Posts Tagged ‘Power Systems’

Syncsort Finds New Corporate Home and Friend

September 8, 2017

Centerbridge Partners, L.P. a private investment firm, completed the $1.26 billion acquisitions of enterprise software providers Syncsort Incorporated and Vision Solutions, Inc. from affiliates of Clearlake Capital Group, L.P. Clearlake, which acquired Syncsort in 2015 and Vision in 2016, will retain a minority ownership stake in the combined company.

Syncsort is a provider of enterprise software and a player in Big Iron to Big Data solutions. DancingDinosaur has covered it here and here. According to the company, customers in more than 85 countries rely on Syncsort to move and transform mission-critical data and workloads. Vision Solutions provides business resilience tools addressing high availability, disaster recovery, migration, and data sharing for IBM Power Systems.

The company apparently hasn’t suffered from being passed between owners. Syncsort has been active in tech acquisitions for the past two years as it builds its data transformation footprint. Just a couple of weeks ago, it acquired Metron, a provider of cross-platform capacity management software, services. Metron’s signature athene solution delivers trend-based forecasting, capacity modeling, and planning capabilities that enable enterprises to optimize their data infrastructure to improve performance and control costs on premise or in the cloud.

This acquisition is the first since the announcement that Syncsort and Vision Solutions are combining, adding expertise and proven leadership in IBMi and AIX Power Systems platforms and to reinforce its ‘Big Iron to big data’ focus. Syncsort has also long established player in the mainframe business. Its Big Iron to Big Data promises to be a fast-growing market segment comprised of solutions that optimize traditional data systems and deliver mission-critical data from these systems to next-generation analytic environments using innovative Big Data technologies. Metron’s solutions and expertise is expected to contribute to the company’s data infrastructure optimization portfolio.

Syncsort has been on a roll since late in 2016 when, backed by Clearlake, it acquired Trillium Software, a global provider of data quality solutions. The acquisition of Trillium was the largest in Syncsort’s history then, and brings together data quality and data integration technology for enterprise environments. The combination of Syncsort and Trillium, according to the company, enables enterprises to harness all their valuable data assets for greater business insights, applying high-performance and scalable data movement, transformation, profiling, and quality across traditional data management technology stacks as well as Hadoop and cloud environments.

Specifically, Syncsort and Trillium both have a substantial number of large enterprise customers seeking to generate new insights by combining traditional corporate data with diverse information sources from mobile, online, social, and the Internet of Things. Syncsort expects these organizations to continue to rely heavily on next-generation analytic capabilities, creating a growing need for its best-in-class data integration and quality solutions to make their Big Data initiatives successful. Together, Syncsort and Trillium will continue to focus on providing customers with these capabilities for traditional environments, while leading the industry in delivering them for Hadoop and Spark too.

Earlier this year Syncsort integrated its own Big Data integration solution, DMX-h, with Cloudera Director, enabling organizations to easily deploy DMX-h along with Cloudera Enterprise on Amazon Web Services, Microsoft Azure, or Google Cloud. By deploying DMX-h with CDH, organizations can quickly pull data into new, ready-to-work clusters in the cloud—accelerating the time to capture cloud benefits, including cost savings and Data-as-a-Service (DaaS) delivery.

“As organizations liberate data from across the enterprise and deliver it into the cloud, they are looking for a self-service, elastic experience that’s easy to deploy and manage. This is a requirement for a variety of use cases – from data archiving to analytics that combine data originating in the cloud with on premise reference data,” said Tendü Yoğurtçu, Chief Technology Officer.

“By integrating DMX-h with Cloudera Director,” Yoğurtçu continued, “DMX-h is instantly available and ready to put enterprise data to work in newly activated cloud clusters.”

Syncsort DMX-h pulls enterprise data into Hadoop in the cloud and prepares that data for business workloads using native Hadoop frameworks, Apache Spark, or MapReduce, effectively enabling IT to achieve time-to-value goals and quickly deliver business insights.

It is always encouraging to see the mainframe eco-system continue to thrive. IBM’s own performance over the past few years has been anything but encouraging.

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 Power and z Platforms Show Renewed Excitement

June 30, 2017

Granted, 20 consecutive quarters of posting negative revenue numbers is enough to get even the most diehard mainframe bigot down. If you ran your life like that your house and your car would have been seized by the bank months ago.

Toward the end of June, however, both z and Power had some good news. First,  a week ago IBM announced that corporate enterprise users ranked the IBM z  enterprise servers as the most reliable hardware platform available on the market today. In its enterprise server category the survey also found that IBM Power Systems achieved the highest levels of reliability and uptime when compared with 14 server hardware options and 11 server hardware virtualization platforms.

IBM links 2 IBM POWER8 with NVIDIA NVLink with 4 NVIDIA Tesla P100 accelerators

The results were compiled and reported by the ITIC 2017 Global Server Hardware and Server OS Reliability survey, which polled 750 organizations worldwide during April/May 2017. Also among the survey finding:

  • IBM z Systems Enterprise mainframe class systems, had zero percent incidents of more than four hours of per server/per annum downtime of any hardware platform. Specifically, IBM z Systems mainframe class servers exhibit true mainframe fault tolerance experiencing just 0.96 minutes of minutes of unplanned per server, per annual downtime. That equates to 8 seconds per month of “blink and you miss it,” or 2 seconds of unplanned weekly downtime. This is an improvement over the 1.12 minutes of per server/per annum downtime the z Systems servers recorded in ITIC’s 2016 – 2017 Reliability poll nine months ago.
  • IBM Power Systems has the least amount of unplanned downtime, with 2.5 minutes per server/per year of any mainstream Linux server platforms.
  • IBM and the Linux operating system distributions were either first or second in every reliability category, including virtualization and security.

The survey also highlighted market reliability trends. For nearly all companies surveyed, having four nines (99.99%) of availability, equating to less than one hour of system downtime per year was a key factor in its decision.

Then consider the increasing costs of downtime. Nearly all survey respondents claimed that one hour of downtime costs them more than $150k, with one-third estimating that the same will cost their business up to $400k.

With so much activity going on 24×7, for an increasing number of businesses, 4 nines of availability is no longer sufficient.  These businesses are adopting carrier levels of availability; 5 nines or 6 nines (or 99.999 to 99.9999 percent) availability, which translates to downtime per year of 30 seconds (6 nines) or 5 minutes (5 nines) of downtime per year.

According to ITIC’s 2016 report: IBM’s z Enterprise mainframe customers reported the least amount of unplanned downtime and the highest percentage of five nines (99.999%) uptime of any server hardware platform.

Just this week, IBM announced that according to results from International Data Corporation (IDC) Worldwide Quarterly Server Tracker® (June, 2017) IBM exceeded market growth by 3x compared with the total Linux server market, which grew at 6 percent. The improved performance are the result of success across IBM Power Systems including IBM’s OpenPOWER LC servers and IBM Power Systems running SAP HANA as well as the OpenPOWER-Ready servers developed through the OpenPOWER Foundation.

As IBM explains it: Power Systems market share growth is underpinned by solutions that handle fast growing applications, like the deep learning capabilities within the POWER8 architecture. In addition these are systems that expand IBM’s Linux server portfolio, which have been co-developed with fellow members of the OpenPOWER Foundation

Now all that’s needed is IBM’s sales and marketing teams to translate this into revenue. Between that and the new systems IBM has been hinting at for the past year maybe the consecutive quarterly losses might come to an end this year.

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 Puts Open DBaaS on IBM OpenPOWER LC Servers

June 15, 2017

Sometimes IBM seems to be thrashing around looking for anything hot that’s selling, and the various NoSQL databases definitely are hot. The interest is driven by DevOps, cloud, and demand for apps fast.

A month or so ago the company took its Power LC server platform to the OpenPOWER Developer Conference in San Francisco where they pitched Database-as-a-Service (DBaaS) and a price-performance guarantee: OpenPOWER LC servers designed specifically for Big Data to deliver a 2.0x price-performance advantage over x86 for MongoDB and 1.8x for EDB PostgreSQL 9.5 guaranteed. With organizations seeking any performance advantage, these gains matter.

There are enough caveats that IBM will almost never be called to deliver on the guarantee. So, don’t expect to cash in on this very quickly. As IBM says in the miles of fine print: the company will provide additional performance optimization and tuning services consistent with IBM Best Practices, at no charge.  But the guarantee sounds intriguing. If you try it, please let DancingDinosaur know how it works out.

IBM Power System S822LC for Big Data

BTW, IBM published the price for the S822LC for big data as starting at $6,399.00 USD. Price includes shipping. Linux OS, however, comes for an additional charge.

Surprisingly, IBM is not aiming this primarily to the IBM Cloud. Rather, the company is targeting the private cloud, the on-premises local version. Its Open DBaaS toolkit, according to IBM, provides enterprise clients with a turnkey private cloud solution that pre-integrates an Open Source DB image library, OpenStack-based private cloud, and DBaaS software packages with hardware (servers/storage/network switches/rack) and a single source of support to enable a DBaaS self-service portal for enterprise developers and LOB users to provision MongoDB, Postgres, and others in minutes. But since it is built on OpenStack, it also supports hybrid cloud integration with IBM Cloud offerings via OpenStack APIs.

In terms of cost it seems remarkably reasonable. It comes in four reference configurations. The Starter configuration is ~$80k (US list price) and includes 3 Power 822LC servers, pair of network switches, rack, DBaaS Toolkit software, and IBM Lab Services. Other configurations include Entry, Cloud Scale, and Performance configurations that have been specified for additional compute, storage, and OpenStack control plane nodes along with high-capacity JBOD storage drawers. To make this even easier, each configuration can be customized to meet user requirements. Organizations also can provide their own racks and/or network switches.

Furthermore, the Power 822LC and Power 821LC form the key building blocks for the compute, storage and OpenStack control plane nodes. As a bonus, however, IBM includes the new 11-core Power 822LC, which provides an additional 10-15% performance boost over the 10-core Power 822LC for the same price.

This is a package deal, at least if you want the best price and to deploy it fast. “As the need for new applications to be delivered faster than ever increases in a digital world, developers are turning to modern software development models including DevOps, as-a-Service, and self-service to increase the volume, velocity and variety of business applications,” said Terri Virnig, VP, Power Ecosystem and Strategy at IBM. Open Platform for DBaaS on IBM in the announcement. Power Systems DBaaS package  includes:

  • A self-service portal for end users to deploy their choice of the most popular open source community databases including MongoDB, PostgreSQL, MySQL, MariaDB, Redis, Neo4j and Apache Cassandra deployable in minutes
  • An elastic cloud infrastructure for a highly scalable, automated, economical, and reliable open platform for on-premises, private cloud delivery of DBaaS
  • A disk image builder tool for organizations that want to build and deploy their own custom databases to the database image library

An open source, cloud-oriented operations manager with dashboards and tools will help you visualize, control, monitor, and analyze the physical and virtual resources. A turnkey, engineered solution comprised of compute, block and archive storage servers, JBOD disk drawers, OpenStack control plane nodes, and network switches pre-integrated with the open source DBaaS toolkit is available through GitHub here.

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.

 

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 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.

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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