Posts Tagged ‘IBM’

IBM Insists Storage is Generating Positive Revenue

May 19, 2017

At a recent quarterly briefing on the company’s storage business, IBM managers crowed over its success: 2,000 new Spectrum Storage customers, 1,300 new DS8880 systems shipped, 1500 PB of capacity shipped, 7% revenue gain Q1’17. This appeared to contradict yet another consecutive losing quarter in which only IBM’s Cognitive Solutions (includes Solutions Software and Transaction Processing Software) posted positive revenue.

However, Martin Schroeter, Senior Vice President and Chief Financial Officer (1Q’17 financials here), sounded upbeat about IBM storage in the quarterly statement: Storage hardware was up seven percent this quarter, led by double-digit growth in our all-flash array offerings. Flash contributed to our Storage revenue growth in both midrange and high-end. In storage, we continue to see the shift in value towards software-defined environments, where we continue to lead the market. We again had double-digit revenue growth in Software-Defined Storage, which is not reported in our Systems segment. Storage software now represents more than 40 percent of our total storage revenue.

IBM Flash System A9000

Highly parallel all-flash storage for hyperscale and cloud data centers

Schroeter continued: Storage gross margins are down, as hardware continues to be impacted by price pressure. To summarize Systems, our revenue and gross profit performance were driven by expected cycle declines in z Systems and Power, mitigated by Storage revenue growth. We continue to expand our footprint and add new capabilities, which address changing workloads. While we are facing some shifting market dynamics and ongoing product transitions, our portfolio remains uniquely optimized for cognitive and cloud computing.

DancingDinosaur hopes he is right.  IBM has been signaling a new z System coming for months, along with enhancements to Power storage. Just two weeks ago IBM reported achievements with Power and Nvidia, as DancingDinosaur covered at that time.

If there was any doubt, all-flash storage is the way IBM and most other storage providers are heading for the performance and competitive economics. In January IBM announced three all flash DS888* all flash products, which DancingDinosaur covered at the time here. Specifically:

  • DS8884 F (the F designates all flash)—described by IBM as performance delivered within a flexible and space-saving package
  • DS8886 F—combines performance, capacity, and cost to support a variety of workloads and applications
  • DS8888 F—promises performance and capacity designed to address the most demanding business workload requirements

The three products are intended to provide the speed and reliability needed for workloads ranging from enterprise resource planning (ERP) and financial transactions to cognitive applications like machine learning and natural language processing. Doubt that a lot of mainframe data centers are doing much with cognitive systems yet, but that will be coming.

Spectrum Storage also appears to be looming large in IBM’s storage plans. Spectrum Storage is IBM’s software defined storage (SDS) family of products. DancingDinosaur covered the latest refresh of the suite of products this past February.

The highlights of the recent announcement included the addition of Cloud Object Storage and a version of Spectrum Virtualize as software only.  Spectrum Control got a slew of enhancements, including new cloud-based storage analytics for Dell EMC VNX, VNXe, and VMAX; extended capacity planning views for external storage, and transparent cloud tiering for IBM Spectrum Scale.  The on-premises editions added consolidated chargeback/showback and support for Dell EMC VNXe file storage. This should make it clear that Spectrum Storage is not only for underlying IBM storage products.

Along the same lines, Spectrum Storage added VMware 6 support and the certified vSphere Web client. In the area of cloud object storage, IBM added native NFS access, enhance STaaS multi-tenancy, IPV6 support, and preconfigured bundles.

IBM also previewed enhancements coming in 2Q’17.   Of specific interest to DancingDinosaur readers will likely be  the likely updates to the FlashSystem and VeraStack portfolio.

The company is counting on these enhancements and more to help pull IBM out of its tailspin. As Schroeter wrote in the 1Q’17 report: New systems product introductions later in the year will drive improved second half performance as compared to the first. Hope so; already big investors are cashing out. Clients, however, appear to be staying for now.

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.

 

IBM Spotlights Blockchain and Hyperledger Fabric at IBM InterCONNECT

March 23, 2017

IBM announced earlier this week Hyperledger Fabric v 1.0 beta, with security for regulated industries, governance tools, and over 1,000 transactions per second possible.  This is represents the first enterprise-ready blockchain service based on the Linux Foundation’s open source Hyperledger Fabric version 1.0. The service enables developers to quickly build and host security-rich production blockchain networks on the IBM Cloud and underpinned by IBM LinuxONE.

Maersk and IBM transform global trade with blockchain

LinuxONE, a dedicated z-based Linux system with as much security as any commercial platform is likely to have, should play a central role in blockchain networks. The machine also delivers all the itys the z is renowned for: scalability, availability, flexibility, manageability, and more.

The Linux Foundation’s open source Hyperledger Fabric v1.0 is being developed by members of the Hyperledger consortium alongside other open source blockchain technologies. The Hyperledger consortium’s Technical Steering Committee recently promoted Fabric from incubator to active state, and it is expected to be available in the coming weeks. It is designed to provide a framework for enterprise-grade blockchain networks that can transact at over 1,000 transactions per second.

Safety and security is everything with blockchain, which means blockchain networks are only as safe as the infrastructures on which they reside, hence the underpinning on LinuxONE. In addition, IBM’s High Security Business Network brings an extremely secure Linux infrastructure that, according to IBM, integrates security from the hardware up through the software stack, specifically designed for enterprise blockchains by providing:

  • Protection from insider attacks – helps safeguard entry points on the network and fight insider threats from anyone with system administrator credentials
  • The industry’s highest certified level of isolation for a commercial system- Evaluation Assurance Level certification of EAL5+ is critical in highly regulated industries such as government, financial services and healthcare, to prevent the leakage of information from one party’s environment to another
  • Secure Service Containers – to help protect code throughout the blockchain application and effectively encapsulating the blockchain into a virtual appliance, denying access even to privileged users
  • Tamper-responsive hardware security modules –to protect encrypted data for storage of cryptographic keys. These modules are certified to FIPS 140-2 Level 4, the highest level of security certification available for cryptographic modules
  • A highly auditable operating environment – comprehensive , immutable log data supports forensics, audit, and compliance

IBM also announced today the first commercially available blockchain governance tools, and new open-source developer tools that automate the steps it takes to build with the Hyperledger Fabric, reportedly speeding the process from weeks to days.

The new blockchain governance tools also make it easy to set up a blockchain network and assign roles and levels of visibility from a single dashboard. They help network members set rules, manage membership, and enforce network compliance once the network is up and running.

This seems straightforward enough. Once setup is initiated, members can determine the rules of the blockchain and share consent when new members request to join the network. In addition, the deployment tool assigns each network a Network Trust Rating of 1 to 100. New network members can view this before joining and determine whether or not they can trust the network enough to participate. Organizations can also take steps to improve their Trust Ratings before moving into production.

To make it easier for developers to translate business needs from concept to actual code, IBM Blockchain includes a new open-source developer tools for the Hyperledger Fabric called Fabric Composer. Fabric Composer promises to help users model business networks, create APIs that integrate with the blockchain network and existing systems of record, and quickly build a user interface. Fabric Composer also automates tasks that traditionally could take weeks, allowing developers to complete them in minutes instead.

IBM Blockchain for Hyperledger Fabric v1.0 is now available through a beta program on IBM Bluemix. Hyperledger Fabric also is available on Docker Hub as an IBM-certified image available for download at no cost.

At this point, IBM has over 25 publicly named Blockchain projects underway. They address everything from carbon asset management to consumer digital ID, post trade derivatives processing, last mile shipping, supply chain food safety, provenance, securities lending, and more seemingly are being added nearly weekly.

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 Introduces First Universal Commercial Quantum Computers

March 9, 2017

A few years ago DancingDinosaur first encountered the possibility of quantum computing. It was presented as a real but distant possibility. This is not something I need to consider I thought at the time.  By the time it is available commercially I will be long retired and probably six feet under. Well, I was wrong.

This week IBM unveiled its IBM Q quantum systems. IBM Q will be leading Watson and blockchain to deliver the most advanced set of services on the IBM Cloud platform. There are organizations using it now, and DancingDinosaur continues to be living and working still.

IBM Quantum Computing scientists Hanhee Paik (left) and Sarah Sheldon (right) examine the hardware inside an open dilution fridge at the IBM Q Lab

As IBM explains: While technologies that currently run on classical (or conventional) computers, such as Watson, can help find patterns and insights buried in vast amounts of existing data, quantum computers will deliver solutions to multi-faceted problems where patterns cannot be seen because the data doesn’t exist and the possibilities that you need to explore are too enormous to ever be processed by conventional computers.

Just don’t retire your z or Power system in favor on an IBM Q yet. As IBM explained at a recent briefing on the quantum computing the IBM Q universal quantum computers will be able to do any type of problem that conventional computers do today. However, many of today’s workloads, like on-line transaction processing, data storage, and web serving will continue to run more efficiently on conventional systems. The most powerful quantum systems of the next decade will be a hybrid of quantum computers with conventional computers to control logic and operations on large amounts of data.

The most immediate use cases will involve molecular dynamics, drug design, and materials. The new quantum machine, for example, will allow the healthcare industry to design more effective drugs faster and at less cost and the chemical industry to develop new and improved materials.

Another familiar use case revolves around optimization in finance and manufacturing. The problem here comes down to computers struggling with optimization involving an exponential number of possibilities. Quantum systems, noted IBM, hold the promise of more accurately finding the most profitable investment portfolio in the financial industry, the most efficient use of resources in manufacturing, and optimal routes for logistics in the transportation and retail industries.

To refresh the basics of quantum computing.  The challenges invariably entail exponential scale. You start with 2 basic ideas; 1) the uncertainty principle, which states that attempting to observe a state in general disturbs it while obtaining only partial information about the state. Or 2) where two systems can exist in an entangled state, causing them to behave in ways that cannot be explained by supposing that each has some state of its own. No more zero or 1 only.

The basic unit of quantum computing is the qubit. Today IBM is making available a 5 qubit system, which is pretty small in the overall scheme of things. Large enough, however, to experiment and test some hypotheses; things start getting interesting at 20 qubits. An inflexion point, IBM researchers noted, occurs around 50 qubits. At 50-100 qubits people can begin to do some serious work.

This past week IBM announced three quantum computing advances: the release of a new API for the IBM Quantum Experience that enables developers and programmers to begin building interfaces between IBM’s existing 5 qubit cloud-based quantum computer and conventional computers, without needing a deep background in quantum physics. You can try the 5 qubit quantum system via IBM’s Quantum Experience on Bluemix here.

IBM also released an upgraded simulator on the IBM Quantum Experience that can model circuits with up to 20 qubits. In the first half of 2017, IBM plans to release a full SDK on the IBM Quantum Experience for users to build simple quantum applications and software programs. Only the publically available 5 qubit quantum system with a web-based graphical user interface now; soon to be upgraded to more qubits.

 IBM Research Frontiers Institute allows participants to explore applications for quantum computing in a consortium dedicated to making IBM’s most ambitious research available to its members.

Finally, the IBM Q Early Access Systems allows the purchase of access to a dedicated quantum system hosted and managed by IBM. Initial system is 15+ qubits, with a fast roadmap promised to 50+ qubits.

“IBM has invested over decades to growing the field of quantum computing and we are committed to expanding access to quantum systems and their powerful capabilities for the science and business communities,” said Arvind Krishna, senior vice president of Hybrid Cloud and director for IBM Research. “We believe that quantum computing promises to be the next major technology that has the potential to drive a new era of innovation across industries.”

Are you ready for quantum computing? Try it today on IBM’s Quantum Experience through Bluemix. Let me know how it works for you.

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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