconverged solutions that marry Nutanix’s one-click software simplicity and scalability with the proven performance of the IBM POWER architecture, which is designed specifically for data-intensive workloads. The IBM CS822 (the larger of the two offerings) sports 22 POWER8 processor cores. That’s 176 compute threads, with up to 512 GB of memory and 15.36 TB of flash storage in a compact server that meshes seamlessly with simple Nutanix Prism management.

This server runs Nutanix Acropolis with AHV and little endian Linux. If IBM honors its stated pricing policy promise, the cost should be competitive on the total cost of acquisition for comparable offerings on x86. DancingDinosaur is not a lawyer (to his mother’s disappointment), but it looks like there is considerable wiggle room in this promise. IBM Hyperconverged-Nutanix Systems will be released for general availability in Q3 2017. Specific timelines, models, and supported server configurations will be announced at the time of availability.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

IBM Resurrects Moore’s Law

June 23, 2017

Guess Moore’s Law ain’t as dead as we were led to believe. On Jun 5 IBM and Research Alliance partners GLOBALFOUNDRIES and Samsung, along with equipment suppliers announced the development of an industry-first process to build silicon nano sheet transistors that will enable 5nm chips. Previously, IBM announced a 7nm process using a silicon germanium (SiGe) alloy.

As DancingDinosaur wrote in early Oct. 2015, the last z System that conformed to the expectations of Moore’s Law was the zEC12, introduced Aug 2012. IBM could boast then it had the fastest commercial processor available.  The subsequent z13 didn’t match it in processor speed.  The z13 chip runs a 22 nm core at 5 GHz, one-half a GHz slower than the zEC12, which ran its 32nm core at 5.5 GHz. IBM compensated for the slower chip speed by adding more processors throughout the system to boost I/O and other functions and optimizing the box every way possible.

5nm silicon nano-sheet transistors delivers 40% performance gain

By 2015, the z13 delivered about a 10 percent performance bump per core thanks to the latest tweaks in the core design, such as better branch prediction and better pipelining. But even at one-half Ghz slower, the z13 was the first system to process 2.5 billion transactions a day.  Even more importantly for enterprise data centers, z13 transactions are persistent, protected, and auditable from end-to-end, adding assurance as mobile transactions grow to an estimated 40 trillion mobile transactions per day by 2025. The z13 also received and continues to receive praise for its industry leading security ratings as well as its scalability and flexibility.

Just recently Hitachi announced a partnership with IBM to develop a version of the z13 to run its own operating system, VOS3. The resulting z13 will run the next generation of Hitachi’s AP series.

But IBM isn’t back in pursuit of Moore’s Law just to deliver faster traditional mainframe workloads. Rather, the company is being driven by its strategic initiatives, mainly cognitive computing. As IBM explained in the announcement: The resulting increase in performance will help accelerate cognitive computing, the Internet of Things (IoT), and other data-intensive applications delivered in the cloud. The power savings could also mean that the batteries in smartphones and other mobile products could last two to three times longer than today’s devices, before needing to be charged.

Scientists working as part of the IBM-led Research Alliance at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering’s NanoTech Complex in Albany, NY achieved the breakthrough by using stacks of silicon nanosheets as the device structure of the transistor instead of the standard FinFET architecture, which is the blueprint for the semiconductor industry up through 7nm node technology. “For business and society to meet the demands of cognitive and cloud computing in the coming years, advancement in semiconductor technology is essential,” said Arvind Krishna, senior vice president, Hybrid Cloud, and director, IBM Research in the announcement. “That’s why IBM aggressively pursues new and different architectures and materials that push the limits of this industry, and brings them to market in technologies like mainframes and our cognitive systems.”

Compared to the leading edge 10nm technology available in the market, according to IBM, a nanosheet-based 5nm technology can deliver 40 percent performance enhancement at fixed power, or 75 percent power savings at matched performance. This improvement enables a significant boost to meeting the future demands of artificial intelligence (AI) systems, virtual reality, and mobile devices.

These may not sound like the workloads you are running on your mainframe now, but systems with these chips are not going to be shipped in the next mainframe either. So, you have a couple of years. The IBM team expects to make progress toward commercializing 7nm in 2018. By the time they start shipping 5nm systems you might be desperate for a machine to run such workloads and others like them.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

IBM Demonstrates Quantum Computing Advantage

May 12, 2017

In an announcement last week, IBM reported that scientists from IBM Research and Raytheon BBN have demonstrated one of the first proven examples of a quantum computer’s advantage over a conventional computer. By probing a black box containing an unknown string of bits, they showed that just a few superconducting qubits can discover the hidden string faster and more efficiently than today’s computers. Their research was published in a paper titled, “Demonstration of quantum advantage in machine learning” in nature.com.

With IBM’s current 5 qubit processor, the quantum algorithm consistently identified the sequence in up to 100x fewer computational steps and was more tolerant of noise than the conventional (non-quantum) algorithm. This is much larger than any previous head-to-head comparison between quantum and conventional processors.

Courtesy: IBM Research

The graphic above defines 3 types of quantum computers. At the top is the quantum annealer, described as the least powerful and most restrictive.  In the middle sits analog quantum, 50-100 qubits, a device able to simulate complex quantum interactions. This will probably be IBM’s next quantum machine; currently IBM offers a 5 qubit device. At the bottom sits the universal quantum. IBM suggests this will scale to over 100,000 qubits and be capable of handling machine learning, quantum chemistry, optimization problems, secure computing, and more. It will be exponentially faster than traditional computers and be able to handle just about all the things even the most powerful conventional supercomputers cannot do now.

The most powerful z System, regardless of how many cores or accelerators or memory or bandwidth, remains a traditional, conventional computer. It deals with problems as a series of basic bits, sequences of 0 or 1. That it runs through these sequences astoundingly fast fools us into thinking that there is something beyond the same old digital computing we have known for the last 50 years or more.

Digital computers see the world and the problems you trying to solve as sequences of 0 and 1. That’s it; there is nothing in-between. They store numbers as sequences of 0 and 1 in memory, and they process stored numbers using only the simplest mathematical operations, add and subtract. As a college student DancingDinosaur was given the most powerful TI programmable calculator then available and, with a few buddies, we tried to come up with things it couldn’t do. No matter how many beer-inspired tries, we never found something it couldn’t handle.  The TI was just another digital device.

Quantum computers can digest 0 and 1 but have a broader array of tricks. For example, contradictory things can exist concurrently. Quantum geeks often cite a riddle dubbed Schrödinger’s cat. In this riddle the cat can be alive and dead at the same time because quantum system can handle multiple, contradictory states. If we had known of Schrödinger’s cat my buddies and I might have stumped that TI calculator.

In an article on supercomputing in Explain That Stuff by Chris Woodford he shows the thinking behind Schrödinger’s cat, called superposition.  This is where two waves, representing a live cat and a dead one, combine to make a third that contains both cats or maybe hundreds of cats. The wave inside the pipe contains all these waves simultaneously: they’re added to make a combined wave that includes them all. Qubits use superposition to represent multiple states (multiple numeric values) simultaneously.

In its latest quantum achievement IBM with only a 5 cubit the quantum algorithm consistently identified the sequence in up to a 100x fewer computational steps and was more tolerant of noise than the conventional (non-quantum) algorithm. This is much larger than any previous head-to-head comparison between quantum and conventional processors.

In effect, the IBM-Raytheon team programmed a black box such that, with the push of a button, it produces a string of bits with a hidden a pattern (such as 0010) for both a conventional computation and a quantum computation. The conventional computer examines the bits one by one. Each result gives a little information about the hidden string. By forcing the conventional computer to query the black box many times it can determine the full answer.

The quantum computer employs a quantum algorithm that extracts the information hidden in the quantum phase — information to which a conventional algorithm is completely blind. The bits are then measured as usual and, in about half the time, the hidden string can be fully revealed.

Most z data centers can’t use quantum capabilities for their daily work, at least not yet. As Woodford noted: It’s very early for the whole field—and most researchers agree that we’re unlikely to see practical quantum computers appearing for many years—perhaps even decades. Don’t bet on it; at the rate IBM is driving this, you’ll probably see useful things much sooner. Maybe tomorrow.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

IBM Shows Off POWER and NVIDIA GPU Setting High Performance Record 

May 4, 2017

The record achievement used 60 Power processors and 120 GPU accelerators to shatter the previous supercomputer record, which used over a 700,000 processors. The results point to how dramatically the capabilities of high performance computing (HPC) has increase while the cost of HPC systems has declined. Or put another way: the effort demonstrates the ability of NVIDIA GPUs to simulate one billion cell models in a fraction of the time, while delivering 10x the performance and efficiency.

Courtesy of IBM: Takes a lot of processing to take you into a tornado

In short, the combined success of IBM and NVIDIA puts the power of cognitive computing within the reach of mainstream enterprise data centers. Specifically the project performed reservoir modeling to predict the flow of oil, water, and natural gas in the subsurface of the earth before they attempt to extract the maximum oil in the most efficient way. The effort, in this case, involved a billion-cell simulation, which took just 92 minutes using 30 for HPC servers equipped with 60 POWER processors and 120 NVIDIA Tesla P100 GPU accelerators.

“This calculation is a very salient demonstration of the computational capability and density of solution that GPUs offer. That speed lets reservoir engineers run more models and ‘what-if’ scenarios than previously,” according to Vincent Natoli, President of Stone Ridge Technology, as quoted in the IBM announcement. “By increasing compute performance and efficiency by more than an order of magnitude, we’re democratizing HPC for the reservoir simulation community,” he added.

“The milestone calculation illuminates the advantages of the IBM POWER architecture for data-intensive and cognitive workloads.” said Sumit Gupta, IBM Vice President, High Performance Computing, AI & Analytics in the IBM announcement. “By running Stone Ridge’s ECHELON on IBM Power Systems, users can achieve faster run-times using a fraction of the hardware.” Gupta continued. The previous record used more than 700,000 processors in a supercomputer installation that occupies nearly half a football field while Stone Ridge did this calculation on two racks of IBM Power Systems that could fit in the space of half a ping-pong table.”

This latest advance challenges perceived misconceptions that GPUs could not be efficient on complex application codes like reservoir simulation and are better suited to simple, more naturally parallel applications such as seismic imaging. The scale, speed, and efficiency of the reported result disprove this misconception. The milestone calculation with a relatively small server infrastructure enables small and medium-size oil and energy companies to take advantage of computer-based reservoir modeling and optimize production from their asset portfolio.

Billion cell simulations in the industry are rare in practice, but the calculation was accomplished to highlight the performance differences between new fully GPU-based codes like the ECHELON reservoir simulator and equivalent legacy CPU codes. ECHELON scales from the cluster to the workstation and while it can simulate a billion cells on 30 servers, it can also run smaller models on a single server or even on a single NVIDIA P100 board in a desktop workstation, the latter two use cases being more in the sweet spot for the energy industry, according to IBM.

As importantly, the company notes, this latest breakthrough showcases the ability of IBM Power Systems with NVIDIA GPUs to achieve similar performance leaps in other fields such as computational fluid dynamics, structural mechanics, climate modeling, and others that are widely used throughout the manufacturing and scientific community. By taking advantage of POWER and GPUs organizations can literally do more with less, which often is an executive’s impossible demand.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

IBM Gets Serious About Open Data Science (ODS) with Anaconda

April 21, 2017

As IBM rapidly ramps up cognitive systems in various forms, its two remaining platforms, z System and POWER, get more and more interesting. This week IBM announced it was bringing the Anaconda Open Data Science (ODS) platform to its Cognitive Systems and PowerAI.

Anaconda, Courtesy Pinterest

Specifically, Anaconda will integrate with the PowerAI software distribution for machine learning (ML) and deep learning (DL). The goal: make it simple and fast to take advantage of Power performance and GPU optimization for data-intensive cognitive workloads.

“Anaconda on IBM Cognitive Systems empowers developers and data scientists to build and deploy deep learning applications that are ready to scale,” said Bob Picciano, senior vice president of IBM Cognitive Systems. Added Travis Oliphant, co-founder and chief data scientist, Continuum Analytics, which introduced the Anaconda platform: “By optimizing Anaconda on Power, developers will also gain access to the libraries in the PowerAI Platform for exploration and deployment in Anaconda Enterprise.”

With more than 16 million downloads to date, Anaconda has emerged as the Open Data Science platform leader. It is empowering leading businesses across industries worldwide with tools to identify patterns in data, uncover key insights, and transform basic data into the intelligence required to solve the world’s most challenging problems.

As one of the fastest growing fields of AI, DL makes it possible to process enormous datasets with millions or even billions of elements and extract useful predictive models. DL is transforming the businesses of leading consumer Web and mobile application companies, and it is catching on with more traditional business.

IBM developed PowerAI to accelerate enterprise adoption of open-source ML and DL frameworks used to build cognitive applications. PowerAI promises to reduce the complexity and risk of deploying these open source frameworks for enterprises on the Power architecture and is tuned for high performance, according to IBM. With PowerAI, organizations also can realize the benefit of enterprise support on IBM Cognitive Systems HPC platforms used in the most demanding commercial, academic, and hyperscale environments

For POWER shops getting into Anaconda, which is based on Python, is straightforward. You need a Power8 with IBM GPU hardware or a Power8 combined with a Nvidia GPU, in effect a Minsky machine. It’s essentially a developer’s tool although ODS proponents see it more broadly, bridging the gap between traditional IT and lines of business, shifting traditional roles, and creating new roles. In short, they envision scientists, mathematicians, engineers, business people, and more getting involved in ODS.

The technology is designed to run on the user’s desktop but is packaged and priced as a cloud subscription with a base package of 20 users. User licenses range from $500 per year to $30,000 per year depending on which bells and whistles you include. The number of options is pretty extensive.

According to IBM, this started with PowerAI to accelerate enterprise adoption of open-source ML/DL learning frameworks used to build cognitive applications. Overall, the open Anaconda platform brings capabilities for large-scale data processing, predictive analytics, and scientific computing to simplify package management and deployment. Developers using open source ML/DL components can use Power as the deployment platform and take advantage of Power optimization and GPU differentiation for NVIDIA.

Not to be left out, IBM noted growing support for the OpenPOWER Foundation, which recently announced the OpenPOWER Machine Learning Work Group (OPMLWG). The new OPMLWG includes members like Google, NVIDIA and Mellanox to provide a forum for collaboration that will help define frameworks for the productive development and deployment of ML solutions using OpenPOWER ecosystem technology. The foundation has also surpassed 300-members, with new participants such as Kinetica, Red Hat, and Toshiba. For traditional enterprise data centers, the future increasingly is pointing toward cognitive in one form or another.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

Compuware-Syncsort-Splunk to Boost Mainframe Security

April 6, 2017

The mainframe has proven to be remarkably secure over the years, racking up the highest security certifications available. But there is still room for improvement. Earlier this week Compuware announced Application Audit, a software tool that aims to transform mainframe cybersecurity and compliance through real-time capture of user behavior.

Capturing user behavior, especially in real-time, is seemingly impossible if you have to rely on the data your collect from the various logs and SMF data.  Compuware’s solution, Application Audit, in conjunction with Syncsort and Splunk, fully captures and analyzes start-to-finish mainframe application user behavior.

As Compuware explains: Most enterprises still rely on disparate logs and SMF data from security products such as RACF, CA-ACF2 and CA-Top Secret to piece together user behavior.  This is too slow if you want to capture bad behavior while it’s going on. Some organization try to apply analytics to these logs but that also is too slow. By the time you have collected enough logs to deduce who did what and when the damage may have been done.  Throw in the escalating demands of cross-platform enterprise cybersecurity and increasingly burdensome global compliance mandates you haven’t a chance without an automated tool optimized for this.

Fortunately, the mainframe provides rich and comprehensive session data you can run through and analyze with Application Audit and in conjunction with the organization’s security information and event management (SIEM) systems to more quickly and effectively see what really is happening. Specifically, it can:

  • Detect, investigate, and respond to inappropriate behavior by internal users with access
  • Detect, investigate, and respond to hacked or illegally accessed user accounts
  • Support criminal/legal investigations with complete and credible forensics
  • Fulfill compliance mandates regarding protection of sensitive data

IBM, by the way, is not ignoring the advantages of analytics for z security.  Back in February you read about IBM bringing its cognitive system to the z on DancingDinosaur.  IBM continues to flog cognitive on z for real-time analytics and security; promising to enable faster customer insights, business insights, and systems insights with decisions based on real-time analysis of both current and historical data delivered on an analytics platform designed for availability, optimized for flexibility, and engineered with the highest levels of security. Check out IBM’s full cognitive for z pitch.

The data Compuware and Syncsort collect with Application Audit is particularly valuable for maintaining control of privileged mainframe user accounts. Both private- and public-sector organizations are increasingly concerned about insider threats to both mainframe and non-mainframe systems. Privileged user accounts can be misused by their rightful owners, motivated by everything from financial gain to personal grievances, as well as by malicious outsiders who have illegally acquired the credentials for those accounts. You can imagine what havoc they could wreak.

In addition, with Application Audit Compuware is orchestrating a number of players to deliver the full security picture. Specifically, through collaboration with CorreLog, Syncsort and Splunk, Compuware is enabling enterprise customers to integrate Application Audit’s mainframe intelligence with popular SIEM solutions such as Splunk, IBM QRadar, and HPE Security ArcSight ESM. Additionally, Application Audit provides an out-of-the-box Splunk-based dashboard that delivers value from the start. As Compuware explains, these integrations are particularly useful for discovering and addressing security issues associated with today’s increasingly common composite applications, which have components running on both mainframe and non-mainframe platforms. SIEM integration also ensures that security, compliance and other risk management staff can easily access mainframe-related data in the same manner as they access data from other platforms.

“Effective IT management requires effective monitoring of what is happening for security, cost reduction, capacity planning, service level agreements, compliance, and other purposes,” noted Stu Henderson, Founder and President of the Henderson Group in the Compuware announcement. “This is a major need in an environment where security, technology, budget, and regulatory pressures continue to escalate.”

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

 

IBM Changes the Economics of Cloud Storage

March 31, 2017

Storage tiering used to be simple: active data went to your best high performance storage, inactive data went to low cost archival storage, and cloud storage filled in for one or whatever else was needed. Unfortunately, today’s emphasis on continuous data analytics, near real-time predictive analytics, and now cognitive has complicated this picture and the corresponding economics of storage.

In response, last week IBM unveiled new additions to the IBM Cloud Object Storage family. The company is offering clients new choices for archival data and a new pricing model to more easily apply intelligence to unpredictable data patterns using analytics and cognitive tools.

Analytics drive new IBM cloud storage pricing

By now, line of business (LOB) managers, having been exhorted to leverage big data and analytics for years, are listening. More recently, the analytics drumbeat has expanded to include not just big data but sexy IoT, predictive analytics, machine learning, and finally cognitive science. The idea of keeping data around for a few months and parking it in a long term archive to never be looked at again until it is finally deleted permanently just isn’t happening as it was supposed to (if it ever did). The failure to permanently remove expired data can become costly from a storage standpoint as well as risky from an e-discovery standpoint.

IBM puts it this way: Businesses typically have to manage across three types of data workloads: “hot” for data that’s frequently accessed and used; “cool” for data that’s infrequently accessed and used; and “cold” for archival data. Cold storage is often defined as cheaper but slower. For example, if a business uses cold storage, it typically has to wait to retrieve and access that data, limiting the ability to rapidly derive analytical or cognitive insights. As a result, there is a tendency to store data in more expensive hot storage.

IBM’s new cloud storage offering, IBM Cloud Object Storage Flex (Flex), uses a “pay as you use” model of storage tiers potentially lowering the price by 53 percent compared to AWS S3 IA1 and 75 percent compared to Azure GRS Cool Tier.2 (See footnotes at the bottom of the IBM press release linked to above. However IBM is not publishing the actual Flex storage prices.) Flex, IBM’s new cloud storage service, promises simplified pricing for clients whose data usage patterns are difficult to predict. Flex promises organizations will benefit from the cost savings of cold storage for rarely accessed data, while maintaining high accessibility to all data.

Of course, you could just lower the cost of storage by permanently removing unneeded data.  Simply insist that the data owners specify an expiration date when you set up the storage initially. When the date arrives in 5, 10, 15 years automatically delete the data. At least that’s how I was taught eons ago. Of course storage costs orders of magnitude less now although storage volumes are orders of magnitude greater and near real-time analytics weren’t in the picture.

Without the actual rates for the different storage tiers you cannot determine how much Storage Flex may save you.  What it will do, however, is make it more convenient to perform analytics on archived data you might otherwise not bother with.  Expect this issue to come up increasingly as IoT ramps up and you are handling more data that doesn’t need hot storage beyond the first few minutes of its arrival.

Finally, the IBM Cloud Object Storage Cold Vault (Cold Vault) service gives clients access to cold storage data on the IBM Cloud and is intended to lead the category for cold data recovery times among its major competitors. Cold Vault joins its existing Standard and Vault tiers to complete a range of IBM cloud storage tiers that are available with expanded expertise and methods via Bluemix and through the IBM Bluemix Garages.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

Open POWER-Open Compute-POWER9 at Open Compute Summit

March 16, 2017

Bryan Talik, President, OpenPOWER Foundation provides a detailed rundown on the action at the Open Compute  Summit held last week in Santa Clara. After weeks of writing about Cognitive, Machine Learning, Blockchain, and even quantum computing, it is a nice shift to conventional computing platforms that should still be viewed as strategic initiatives.

The OpenPOWER, Open Compute gospel was filling the air in Santa Clara.  As reported, Andy Walsh, Xilinx Director of Strategic Market Development and OpenPOWER Foundation Board member explained, “We very much support open standards and the broad innovation they foster. Open Compute and OpenPOWER are catalysts in enabling new data center capabilities in computing, storage, and networking.”

Added Adam Smith, CEO of Alpha Data:  “Open standards and communities lead to rapid innovation…We are proud to support the latest advances of OpenPOWER accelerator technology featuring Xilinx FPGAs.”

John Zannos, Canonical OpenPOWER Board Chair chimed in: For 2017, the OpenPOWER Board approved four areas of focus that include machine learning/AI, database and analytics, cloud applications and containers. The strategy for 2017 also includes plans to extend OpenPOWER’s reach worldwide and promote technical innovations at various academic labs and in industry. Finally, the group plans to open additional application-oriented workgroups to further technical solutions that benefits specific application areas.

Not surprisingly, some members even see collaboration as the key to satisfying the performance demands that the computing market craves. “The computing industry is at an inflection point between conventional processing and specialized processing,” according to Aaron Sullivan, distinguished engineer at Rackspace. “

To satisfy this shift, Rackspace and Google announced an OCP-OpenPOWER server platform last year, codenamed Zaius and Barreleye G2.  It is based on POWER9. At the OCP Summit, both companies put on a public display of the two products.

This server platform promises to improve the performance, bandwidth, and power consumption demands for emerging applications that leverage machine learning, cognitive systems, real-time analytics and big data platforms. The OCP players plan to continue their work alongside Google, OpenPOWER, OpenCAPI, and other Zaius project members.

Andy Walsh, Xilinx Director of Strategic Market Development and OpenPOWER Foundation Board member explains: “We very much support open standards and the broad innovation they foster. Open Compute and OpenPOWER are catalysts in enabling new data center capabilities in computing, storage, and networking.”

This Zaius and Barreleye G@ server platforms promise to advance the performance, bandwidth and power consumption demands for emerging applications that leverage the latest advanced technologies. These latest technologies are none other than the strategic imperatives–cognitive, machine learning, real-time analytics–IBM has been repeating like a mantra for months.

Open Compute Projects also were displayed at the Summit. Specifically, as reported: Google and Rackspace, published the Zaius specification to Open Compute in October 2016, and had engineers to explain the specification process and to give attendees a starting point for their own server design.

Other Open Compute members, reportedly, also were there. Inventec showed a POWER9 OpenPOWER server based on the Zaius server specification. Mellanox showcased ConnectX-5, its next generation networking adaptor that features 100Gb/s Infiniband and Ethernet. This adaptor supports PCIe Gen4 and CAPI2.0, providing a higher performance and a coherent connection to the POWER9 processor vs. PCIe Gen3.

Others, reported by Talik, included Wistron and E4 Computing, which showcased their newly announced OCP-form factor POWER8 server. Featuring two POWER8 processors, four NVIDIA Tesla P100 GPUs with the NVLink interconnect, and liquid cooling, the new platform represents an ideal OCP-compliant HPC system.

Talik also reported IBM, Xilinx, and Alpha Data showed their line ups of several FPGA adaptors designed for both POWER8 and POWER9. Featuring PCIe Gen3, CAPI1.0 for POWER8 and PCIe Gen4, CAPI2.0 and 25G/s CAPI3.0 for POWER9 these new FPGAs bring acceleration to a whole new level. OpenPOWER member engineers were on-hand to provide information regarding the CAPI SNAP developer and programming framework as well as OpenCAPI.

Not to be left out, Talik reported that IBM showcased products it previously tested and demonstrated: POWER8-based OCP and OpenPOWER Barreleye servers running IBM’s Spectrum Scale software, a full-featured global parallel file system with roots in HPC and now widely adopted in commercial enterprises across all industries for data management at petabyte scale.  Guess compute platform isn’t quite the dirty phrase IBM has been implying for months.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

IBM Launches New IoT Collaborative Initiative

February 23, 2017

Collaboration partners can pull hundreds of millions of dollars in new revenue from IoT, according to IBM’s recent IoT announcement. Having reached what it describes as a tipping point with IoT innovation the company now boasts of having over 6,000 clients and partners around the world, many of whom are now wanting to join in its new global Watson IoT center to co-innovate. Already Avnet, BNP Paribas, Capgemini, and Tech Mahindra will collocate development teams at the IBM Munich center to work on IoT collaborations.

new-ibm-watson-iot-center

IBM Opens New Global Center for Watson IoT

The IBM center also will act as an innovation space for the European IoT standards organization EEBus.  The plan, according to Harriet Green, General Manager, IBM Watson IoT, Cognitive Engagement and Education (pictured above left), calls for building a new global IoT innovation ecosystem that will explore how cognitive and IoT technologies will transform industries and our daily lives.

IoT and more recently cognitive are naturals for the z System, and POWER Systems have been the platform for natural language processing and cognitive since Watson won Jeopardy three years ago. With the latest enhancements IBM has brought to the z in the form of on-premises cognitive and machine learning the z should assume an important role as it gathers, stores, collects, and processes IoT data for cognitive analysis. DancingDinosaur first reported on this late in 2014 and again just last week. As IoT and cognitive workloads ramp up on z don’t be surprised to see monthly workload charges rise.

Late last year IBM announced that car maker BMW will collocate part of its research and development operations at IBM’s new Watson IoT center to help reimagine the driving experience. Now, IBM is announcing four more companies that have signed up to join its special industry “collaboratories” where clients and partners work together with 1,000 Munich-based IBM IoT experts to tap into the latest design thinking and push the boundaries of the possible with IoT.

Let’s look at the four newest participants starting with Avnet. According to IBM, an IT distributor and global IBM partner, Avnet will open a new joint IoT Lab within IBM’s Watson IoT HQ to develop, build, demonstrate and sell IoT solutions powered by IBM Watson. Working closely with IBM’s leading technologists and IoT experts, Avnet also plans to enhance its IoT technical expertise through hands-on training and on-the-job learning. Avnet’s team of IoT and analytics experts will also partner with IBM on joint business development opportunities across multiple industries including smart buildings, smart homes, industry, transportation, medical, and consumer.

As reported by BNP Paribas, Consorsbank, its retail digital bank in Germany, will partner with IBM´s new Watson IoT Center. The company will collocate a team of solution architects, developers and business development personnel at the Watson facility. Together with IBM’s experts, they will explore how IoT and cognitive technologies can drive transformation in the banking industry and help innovate new financial products and services, such as investment advice.

Similarly, global IT consulting and technology services provider Capgemini will collocate a team of cognitive IoT experts at the Watson center. Together they will help customers maximize the potential of Industry 4.0 and develop and take to market sector-specific cognitive IoT solutions. Capgemini plans a close link between its Munich Applied Innovation Exchange and IBM’s new Customer Experience zones to collaborate with clients in an interactive environment.

Finally, the Indian multinational provider of enterprise and communications IT and networking technology Tech Mahindra, is one of IBM’s Global System Integrators with over 3,000 specialists focused on IBM technology around the world. The company will locate a team of six developers and engineers within the Watson IoT HQ to help deliver on Tech Mahindra’s vision of generating substantial new revenue based on IBM’s Watson IoT platform. Tech Mahindra will use the center to co-create and showcase new solutions based on IBM’s Watson IoT platform for Industry 4.0 and Manufacturing, Precision Farming, Healthcare, Insurance and Banking, and automotive.

To facilitate connecting the z to IoT IBM offers a simple recipe. It requires 4 basic ingredients and 4 steps: Texas Instrument’s SensorTag, a Bluemix account, IBM z/OS Connect Enterprise Edition, and a back-end service like CICS.  Start by exposing an existing z Systems application as a RESTful AP. This is where the z/OS Connect Edition comes in.  Then enable your SensorTag device to Watson IoT Quick Start. From there connect the Cloud to your on-premises Hybrid Cloud.  Finally, enable the published IoT data to trigger a RESTful API. Sounds pretty straightforward but—full disclosure—Dancing Dinosaur has not tried it due to lacking the necessary pieces. If you try it, please tell DancingDinosaur how it works (info@radding.net). Good luck.

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 


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