Posts Tagged ‘System z’

IBM z System After Moore’s Law

October 2, 2015

The last z System that conformed to the expectations of Moore’s Law was the zEC12. IBM could boast that 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. Did you even notice?

third dimension chip

In 2007 an IBM scientist holds a 3-D integrated stacked chip

In 2015, the z13 delivers 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 one-half a 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.

IBM clearly isn’t bemoaning the decline of Moore’s Law. In fact, it has been looking beyond silicon for the processing of the future.  This week it announced a major engineering breakthrough that could accelerate carbon nanotubes for the replacement of silicon transistors to power future computing. The breakthrough allows a new way to shrink transistor contacts without reducing the performance of carbon nanotube devices, essentially opening a path to dramatically faster, smaller, and more powerful computer chips beyond the capabilities of traditional semiconductors. Guess we can stop worrying about Moore’s Law.

Without Moore’s Law, IBM optimized just about everything on the z13 that could be optimized. It provides 320 separate channels dedicated to drive I/O throughput as well as such performance goodies as simultaneous multithreading (SMT), symmetric multiprocessing (SMP), and single instruction, multiple data (SIMD). Overall about 600 processors (in addition to your configurable cores) speed and streamline processes throughout the machine. Moore’s Law, in effect, has been bypassed. As much as the industry enjoyed the annual doubling of capacity and corresponding lower price/performance it doesn’t need Moore’s Law to meet today’s insatiable demand for processing power.

The company will be doing similar things with the POWER processor. Today we have the POWER8. Coming is the POWER9 followed by the POWER10. The POWER9 reportedly will arrive in 2017 at 14nm, feature a new micro-architecture, and be optimized with CAPI and NVLINK. POWER10, reportedly, arrives around 2020 optimized for extreme analytics.

As IBM explains its latest breakthrough, carbon nanotubes represent a new class of semiconductor materials that consist of single atomic sheets of carbon rolled up into a tube. The carbon nanotubes form the core of a transistor device whose superior electrical properties promise several generations of technology scaling beyond the physical limits of silicon.

The new processor technology, IBM reports, overcomes a major hurdle that silicon and any other semiconductor transistor technologies face when scaling down. In the transistor, two things scale: the channel and its two contacts. As devices become smaller, the increased contact resistance of carbon nanotubes hindered performance gains. The latest development could overcome contact resistance all the way to the 1.8 nanometer node – four technology generations away.

Carbon nanotube chips could greatly improve the capabilities of high performance computers, enabling, for example, big data to be analyzed faster, increasing the power and battery life of mobile devices, and allowing cloud data centers to deliver services more efficiently and economically. Even cognitive computing and Internet of Things can benefit.

Until now, vendors have be able to shrink the silicon transistors, but they are approaching a point of physical limitation, which is why Moore’s Law is running out of steam. Previously, IBM demonstrated that carbon nanotube transistors can operate as effective switches at channel dimensions of less than ten nanometers. IBM’s new contact approach overcomes the contact resistance by incorporating carbon nanotubes into semiconductor devices, which could result in smaller chips with greater performance and lower power consumption.

As transistors shrink in size, electrical resistance within the contacts increases, which limits performance. To overcome this resistance, IBM researchers gave up traditional contact schemes and created a metallurgical process akin to microscopic welding that chemically binds the metal atoms to the carbon atoms at the ends of nanotubes. This end-bonded contact scheme allows the contacts to be shrunken below 10 nanometers without impacting performance. This brings the industry a step closer to the goal of a carbon nanotube technology within the decade, says IBM.

Let’s hope this works as expected. If not, IBM has other possibilities already in its research labs. DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at and here.

IBM Ranked #1 in Midrange Servers and Enterprise Network Storage

August 13, 2015

Although the financial markets may be beating up IBM the technology world continues to acclaim IBM technology and products. Most recently, IBM ranked on top in the CRN Annual Report Card (ARC) Survey recognizing the best-in-class vendors in the categories of partnership, support, and product innovation.  But the accolades don’t stop there.

Mobile Security Infographic

Courtesy of IBM (click to enlarge)

IBM was named a leader in four key cloud services categories—hosting, overall cloud professional services, cloud consulting services, and systems integration—by the independent technology market research firm Technology Business Research, Inc. (TBR).  This summer Gartner also named IBM as a leader in Security Information and Event Management (SIEM) in the latest Gartner Magic Quadrant for SIEM, this for the seventh consecutive year. Gartner also named IBM as a Leader in the 2015 Magic Quadrant for Mobile Application Development Platforms, specifically calling out the IBM MobileFirst Platform.

The CRN award addresses the technology channel. According to IBM, the company and its business partners are engaging with clients in new ways to work, building the infrastructure, and deploying innovative solutions for the digital era.  This should come as no surprise to anyone reading this blog; the z 13 was designed expressly to be a digital platform for the cloud, mobile, and big data era.  IBM’s z and Power Systems servers and Storage Solutions specifically were designed to address the challenges these areas present.

Along the same lines, IBM’s commitment to open alliances has continued this year unabated, starting with its focus on innovation platforms designed for big data and superior cloud economics, which continue to be the cornerstone of IBM Power System. The company also plays a leading role in the Open Power Foundation, the Linux Foundation as well as ramping up communities around the Internet of Things, developerWorks Recipes, and the open cloud, developerWorks Open. The last two were topics DancingDinosaur tackled recently, here and here.

The TBR report, entitled Hosted Private & Professional Services Cloud Benchmark, provides a market synopsis and growth estimates for 29 cloud providers in the first quarter of 2015. In that report, TBR cited IBM as:

  • The undisputed growth leader in overall professional cloud services
  • The leader in hosted private cloud and managed cloud services
  • A leader in OpenStack vendor acquisitions and OpenStack cloud initiatives
  • A growth leader in cloud consulting services, bridging the gap between technology and strategy consulting
  • A growth leader in cloud systems integration services

According to the report: IBM’s leading position across all categories remains unchallenged as the company’s established SoftLayer and Bluemix portfolios, coupled with in-house cloud and solutions integration expertise, provide enterprises with end-to-end solutions.

Wall Street analysts and pundits clearly look at IBM differently than IT analysts.  The folks who look at IBM’s technology, strategy, and services, like those at Gartner, TBR, and the CRN report card, tell a different story. Who do you think has it right?

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

IBM Simplifies Internet of Things with developerWorks Recipes

August 6, 2015

IBM has a penchant for working through communities going back as far as Eclipse and probably before. Last week DancingDinosaur looked at the developerWorks Open community. Now let’s look at the IBM’s developerWorks Recipes community intended to address the Internet of Things (IoT).

recipes iot sensor tag

TI SensorTag

The Recipes community  will try to help developers – from novice to experienced – quickly and easily learn how to connect IoT devices to the cloud and how to use data coming from those connected devices. For example one receipe walks you through Connecting the TI Simplelink SensorTag (pictured above) to the IBM IoT foundation service in a few simple step. By following these steps a developer, according to IBM, should be able to connect the SensorTag to the IBM quickstart cloud service in less than 3 minutes. Think of recipes as simplified development patterns—so simple that almost anyone could follow it. (Wanted to try it myself but didn’t have a tag.  Still, it looked straightfoward enough.)

IoT is growing fast. Gartner forecasts 4.9 billion connected things in use in 2015, up 30% from 2014, and will reach 25 billion by 2020. In terms of revenue, this is huge. IDC predicts the worldwide IoT market to grow from $655.8 billion in 2014 to $1.7 trillion in 2020, a compound annual growth rate (CAGR) of 16.9%. For IT people who figure out how to do this, the opportunity will be boundless. Every organization will want to connect its devices to other devices via IoT. The developerWorks Recipes community seems like a perfect way to get started.

IoT isn’t exactly new. Manufacturers have cobbled together machine-to-machine (M2M) networks Banks and retailers have assembled networks of ATMs and POS terminals. DancingDinosaur has been writing about IoT for mainframe shops for several years.  Now deveoperWorks Recipes promises a way for just about anyone to set up their own IoT easily and quickly while leveraging the cloud in the process. There is a handful of recipes now but it provides a mechanism to add recipes so expect the catalog of recipes to steadily increase. And developers are certain to take existing recipes and improvise on them.

IBM has been trying to simplify  development for cloud, mobile, IoT starting with the launch of Bluemix last year. By helping users connect their IoT devices to IBM Bluemix, which today boasts more than 100 open-source tools and services, users can then run advanced analytics, utilize machine learning, and tap into additional Bluemix services to accelerate the adoption of  IoT and more.

As easy as IBM makes IoT development sound this is a nascent effort industry wide. There is a crying need for standards at every level to facilitate the interoperability and data exchange among the many and disparate devices, networks, and applications that will make up IoT.  Multiple organizations have initiated standards efforts but it will take some time to sort it all out.

And then there is the question of security. In a widely reported experiment by Wired Magazine  hackers were able to gain control of a popular smart vehicle. Given that cars are expected to be a major medium for IoT and every manufacturer is rushing to jam as much smart componentry into their vehicles you can only hope every automaker is  scrambling for security solutions .

Home appliances represent another fat, lucrative market target for manufacturers that want to embed intelligent devices and IoT into their all products. What if hackers access your automatic garage door opener? Or worse yet, what if they turn off your coffee maker and water heater? Could you start the day without a hot shower and cup of freshly brewed coffee and still function?

Running IoT through secure clouds like the IBM Cloud is part of the solution. And industry-specific clouds intended for IoT already are being announced, much like the Internet exchanges of a decade or two ago. Still, more work needs to be done on security and interoperability standards if IoT is to work seamlessly and broadly to achieve the trillions of dollars of economic value projected for it.

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









z Systems and Cloud Lead IBM 2Q Results

July 24, 2015

DancingDinosaur generally steers clear of writing about reported quarterly revenue. Given the general focus of this blog on enterprise and cloud computing, however, IBM’s recent 2Q15 report can’t be ignored. Although it continued IBM’s recent string of negative quarterly results, the z and cloud proved to be bright spots.

Infographic - IBM Q2 2015 Earnings - Cloud - July 20 2015 - Final

Strong IBM cloud performance, Q2 2015 (click to enlarge)

As IBM reported on Monday: Revenues from z Systems mainframe server products increased 9 percent compared with the year-ago period (up 15 percent adjusting for currency).  Total delivery of z Systems computing power, as measured in MIPS, increased 24 percent.  Revenues from Power Systems were down 1 percent compared with the 2014 period (up 5 percent adjusting for currency).

It’s not clear when and how Power Systems will come back. IBM has opened up the Power platform through the Open Power Foundation. A good move in theory, which DancingDinosaur applauds. Still, much depends on the Foundation gaining increased momentum and individual members rolling out successful Power-based products. The roadmap for POWER8, POWER9, and beyond looks promising but how fast products will arrive is unclear. There also is potential for the commoditization of the Power platform, a welcome development in many quarters, but commoditization’s impact on future revenue also is not clear.

Cloud revenue was up more than 70 percent, adjusting for currency and divested businesses; up more than 50 percent as reported, according to IBM. Given that cloud, along with mobile and analytics, has been designated strategic by IBM this is an encouraging development. The company’s cloud strategy is starting to bear fruit.

The big question hanging over every vendor’s cloud strategy is how to make money at it. One of the appealing aspects of the cloud in terms of cost and pricing for IT-using organizations is what amounts to a race to the bottom. With pricing immediately apparent and lower pricing just a click away it has become a feast for the bottom grazers to whom the lowest price is all that matters. For companies like IBM and Oracle, which also has declared cloud a strategic initiative, and other large legacy enterprise platform providers the challenge is to be competitive on price while differentiating their offerings in other ways. Clearly IBM has some unique cloud offerings in Watson and Bluemix and others but can they deliver enough revenue fast enough to offset the reduction in legacy platform revenue. Remember, x86 is off IBM’s menu.

Timothy Prickett Morgan, who writes frequently about IBM technology, also had plenty to say about IBM’s 2Q15 announcement, as did a zillion other financial and industry analyst. To begin he noted the irony of IBM promoting cloud computing, primarily an x86 phenomenon while trying to convince people that Power-based systems are cost competitive—which they can be—and will do a better job for many of those workloads, correct again.

Morgan also makes an interesting point in regard to the z: “IBM doesn’t have to push the System z mainframe so much as keep it on a Moore’s Law curve of its own and keep the price/performance improving to keep those customers in the mainframe fold.” That’s harder than it may seem; DancingDinosaur addressed the Moore’ Law issue last week here. As Morgan notes, with well over $1 trillion in software assets running on the mainframe, the 6,000 or so enterprises that use mainframes are unlikely to move off the platform because of the cost, disruption, and risk such a move would entail. Just ask Union-Pacific Railroad, which seems to be doing a slow-motion platform migration off the mainframe that seemingly may never actually end. Morgan concludes: “IBM can count on a certain level of money from the System z line that it just cannot with the Power Systems line.”

As noted above, how much revenue Power can generate for IBM depends on how fast the Open Power Foundation members introduce products that expand the market and how many Power processors SoftLayer can absorb as the business unit expands its global footprint.  There also is the question of how many POWER8 servers Rackspace, a much larger cloud provider than SoftLayer, will take and whether the Rackspace initiative will catch on elsewhere.

In any event, IBM’s 2Q15 report showed enough positive momentum to encourage IT platform enthusiasts. For its part, DancingDinosaur is expecting a business class z13 in the coming months and more.

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

IBM Extends Moore’s Law with First 7nm Test Chip

July 17, 2015

In an announcement last week, IBM effectively extended Moore’s Law for at least another generation of chips, maybe two.  This contradicts what leading vendors, including IBM, have been saying for years about the imminent diminishing returns of Moore’s Law, which postulated that chips would double in capacity every 18-24 months. Moore’s Law drove the price/performance curve the industry has been experiencing for the past several decades.


Click to enlarge, courtesy of IBM

The announcement, ironically, coincides with IBM’s completion of the sale of its semi-conductor fabrication business to GLOBALFOUNDRIES, which IBM paid to take the costly facilities off its hands. To pull off the 7nm achievement IBM ended up partnering with a handful of players including public-private partnership with New York State and joint development alliance with GLOBALFOUNDRIES, Samsung, and equipment suppliers. The team is based at SUNY Poly’s NanoTech Complex in Albany.

To achieve the higher performance, lower power, and scaling benefits promised by 7nm technology, the IBM researchers turned to two main innovations, the use Silicon Germanium (SiGe) channel transistors and Extreme Ultraviolet (EUV) lithography integration at multiple levels, in effect bypassing conventional semiconductor manufacturing approaches.

Don’t expect to see new systems featuring these 7nm chips very soon. The announcement made no mention of any timetable for producing commercial products based on this technology. As Timothy Prickett Morgan, who writes extensively on IBM POWER Systems technology observed: the use of silicon germanium for portions of the transistors cuts back on power consumption for the very fast switching necessary for improving circuit performance, and the circuits are etched using extreme ultraviolet (EUV) lithography. These technologies may be difficult and expensive to put into production.

In the meantime, IBM notes that microprocessors utilizing 22nm and 14nm technology run today’s servers, cloud data centers, and mobile devices; and already 10nm technology is well on the way to becoming a mature technology. The 7nm chips promise even more: at least a 50% power/performance improvement for next mainframe and POWER systems that will fuel the Big Data, cloud and mobile era, and soon you can add the Internet of Things too.

The z13 delivers unbeatable performance today. With the zEC12 IBM boasted of the fastest commercial chip in the industry, 5.5 GHz on a 32 nm wafer. It did not make that boast with the z13. Instead the z13 runs on a 22 nm core at 5 GHz but still delivers a 40% total capacity improvement over the zEC12.

It does this by optimizing the stack top to bottom with 600 processors and 320 separate channels dedicated just to drive I/O throughput. The reason for not cranking up the clock speed on the z13, according to IBM, was the plateauing of Moore’s Law. The company couldn’t get enough boost for the tradeoffs it would have had to make. Nobody seems to be complaining about giving up that one-half GHz. Today the machine can process 2.5 billion transactions a day.

The ride up the Moore’s Law curve has been very enjoyable for all. Companies took the additional processing power to build onto the chip more capabilities that otherwise would have required additional processors.  The result: more performance and more capabilities at lower cost. But all good things come to an end.

This 7nm  breakthrough doesn’t necessarily restore Moore’s Law. At this point, the best we can guess is that it temporarily moves the price/performance curve to a new plane. Until we know the economics of mass fabrication in the 7nm silicon germanium world we can’t tell whether we’ll see a doubling as before or maybe just a half or quarter or maybe it could triple. We just don’t now.

For the past decade, Morgan reports, depending on the architecture, the thermal limits of systems imposed a clock speed limit on processors, and aside from some nominal instruction per clock (IPC) improvements with each  recent microarchitecture change, clock speeds and performance for a processor stayed more or less flat. This is why vendors went parallel with their CPU architectures, in effect adding cores to expand throughput rather than increasing clock speed to boost performance on a lower number of cores. Some, like IBM, also learned to optimize at every level of the stack. As the z13 demonstrates, lots of little improvements do add up.

Things won’t stop here. As Morgan observes, IBM Research and the Microelectronics Division were working with GLOBALFOUNDRIES and Samsung and chip-making equipment suppliers who collaborate through the SUNY Polytechnic Institute’s Colleges of Nanoscale Science and Engineering in nearby Albany to get a path to 10 nm and then 7 nm processes even as the sale of GLOBALFOUNDRIES was being finalized.

The next step, he suggests, could possibly be at 4 nm but no one is sure if this can be done in a way that is economically feasible. If it can’t, IBM already has previewed the possibility of other materials that show promise.

Moore’s Law has been a wonderful ride for the entire industry. Let’s wish them the best as they aim for ever more powerful processors.

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

IBM POWER8 Tops STAC-A2 Benchmark in Win for OpenPOWER

June 25, 2015

In mid-March the Security Technology Analysis Center (STAC) released the first audited STAC-A2 Benchmark results for a server using the IBM Power8 architecture. STAC provides technology research and testing tools based on community-source standards. The March benchmark results showed that an IBM POWER8-based server can deliver more than twice the performance of the best x86 server when running standard financial industry workloads.

stac benchmark power8

IBM Power System S824

This is not IBM just blowing its own horn. The STAC Benchmark Council consists of a group of over 200 major financial firms and other algorithmic-driven enterprises as well as more than 50 leading technology vendors. Their mission is to explore technical challenges and solutions in financial services and develop technology benchmark standards that are useful to financial organizations.

The POWER8 system not only delivered more than twice the performance of the nearest x86 system but its set four new performance records for financial workloads, 2 of which apparently were new public records.  This marked the first time the IBM Power8 architecture has gone through STAC-A2 testing.

The community developed STAC-A2 benchmark set represents a class of financial risk analytics workloads characterized by Monte Carlo simulation and Greeks computations. Greeks computations cover theta, rho, delta, gamma, cross-gamma, model vega, and correlation vega. Together they are referred to as the Greeks. Quality is assessed for single assets by comparing the Greeks obtained from the Monte Carlo with Greeks obtained from a Heston closed form formula for vanilla puts and calls.  Suffice to say, this as an extremely CPU-intensive set of computations. For more detail, click here.

In this case, results were compared to other publicly-released results of warm runs on the Greeks benchmark (STAC-A2.β2.GREEKS.TIME.WARM). The two-socket Power8 server, outfitted with two 12-core 3.52 GHz Power8 processor cards, achieved:

  • 2.3x performance over the comparable x86 setup, an Intel white box with two Xeon E5-2699 v3 (Haswell EP) @ 2.30GHz.
  • 1.7x the performance of the best-performing x86 solution, an Intel white box with two Intel Xeon E5-2699 v3 processors (Haswell EP) @ 2.30GHz and one Intel Xeon Phi 7120A coprocessor.
  • Only 10% less performance than the best-performing solution, a Supermicro server with two 10-core Intel Xeon E5-2690 v2 @ 3.0GHz (Ivy Bridge) and one NVIDIA K80 GPU accelerator.

The Power server also set new records for path scaling (STAC-A2.β2.GREEKS.MAX_PATHS) and asset capacity (STAC-A2.β2.GREEKS.MAX_ASSETS). Compared to the best four-socket x86-based solution — a server comprised of four Xeon E7-4890 v2 (Ivy Bridge EX) parts running at 2.80 GHz — the Power8 server delivered:

  • Double the throughput.
  • 16 percent increase for asset capacity.

The STAC test system consisted of an IBM Power System S824 server with two 12-core 3.52 GHz POWER8 processor cards, equipped with 1TB of DRAM and running Red Hat Enterprise Linux version 7. The solution stack included the IBM-authored STAC-A2 Pack for Linux on Power Systems (Rev A), which used IBM XL, a suite for C/C++ developers that includes the C++ Compiler and the Mathematical Acceleration Subsystem libraries (MASS), and the Engineering and Scientific Subroutine Library (ESSL).

POWER8 processors are based on high performance, multi-threaded cores with each core of the Power System S824 server running up to eight simultaneous threads at 3.5 GHz. With POWER8 IBM also is able to tap the innovations of the OpenPOWER Foundation including CAPI and a variety of accelerators that have started to ship.

The S824 also brings a very high bandwidth memory interface that runs at 192 GB/s per socket which is almost three times the speed of a typical x86 processor. These factors along with a balanced system structure including a large internal 8MB per core L3 cache are the primary reasons why financial computing workloads run significantly faster on POWER8-based systems than alternatives, according to IBM.

Sumit Gupta, vice president of HPC and OpenPOWER operations at IBM, reports STAC-A2 gives a much more accurate view of the expected performance as compared to micro benchmarks or simple code loops. This is especially important when the challenge is big data.

In his blog on the topic, Gupta elaborated on the big data challenge in the financial industry and the POWER8 advantages. STAC-A2 is a set of standard benchmarks that help estimate the relative performance of full systems running complete financial applications. This enables clients in the financial industry to evaluate how systems will perform on real applications. “Those are the kind of results that matter—real results for real client challenges,” Gupta wrote.

Gupta went on to note that the S824 also has a very high bandwidth memory interface. Combined with the large L3 cache noted above it can run financial applications noticeably faster than alternatives.  Combine the STAC results with data recently published by Cabot Partners and you have convincing proof that IBM POWER8-based systems have taken the performance lead in the financial services space (and elsewhere). The Cabot Partners report evaluates functionality, performance, and price/performance across several industries, including life sciences, financial services, oil and gas, and analytics while referencing standard benchmarks as well as application-oriented benchmark data.

Having sat through numerous briefings on POWER8 performance, DancingDinosaur felt reassured, but he doesn’t have to actually run these workloads. It is encouraging, however, to see proof in the form of 3rd party benchmarks like STAC and reports from Cabot Partners. Check out Cabot’s OpenPOWER report here.

DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing on and here.

IBM Continues Open Source Commitment with Apache Spark

June 18, 2015

If anyone believes IBM’s commitment to open source is a passing fad, forget it. IBM has invested billions in Linux, open Power through the Open Power Foundation, and more. Its latest is the announcement of a major commitment to Apache Spark, a fast open source and general cluster computing system for big data.

spark VGN8668

Courtesy of IBM: developers work with Spark at Galvanize Hackathon

As IBM sees it, Spark brings essential advances to large-scale data processing. Specifically, it dramatically improves the performance of data dependent-apps and is expected to play a big role in the Internet of Things (IoT). In addition, it radically simplifies the process of developing intelligent apps, which are fueled by data. It does so by providing high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

IBM is contributing its breakthrough IBM SystemML machine learning technology to the Spark open source ecosystem. Spark brings essential advances to large-scale data processing, such as improvements in the performance of data dependent apps. It also radically simplifies the process of developing intelligent apps, which are fueled by data. But maybe the biggest advantage is that it can handle data coming from multiple, disparate sources.

What IBM likes in Spark is that it’s agile, fast, and easy to use. It also likes it being open source, which ensures it is improved continuously by a worldwide community. That’s also some of the main reasons mainframe and Power Systems data centers should pay attention to Spark.  Spark will make it easier to connect applications to data residing in your data center. If you haven’t yet noticed an uptick in mobile transactions coming into your data center, they will be coming. These benefit from Spark. And if you look out just a year or two, expect to see IoT applications adding to and needing to combine all sorts of data, much of it ending up on the mainframe or Power System in one form or another. So make sure Spark is on your radar screen.

Over the course of the next few months, IBM scientists and engineers will work with the Apache Spark open community to accelerate access to advanced machine learning capabilities and help drive speed-to-innovation in the development of smart business apps. By contributing SystemML, IBM hopes data scientists iterate faster to address the changing needs of business and to enable a growing ecosystem of app developers who will apply deep intelligence to everything.

To ensure that happens, IBM will commit more than 3,500 researchers and developers to work on Spark-related projects at more than a dozen labs worldwide, and open a Spark Technology Center in San Francisco for the Data Science and Developer community to foster design-led innovation in intelligent applications. IBM also aims to educate more than 1 million data scientists and data engineers on Spark through extensive partnerships with AMPLab, DataCamp, MetiStream, Galvanize, and Big Data University MOOC (Massive Open Online Course).

Of course, Spark isn’t going to be the end of tools to expedite the latest app dev. With IoT just beginning to gain widespread interest expect a flood of tools to expedite developing IoT data-intensive applications and more tools to facilitate connecting all these coming connected devices, estimated to number in the tens of billions within a few years.

DancingDinosaur applauds IBM’s decade-plus commitment to open source and its willingness to put real money and real code behind it. That means the IBM z System mainframe, the POWER platform, Linux, and the rest will be around for some time. That’s good; DancingDinosaur is not quite ready to retire.

DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing on and here.

API Economy Comes to the IBM z System

June 11, 2015

What comes to mind when you hear (or read) about a RESTful IBM z System? Hint: it is not a mainframe that is loafing. To the contrary, a RESTful mainframe probably is busier than it has ever been, now running a slew of new apps, most likely mobile or social apps with REST APIs connecting to z/OS-based web services plus its usual workloads. Remember web services when SOA first came to the mainframe? They continue today behind the new mobile, cloud, social, and analytical workloads that are putting the spotlight on the mainframe.

Travel and Transportation - Passenger Care

Courtesy of IBM: travel fuels mobile activity (click to enlarge)

A variety of Edge2015 sessions, given by Asit Dan, chief architect, z Service API Management and Glenn Anderson, IBM Lab Services and Training, put what the industry refers to as the emerging API economy in perspective. The z, it should come as no surprise, lies at the heart of this burgeoning API economy, not only handling transactions but also providing governance and management to the API phenomenon that is exploding. Check out IBM’s APIs for Dummies.

The difference between first generation SOA and today’s API economy lies in the new workloads—especially mobile and cloud—fueling the surging interest. The mobile device certainly is the fastest growing platform and will likely become the largest platform soon if it is not already, surpassing desktop and laptop systems.

SOA efforts initially focused on the capabilities of the providers of services, noted Dan, particularly the development, run-time invocation, and management of services. The API economy, on the other hand, focuses on the consumption of these services. It really aims to facilitate the efforts of application developers (internal developers and external business partners) who must code their apps for access to existing and new API-enabled services.

One goal of an enterprise API effort is to access already deployed services, such z-based CICS services or those of a partner. Maybe a more important goal, especially where the z is involved, is to drive use of mainframe software assets by customers, particularly mobile customers.  The API effort not only improves customer service and satisfaction but could also drive added revenue. (Have you ever fantasized of the z as a direct revenue generator?)

This calls, however, for a new set of interfaces. As Dan notes in a recent piece, APIs for accessing these assets, defined using well known standards such as web services and Representational State Transfer (REST) with JSON (JavaScript Object Notation), and published via an easily accessible catalog, make it efficient to subscribe to APIs for obtaining permissions and building new applications. Access to the APIs now can be controlled and tracked during run-time invocations (and even metered where revenue generation is the goal).

Now the API economy can morph into a commercial exchange of business functions, capabilities, and competencies as services using web APIs, noted Glenn Anderson at Edge2015. In-house business functions running on the z can evolve into an API as-a-service delivery vehicle, which amounts to another revenue stream for the mainframe data center.

The API economy often is associated with the concept of containers. Container technology provides a simplified way to make applications more mobile in a hybrid cloud, Anderson explained, and brings some distinct advantages. Specifically, containers are much smaller in size than virtual machines and provide more freedom in the placement of workloads in a cloud (private, public, hybrid) environment. Container technology is being integrated into OpenStack, which is supported on the z through IBM Cloud Manager. Docker is the best known container technology and it works with Linux on z.

With the combination of SOA, web services, REST, JSON, OpenStack, and Docker all z capable, a mainframe data center can fully participate in the mobile, apps, cloud API economy. BTW, POWER servers also can play the API, OpenStack, Docker game too. Even Watson can participate in the API economy through IBM’s early March acquisition of AlchemyAPI, a provider of scalable cognitive computing API services. The acquisition will drive the API economy into cognitive computing too. Welcome to the mainframe API economy.

DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing on and here.

Real Time Analytics on the IBM z13

June 4, 2015

For years organizations have been putting their analytics on distributed platforms thinking that was the only way to get fast, real-time and predictive analytics. Maybe once but not anymore. Turns out the IBM z System, especially the z13 not only is ideal for real time, predictive analytics but preferable.

IBM today is so bullish on analytics, especially predictive analytics, that last month it introduced 20 pre-built industry-specific predictive analytics solutions. To build these solutions IBM tapped its own experience working on 50,000 engagements but also an array of outside organizations with success in predictive analytics, including Urban Outfitters, National Grid, Deloitte, Bolsa de Santiago, Interactive Data Managed Solutions, and Bendigo and Adelaide Bank, among others.


Courtesy of IBM (click to enlarge)

The truth of the matter is that without efficient real time, predictive analytics managers get it wrong most of the time when it comes to making operational decisions, said Paul DiMarzio, IBM z Systems Big Data and Analytics Worldwide Portfolio Marketing Manager. He spoke at IBM Edge2015 in a session titled When Milliseconds Matter: Architecting Real-Time Analytics into Operational Systems. His key point: you can do this completely within the IBM z System.

The old notion of sending data to distributed systems someplace else for analytics now appears ridiculous, especially with the introduction of systems like the z13 that can handle operations and perform real time analytics concurrently. It performs analytics fast enough that you can make decisions when the action is still going on. Now the only question is whether we have the right business rules and scoring models. The data already are there and the tools are ready and waiting on the z13.

You start with the IBM SPSS Modeler with Scoring Adapter for zEnterprise. The real time predictive analytics capability delivers better, more profitable decisions at the point of customer impact. For business rules just turn to the IBM Operational Decision Manager for z/OS, which codifies business policies, practices, and regulations.

IBM SPSS improves accuracy by scoring directly within the transactional application against the latest committed data. As such it delivers the performance needed to meet operations SLAs and avoid data governance and security issues, effectively saving network bandwidth, data copying latency, and disk storage.

In addition to SPSS and the Operational Decision Manager the z13 brings many capabilities, some new for the z13 at this point. For starters, the z13 excels as a custodian of the data model, providing an accurate, secure, single copy of information that, according to IBM, ensures veracity of the data necessary for reliable analytics and provides centralized control over decision information.

Specifically, the machine brings SIMD (single instruction multiple data) and the MASS (mathematical acceleration subsystem) and ATLAS (automatically tuned linear algebra software) libraries for z/OS and Linux on z. SIMD enables the same operation to be performed on several data elements at the same time rather than sequentially. MASS and ATLAS help programmers create better and more complex analytic models.

In addition, increases in memory to as much as 10 TB, faster I/O, and simultaneous multi-threading (SMT) generally boost overall throughput of the z13, which will surely benefit any analytics being run on the machine, especially real time, predictive analytics.  In addition, analytics on the z13 gains from deep integration with core systems, the integrated architecture, and its single pane management view.

The latest IBM Red Book on analytics on the z13 sums it up as such: z Systems analytics enables organizations to improve performance and lower cost by bringing the analytic processing to where the data resides. Organizations can therefore maximize their current IT investments while adding functionality and improved price and performance with the z13. And with the new z13 features, applications can gain increased throughput for operational business intelligence (operational BI) and DB2 query workloads, which saves money (hardware, software, labor).

The Red Book suggests the following example: a user with a mobile application signs on and initiates a transaction flow through an IBM MobileFirst Platform Server running on Linux on z. The event goes to an LDAP server on z/OS to validate the user’s sign-on credentials. After successful validation, the transaction then proceeds through the z/OS transaction environment where all of the data resides in DB2 z/OS. IBM CICS transactions also are processed in the same z environment and all of the analysis is performed without moving any data, resulting in extremely fast performance. Sweet.

DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing on and here.

IBM zSystem for Social—Far From Forgotten at Edge2015

May 28, 2015

Dexter Doyle and Chris Gamin (z System Middleware) titled their session at Edge2015 IBM z Systems: The Forgotten Platform in Your Social Business. They were only half joking. As systems of engagement play bigger roles in the enterprise the z is not quite as forgotten as it may once have been.  In fact, at IBM the z runs the company’s own deployment of IBM Connections, the company’s flagship social business product.

Doyle used the graphic below (copyright John Atkinson, Wrong Hands) to make the point that new tools replace familiar conventional tools in a social business world.

 social desktop

 (copyright John Atkinson, Wrong Hands, click to enlarge)

Looks almost familiar, huh? Social business is not so radical. The elements of social business have been with us all along. It’s not exactly a one-to-one mapping, but Twitter and Pinterest instead of post-it notes, LinkedIn replaces the rolodex, Instagram instead of photos on your desk, and more.  Social business done right with the appropriate tools enables efficiency, Doyle observed. You don’t see the z in this picture, but it is there connecting all the dots in the social sphere

Many traditional mainframe data centers are struggling to come to grips with social business even as mobile and social workloads increasingly flow through the z. “The biggest thing with social is the change in culture,” said Doyle in his Forgotten Platform session. You end up using different tools to do business in a more social way. Even email appears antiquated in social business.

For data centers still balking at the notion of social business, Doyle noted that by 2016, 50% of large organizations will have internal Facebook-like social networks, a widely reported Gartner finding, and 30% of these will be considered as essential as email and telephones are today. The message: social business is real and z data centers should be a big part of it.

So what parts of social business will engage with the z? Doyle suggested five to start:

  1. Social media analytics
  2. Customer sentiment
  3. Customer and new market opportunity identification
  4. Identification of illegal or suspicious activities
  5. Employee and customer experiences

And the z System’s role? Same as it has always been:

  • Build an agile approach to deliver applications
  • Make every transaction secure
  • Use analytics to improve outcomes at every moment

These are things every z data center should be good at. To get started with social business on z visit the IBM Connections webpage here. There happens to be an offer for the 60-day free trail (it’s a cloud app) here. Easy and free, at least should be worth a try.

IBM Connections delivers a handful of social business capabilities. The main components are home, profiles, communities, and social analytics. Other capabilities include blogs, wikis, bookmarks, and forums for idea generation and sharing. You can use the activities capability to organize your work and that of a team, and another lets you vote on ideas. Finally, it brings a media library, content management capabilities, and file management.

Along with Connections you also might want to deploy WebSphere and Java, if you haven’t already. Then, if you are serious about building out a social business around the z you’ll want to check out Bluemix and MobileFirst. Already there is an IBM Red Book out for mobile app dev on the z13. The idea, of course, is to create engaging mobile and social business apps with the z as the back end.

The biggest payoff from social business on the z comes when you add analytics, especially real-time analytics. DancingDinosaur attended a session on that topic at Edge2015 and will be taking it up in a coming post.

DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing on and here.


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