Posts Tagged ‘Power Systems’

IBM AI Reference Architecture Promises a Fast Start

August 10, 2018

Maybe somebody in your organization has already fooled around with a PoC for an AI project. Maybe you already want to build it out and even put it into production. Great! According to IBM:  By 2020, organizations across a wide array of different industries that don’t deploy AI will be in trouble. So those folks already fooling around with an AI PoC will probably be just in time.

To help organization pull the complicated pieces of AI together, IBM, with the help of IDC, put together its AI Infrastrucure Reference Architecture. This AI reference architecture, as IBM explains, is intended to be used by data scientists and IT professionals who are defining, deploying and integrating AI solutions into an organization. It describes an architecture that will support a promising proof of concept (PoC), experimental application, and sustain growth into production as a multitenant system that can continue to scale to serve a larger organization, while integrating into the organization’s existing IT infrastructure. If this sounds like you check it out. The document runs short, less than 30 pages, and free.

In truth, AI, for all the wonderful things you’d like to do with it, is more a system vendor’s dream than yours.  AI applications, and especially deep learning systems, which parse exponentially greater amounts of data, are extremely demanding and require powerful parallel processing capabilities. Standard CPUs, like those populating racks of servers in your data center, cannot sufficiently execute AI tasks. At some point, AI users will have to overhaul their infrastructure to deliver the required performance if they want to achieve their AI dreams and expectations.

Therefore, IDC recommends businesses developing AI capabilities or scaling existing AI capabilities, should plan to deliberately hit this wall in a controlled fashion. Do it knowingly and in full possession of the details to make the next infrastructure move. Also, IDC recommends you do it in close collaboration with a server vendor—guess who wants to be that vendor—who can guide them from early stage to advanced production to full exploitation of AI capabilities throughout the business.

IBM assumes everything is going to AI as quickly as it can, but that may not be the case for you. AI workloads include applications based on machine learning and deep learning, using unstructured data and information as the fuel to drive the next results. Some businesses are well on their way with deploying AI workloads, others are experimenting, and a third group is still evaluating what AI applications can mean for their organization. At all three stages the variables that, if addressed properly, together make up a well-working and business-advancing solution are numerous.

To get a handle on these variables, executives from IT and LOB managers often form a special committee to actively consider their organization’s approach to the AI. Nobody wants to invest in AI for the sake of AI; the vendors will get rich enough as it is. Also, there is no need to reinvent the wheel; many well-defined use cases exist that are applicable across industries. Many already are noted in the AI reference guide.

Here is a sampling:

  • Fraud analysis and investigation (banking, other industries)
  • Regulatory intelligence (multiple industries)
  • Automated threat intelligence and prevention systems (many industries)
  • IT automation, a sure winner (most industries)
  • Sales process recommendation and automation
  • Diagnosis and treatment (healthcare)
  • Quality management investigation and recommendation (manufacturing)
  • Supply and logistics (manufacturing)
  • Asset/fleet management, another sure winner (multiple industries)
  • Freight management (transportation)
  • Expert shopping/buying advisory or guide

Notes IDC: Many can be developed in-house, are available as commercial software, or via SaaS in the cloud.

Whatever you think of AI, you can’t avoid it. AI will penetrate your company embedded in the new products and services you buy.

So where does IBM hope your AI effort end up? Power9 System, hundreds of GPUs, and PowerAI. Are you surprised?

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

New Syncsort Tools Boost IBMi

July 25, 2018

Earlier this week Syncsort announced new additions to its family of products that can be used to help address top-of-mind compliance challenges faced by IT leaders, especially IBMi shops. Specifically, Syncsort’s IBMi security products can help IBMi shops comply with the EU’s General Data Protection Regulation (GDPR) and strengthen security with multi-factor authentication.

The new innovations in the Syncsort Assure products follow the recent acquisition of IBMi data privacy products from Townsend Security. The Alliance Encryption and Security Suite can be used to address protection of sensitive information and compliance with multi-factor authentication, encryption, tokenization, secure file transfer, and system log collection.

Syncsort’s Cilasoft Compliance and Security Suite for IBMi and Syncsort’s Enforcive Enterprise Security Suite provide unique tools that can help organizations comply with regulatory requirements and address security auditing and control policies. New releases of both security suites deliver technology that can be used to help accelerate and maintain compliance with GDPR.

As the bad guys get more effective, multi-factor authentication is required in many compliance regulations; such as PCI-DSS 3.2, NYDFS Cybersecurity Regulation, Swift Alliance Access, and HIPAA. Multi-factor authentication strengthens login security by requiring something more than a password or passphrase; only granting access after two or more authentication factors have been verified.

To help organizations fulfill regulatory requirements and improve the security of their IBMi systems and applications, Syncsort has delivered the new, RSA-certified Cilasoft Reinforced Authentication Manager for IBMi (RAMi). RAMi’s rules engine facilitates the set-up of multi-factor authentication screens for users or situations that require it, based on specific criteria. RAMi’s authentication features also enable self-service user profile re-enablement and password changes and support of the four eyes principle of supervised changes to sensitive data. Four eyes principle requires that any requested action must be approved by at least two people.

Syncsort expects 30% of its revenue to come from IBMi products. It also plans to integrate its Assure products with Ironstream to offer capacity management for IBMi.

In one sense, Syncsort is joining a handful of vendors, led by IBM, who continue to expand and enhance IBMi. DancingDinosaur has been writing about the IBMi even before it became the AS400, which recently celebrated its 30th birthday this week, writes Timothy Prickett Morgan, a leading analyst at the Next Platform. The predecessors to the AS/400 that your blogger wrote about back then were the System 36 and System 38, but they didn’t survive.  In those 30+ years, however, the IBMi platform has continued to evolve to meet customer needs, most recently by running on Power Systems, where it still remains a viable business, Morgan noted.

The many rivals of the OS/400 platform and its follow-ons since that initial launch of the AS/400 are now gone. You may recall a few of them: DEC’s VMS for the VAX and Alpha systems, Hewlett Packard’s MPE for the HP 3000, HP-UX for the HP 9000s, and Sun Microsystems’ Solaris for the Sparc systems.  DancingDinosaur once tried to cheerlead an effort to port Solaris/Sparc to the mainframe but IBM didn’t buy into that.

Among all of these and other platforms, IBMi is still out there, with probably around 125,000 unique customers and maybe between 250,000 and 300,000 systems. Morgan estimates.

He adds: As much as computing and automation has exploded on the scene since the first AS/400 arrived, one thing continues: Good old fashioned online transaction processing is something that every business still has to do, and even the biggest hyperscalers use traditional applications to keep the books and run the payroll.

The IBMi platform operates as more than an OLTP machine, evolving within the constantly changing environment of modern datacenters. This is a testament, Morgan believes, to the ingenuity and continuing investment by IBM in its Power chips, Power Systems servers, and the IBMi and AIX operating systems. Yes, Linux came along two decades ago and has bolstered the Power platforms, but not to the same extent that Linux bolstered the mainframe. The mainframe had much higher costs and lower priced Linux engines on mainframes exhibited a kind of elasticity of demand that IBM wishes it could get for IBMi and z/OS. Morgan is right about a lot but DancingDinosaur still wishes IBM had backed Solaris/Sparc on the z alongside Linux. Oh well.

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

IBM Introduces a Reference Architecture for On-Premise AI

June 22, 2018

This week IBM announced an AI infrastructure Reference Architecture for on-premises AI deployments. The architecture promises to address the challenges organizations face experimenting with AI PoCs, growing into multi-tenant production systems, and then expanding to enterprise scale while integrating into an organization’s existing IT infrastructure.

The reference architecture includes, according to IBM, a set of integrated software tools built on optimized, accelerated hardware for the purpose of enabling organizations to jump start. AI and Deep Learning projects, speed time to model accuracy, and provide enterprise-grade security, interoperability, and support.  IBM’s graphic above should give you the general picture.

Specifically, IBM’s AI reference architecture should support iterative, multi-stage, data-driven processes or workflows that entail specialized knowledge, skills, and, usually, a new compute and storage infrastructure. Still, these projects have many attributes that are familiar to traditional CIOs and IT departments.

The first of these is that the results are only as good as the data going into it, and model development is dependent upon having a lot of data and the data being in the format expected by the deep learning framework. Surprised? You have been hearing this for decades as GIGO (Garbage In Garbage Out).  The AI process also is iterative; repeatedly looping through data sets and tunings to develop more accurate models and then comparing new data in the model to the original business or technical requirements to refine the approach.  In this sense, AI reference model is no different than IT 101, an intro course for wannabe IT folks.

But AI doesn’t stay simplistic for long. As the reference architecture puts it, AI is a sophisticated, complex process that requires specialized software and infrastructure. That’s where IBM’s PowerAI Platform comes in. Most organizations start with small pilot projects bound to a few systems and data sets but grow from there.

As projects grow beyond the first test systems, however, it is time to bulk up an appropriate storage and networking infrastructure. This will allow it to sustain growth and eventually support a larger organization.

The trickiest part of AI and the part that takes inspired genius to conceive, test, and train is the model. The accuracy and quality of a trained AI model are directly affected by the quality and quantity of data used for training. The data scientist needs to understand the problem they are trying to solve and then find the data needed to build a model that solves the problem.

Data for AI is separated into a few broad sets; the data used to train and test the models and data that is analyzed by the models and the archived data that may be reused. This data can come from many different sources such as traditional organizational data from ERP systems, databases, data lakes, sensors, collaborators and partners, public data, mobile apps, social media, and legacy data. It may be structured or unstructured in many formats such as file, block, object, Hadoop Distributed File Systems (HDFS), or something else.

Many AI projects begin as a big data problem. Regardless of how it starts, a large volume of data is needed, and it inevitably needs preparation, transformation, and manipulation. But it doesn’t stop there.

AI models require the training data to be in a specific format; each model has its own and usually different format. Invariably the initial data is nowhere near those formats. Preparing the data is often one of the largest organizational challenges, not only in complexity but also in the amount of time it takes to transform the data into a format that can be analyzed. Many data scientists, notes IBM, claim that over 80% of their time is spent in this phase and only 20% on the actual process of data science. Data transformation and preparation is typically a highly manual, serial set of steps: identifying and connecting to data sources, extracting to a staging server, tagging the data, using tools and scripts to manipulate the data. Hadoop is often a significant source of this raw data, and Spark typically provides the analytics and transformation engines used along with advanced AI data matching and traditional SQL scripts.

There are two other considerations in this phase: 1) data storage and access and the speed of execution. For this—don’t be shocked—IBM recommends Spectrum Scale to provide multi-protocol support with a native HDFS connector, which can centralize and analyze data in place rather than wasting time copying and moving data. But you may have your preferred platform.

IBM’s reference architecture provides a place to start. A skilled IT group will eventually tweak IBM’s reference architecture, making it their own.

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

IBM Expands and Enhances its Cloud Offerings

June 15, 2018

IBM announced 18 new availability zones in North America, Europe, and Asia Pacific to bolster its IBM Cloud business and try to keep pace with AWS, the public cloud leader, and Microsoft. The new availability zones are located in Europe (Germany and UK), Asia-Pacific (Tokyo and Sydney), and North America (Washington, DC and Dallas).

IBM cloud availability zone, Dallas

In addition, organizations will be able to deploy multi-zone Kubernetes clusters across the availability zones via the IBM Cloud Kubernetes Service. This will simplify how they deploy and manage containerized applications and add further consistency to their cloud experience. Furthermore, deploying multi-zone clusters will have minimal impact on performance, about 2 ms latency between availability zones.

An availability zone, according to IBM, is an isolated instance of a cloud inside a data center region. Each zone brings independent power, cooling, and networking to strengthen fault tolerance. While IBM Cloud already operates in nearly 60 locations, the new zones add even more capacity and capability in these key centers. This global cloud footprint becomes especially critical as clients look to gain greater control of their data in the face of tightening data regulations, such as the European Union’s new General Data Protection Regulation (GDPR). See DancingDinosaur June 1, IBM preps z world for GDPR.

In its Q1 earnings IBM reported cloud revenue of $17.7bn over the past year, up 22 percent over the previous year, but that includes two quarters of outstanding Z revenue that is unlikely to be sustained,  at least until the next Z comes out, which is at least a few quarters away.  AWS meanwhile reported quarterly revenues up 49 percent to $5.4 billion, while Microsoft recently reported 93 percent growth for Azure revenues.

That leaves IBM trying to catch up the old fashioned way by adding new cloud capabilities, enhancing existing cloud capabilities, and attracting more clients to its cloud capabilities however they may be delivered. For example, IBM announced it is the first cloud provider to let developers run managed Kubernetes containers directly on bare metal servers with direct access to GPUs to improve the performance of machine-learning applications, which is critical to any AI effort.  Along the same lines, IBM will extend its IBM Cloud Private and IBM Cloud Private for Data and middleware to Red Hat’s OpenShift Container Platform and Certified Containers. Red Hat already is a leading provider of enterprise Linux to Z shops.

IBM has also expanded its cloud offerings to support the widest range of platforms. Not just Z, LinuxONE, and Power9 for Watson, but also x86 and a variety of non-IBM architectures and platforms. Similarly, notes IBM, users have gotten accustomed to accessing corporate databases wherever they reside, but proximity to cloud data centers still remains important. Distance to data centers can have an impact on network performance, resulting in slow uploads or downloads.

Contrary to simplifying things, the propagation of more and different types of clouds and cloud strategies complicate an organization’s cloud approach. Already, today companies are managing complex, hybrid public-private cloud environments. At the same time, eighty percent of the world’s data is sitting on private servers. It just is not practical or even permissible in some cases to move all the data to the public cloud. Other organizations are run very traditional workloads that they’re looking to modernize over time as they acquire new cloud-native skills. The new IBM cloud centers can host data in multiple formats and databases including DB2, SQLBase, PostreSQL, or NoSQL, all exposed as cloud services, if desired.

The IBM cloud centers, the company continues, also promise common logging and services between the on-prem environment and IBM’s public cloud environment. In fact, IBM will make all its cloud services, including the Watson AI service, consistent across all its availability zones, and offer multi-cluster support, in effect enabling the ability to run workloads and do backups across availability zones.

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

Is Your Enterprise Ready for AI?

May 11, 2018

According to IBM’s gospel of AI “we are in the midst of a global transformation and it is touching every aspect of our world, our lives, and our businesses.”  IBM has been preaching its gospel of AI of the past year or longer, but most of its clients haven’t jumped fully aboard. “For most of our clients, AI will be a journey. This is demonstrated by the fact that most organizations are still in the early phases of AI adoption.”

AC922 with NIVIDIA Tesla V100 and Enhanced NVLink GPUs

The company’s latest announcements earlier this week focus POWER9 squarely on AI. Said Tim Burke, Engineering Vice President, Cloud and Operating System Infrastructure, at Red Hat. “POWER9-based servers, running Red Hat’s leading open technologies offer a more stable and performance optimized foundation for machine learning and AI frameworks, which is required for production deployments… including PowerAI, IBM’s software platform for deep learning with IBM Power Systems that includes popular frameworks like Tensorflow and Caffe, as the first commercially supported AI software offering for [the Red Hat] platform.”

IBM insists this is not just about POWER9 and they may have a point; GPUs and other assist processors are taking on more importance as companies try to emulate the hyperscalers in their efforts to drive server efficiency while boosting power in the wake of declines in Moore’s Law. ”GPUs are at the foundation of major advances in AI and deep learning around the world,” said Paresh Kharya, group product marketing manager of Accelerated Computing at NVIDIA. [Through] “the tight integration of IBM POWER9 processors and NVIDIA V100 GPUs made possible by NVIDIA NVLink, enterprises can experience incredible increases in performance for compute- intensive workloads.”

To create an AI-optimized infrastructure, IBM announced the latest additions to its POWER9 lineup, the IBM Power Systems LC922 and LC921. Characterized by IBM as balanced servers offering both compute capabilities and up to 120 terabytes of data storage and NVMe for rapid access to vast amounts of data. IBM included HDD in the announcement but any serious AI workload will choke without ample SSD.

Specifically, these new servers bring an updated version of the AC922 server, which now features recently announced 32GB NVIDIA V100 GPUs and larger system memory, which enables bigger deep learning models to improve the accuracy of AI workloads.

IBM has characterized the new models as data-intensive machines and AI-intensive systems, LC922 and LC921 Servers with POWER9 processors. The AC922, arrived last fall. It was designed for the what IBM calls the post-CPU era. The AC922 was the first to embed PCI-Express 4.0, next-generation NVIDIA NVLink, and OpenCAPI—3 interface accelerators—which together can accelerate data movement 9.5x faster than PCIe 3.0 based x86 systems. The AC922 was designed to drive demonstrable performance improvements across popular AI frameworks such as TensorFlow and Caffe.

In the post CPU era, where Moore’s Law no longer rules, you need to pay as much attention to the GPU and other assist processors as the CPU itself, maybe even more so. For example, the coherence and high-speed of the NVLink enables hash tables—critical for fast analytics—on GPUs. As IBM noted at the introduction of the new machines this week: Hash tables are fundamental data structure for analytics over large datasets. For this you need large memory: small GPU memory limits hash table size and analytic performance. The CPU-GPU NVLink2 solves 2 key problems: large memory and high-speed enables storing the full hash table in CPU memory and transferring pieces to GPU for fast operations; coherence enables new inserts in CPU memory to get updated in GPU memory. Otherwise, modifications on data in CPU memory do not get updated in GPU memory.

IBM has started referring to the LC922 and LC921 as big data crushers. The LC921 brings 2 POWER9 sockets in a 1U form factor; for I/O it comes with both PCIe 4.0 and CAPI 2.0.; and offers up to 40 cores (160 threads) and 2TB RAM, which is ideal for environments requiring dense computing.

The LC922 is considerably bigger. It offers balanced compute capabilities delivered with the P9 processor and up to 120TB of storage capacity, again advanced I/O through PCIe 4.0/CAPI 2.0, and up to 44 cores (176 threads) and 2TB RAM. The list price, notes IBM is ~30% less.

If your organization is not thinking about AI your organization is probably in the minority, according to IDC.

  • 31 percent of organizations are in [AI] discovery/evaluation
  • 22 percent of organizations plan to implement AI in next 1-2 years
  • 22 percent of organizations are running AI trials
  • 4 percent of organizations have already deployed AI

Underpinning both servers is the IBM POWER9 CPU. The POWER9 enjoys a nearly 5.6x improved CPU to GPU bandwidth vs x86, which can improve deep learning training times by nearly 4x. Even today companies are struggling to cobble together the different pieces and make them work. IBM learned that lesson and now offers a unified AI infrastructure in PowerAI and Power9 that you can use today.

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 Grows Quantum Ecosystem

April 27, 2018

It is good that you aren’t dying to deploy quantum computing soon because IBM readily admits that it is not ready for enterprise production now or in several weeks or maybe several months. IBM, however, continues to assemble the building blocks you will eventually need when you finally feel the urge to deploy a quantum application that can address a real problem that you need to resolve.

cryostat with prototype of quantum processor

IBM is surprisingly frank about the state of quantum today. There is nothing you can do at this point that you can’t simulate on a conventional or classical computer system. This situation is unlikely to change anytime soon either. For years to come, we can expect hybrid quantum and conventional compute environments that will somehow work together to solve very demanding problems, although most aren’t sure exactly what those problems will be when the time comes. Still at Think earlier this year IBM predicted quantum computing will be mainstream in 5 years.

Of course, IBM has some ideas of where the likely problems to solve will be found:

  • Chemistry—material design, oil and gas, drug discovery
  • Artificial Intelligence—classification, machine learning, linear algebra
  • Financial Services—portfolio optimization, scenario analysis, pricing

It has been some time since the computer systems industry had to build a radically different kind of compute discipline from scratch. Following the model of the current IT discipline IBM began by launching the IBM Q Network, a collaboration with leading Fortune 500 companies and research institutions with a shared mission. This will form the foundation of a quantum ecosystem.  The Q Network will be comprised of hubs, which are regional centers of quantum computing R&D and ecosystem; partners, who are pioneers of quantum computing in a specific industry or academic field; and most recently, startups, which are expected to rapidly advance early applications.

The most important of these to drive growth of quantum are the startups. To date, IBM reports eight startups and it is on the make for more. Early startups include QC Ware, Q-Ctrl, Cambridge Quantum Computing (UK), which is working on a compiler for quantum computing, 1Qbit based in Canada, Zapata Computing located at Harvard, Strangeworks, an Austin-based tool developer, QxBranch, which is trying to apply classical computing techniques to quantum, and Quantum Benchmark.

Startups get membership in the Q network and can run experiments and algorithms on IBM quantum computers via cloud-based access; provide deeper access to APIs and advanced quantum software tools, libraries, and applications; and have the opportunity to collaborate with IBM researchers and technical SMEs on potential applications, as well as with other IBM Q Network organizations. If it hasn’t become obvious yet, the payoff will come from developing applications that solve recognizable problems. Also check out QISKit, a software development kit for quantum applications available through GitHub.

The last problem to solve is the question around acquiring quantum talent. How many quantum scientists, engineers, or programmers do you have? Do you even know where to find them? The young people excited about computing today are primarily interested in technologies to build sexy apps using Node.js, Python, Jupyter, and such.

To find the people you need to build quantum computing systems you will need to scour the proverbial halls of MIT, Caltech, and other top schools that produce physicists and quantum scientists. A scan of salaries for these people reveals $135,000- $160,000, if they are available at all.

The best guidance from IBM on starting is to start small. The industry is still at the building block stage; not ready to throw specific application at real problems. In that case sign up for IBM’s Q Network and get some of your people engaged in the opportunities to get educated in quantum.

When DancingDinosaur first heard about quantum physics he was in a high school science class decades ago. It was intriguing but he never expected to even be alive to see quantum physics becoming real, but now it is. And he’s still here. Not quite ready to sign up for QISKit and take a small qubit machine for a spin in the cloud, but who knows…

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

IBM Boosts AI at Think

March 23, 2018

Enterprise system vendors are racing to AI along with all the others. Writes Jeffrey Burt, an analyst at The Next Platform, “There continues to be an ongoing push among tech vendors to bring artificial intelligence (AI) and its various components – including deep learning and machine learning – to the enterprise. The technologies are being rapidly adopted by hyperscalers and in the HPC space, and enterprises stand to reap significant benefits by also embracing them.” Exactly what those benefits are still need to be specifically articulated and, if possible, quantified.

IBM Think Conference this week

For enterprise data centers running the Z or Power Systems, the most obvious quick payoff will be fast, deeper, more insightful data analytics along with more targeted guidance on actions to take in response. After that there still remains the possibility of more automation of operations but the Z already is pretty thoroughly automated and optimized. Just give it your operational and performance parameters and it will handle the rest.  In addition, vendors like Compuware and Syncsort have been making the mainframe more graphical and intuitive. The days of needing deep mainframe experience or expertise have passed. Even x86 admins can quickly pick up a modern mainframe today.

In a late 2016 study by Accenture that modeled the impact of AI for 12 developed economies. The research compared the size of each country’s economy in 2035 in a baseline scenario, which shows expected economic growth under current assumptions and an AI scenario reflecting expected growth once the impact of AI has been absorbed into the economy. AI was found to yield the highest economic benefits for the United States, increasing its annual growth rate from 2.6 percent to 4.6 percent by 2035, translating to an additional USD $8.3 trillion in gross value added (GVA). In the United Kingdom, AI could add an additional USD $814 billion to the economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9 percent. Japan has the potential to more than triple its annual rate of GVA growth by 2035, and Finland, Sweden, the Netherlands, Germany and Austria could see their growth rates double. You can still find the study here.

Also coming out of Think this week was the announcement of an expanded Apple-IBM partnership around AI and machine learning (ML). The resulting AI service is intended for corporate developers to build apps themselves. The new service, Watson Services for Core ML, links Apple’s Core ML tools for developers that it unveiled last year with IBM’s Watson data crunching service. Core ML helps coders build machine learning-powered apps that more efficiently perform calculations on smartphones instead of processing those calculations in external data centers. It’s similar to other smartphone-based machine learning tools like Google’s TensorFlow Lite.

The goal is to help enterprises reimagine the way they work through a combination of Core ML and Watson Services to stimulate the next generation of intelligent mobile enterprise apps. Take the example of field technicians who inspect power lines or machinery. The new AI field app could feed images of electrical equipment to Watson to train it to recognize the machinery. The result would enable field technicians to scan the electrical equipment they are inspecting on their iPhones or iPads and automatically detect any anomalies. The app would eliminate the need to send that data to IBM’s cloud computing data centers for processing, thus reducing the amount of time it takes to detect equipment issues to near real-time.

Apple’s Core ML toolkit could already be used to connect with competing cloud-based machine learning services from Google, Amazon, and Microsoft to create developer tools that more easily link the Core ML service with Watson. For example, Coca-Cola already is testing Watson Services for Core ML to see if it helps its field technicians better inspect vending machines. If you want try it in your shop, the service will be free to developers to use now. Eventually, developers will have to pay.

Such new roll-your-own AI services represent a shift for IBM. Previously you had to work with IBM consulting teams. Now the new Watson developer services are intended to be bought in an “accessible and bite size” way, according to IBM, and sold in a “pay as you go” model without consultants.  In a related announcement at Think, IBM announced it is contributing the core of Watson Studio’s Deep Learning Service as an open source project called Fabric for Deep Learning. This will enable developers and data scientists to work together on furthering the democratization of deep learning.

Ultimately, the democratization of AI is the only way to go. When intelligent systems speak together and share insights everyone’s work will be faster, smarter. Yes, there will need to be ways to compensate distinctively valuable contributions but with over two decades of open source experience, the industry should be able to pretty easily figure that out.

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

Dinosaurs Strike Back in IBM Business Value Survey

March 2, 2018

IBM’s Institute of Business Value (IBV) recently completed a massive study based 12,000 interviews of executives of legacy c-suite companies. Not just CEO and CIO but COO, CFO, CMO, and more, including the CHO. The CHO is the Chief Happiness Officer. Not sure what a CHO actually does but if one had been around when DancingDinosaur was looking for a corporate job he might have stayed on the corporate track instead of pursuing the independent analyst/writer dream.

(unattributed IBM graphic)

IBV actually referred to the study as “Incumbents strike back.” The incumbents being the legacy businesses the c-suite members represent. In a previous c-suite IBV study two years ago, the respondents expressed concern about being overwhelmed and overrun by new upstart companies, the born-on-the-web newcomers. In many ways the execs at that time felt they were under attack.

Spurred by fear, the execs in many cases turned to a new strategy that takes advantage of what has always been their source of strength although they often lacked the ways and means to take advantage of that strength; the huge amounts of data they have gathered and stored, for decades in some cases. With new cognitive systems now able to extract and analyze this legacy data and combine it with new data, they could actually beat some of the upstarts. Finally, they could respond like nimble, agile operations, not the lumbering dinosaurs as they were often portrayed.

“Incumbents have become smarter about leveraging valuable data, honing their employees’ skills, and in some cases, acquired possible disruptors to compete in today’s digital age,” the study finds, according to CIO Magazine, which published excerpts from the study here. The report reveals 72 percent of surveyed CxOs claimed the next wave of disruptive innovation will be led by the incumbents who pose a significant competitive threat to new entrants and digital players. By comparison, the survey found only 22 percent of respondents believe smaller companies and start-ups are leading disruptive change. This presents a dramatic reversal from a similar but smaller IBV survey two years ago.

Making possible this reversal is not only growing awareness among c-level execs of the value of their organizations’ data and the need to use it to counter the upstarts, but new technologies, approaches like DevOps, easier-to-use dev tools, the increasing adoption of Linux, and mainframes like the z13, z14, and LinuxONE, which have been optimized for hybrid and cloud computing.  Also driving this is the emergence of platform options as a business strategy.

The platform option may be the most interesting decision right now. To paraphrase Hamlet, to be (a platform for your industry) or not to be. That indeed is a question many legacy businesses will need to confront. When you look at platform business models, what is right for your organization. Will you create a platform for your industry or piggyback on another company’s platform? To decide you need to first understand the dynamics of building and operating a platform.

The IBV survey team explored that question and found the respondents pretty evenly divided with 54% reporting they won’t while the rest expect to build and operate a platform. This is not a question that you can ruminate over endlessly like Hamlet.  The advantage goes to those who can get there first in their industry segment. Noted IBV, only a few will survive in any one industry segment. It may come down to how finely you can segment the market for your platform and still maintain a distinct advantage. As CIO reported, the IBV survey found 57 percent of disruptive organizations are adopting a platform business model.

Also rising in importance is the people-talent-skills issue. C-level execs have always given lip service to the importance of people as in the cliché people are our greatest asset.  Based on the latest survey, it turns out skills are necessary but not sufficient. Skills must be accompanied by the right culture. As the survey found:  Companies that have the right culture in place are more successful. In that case, the skills are just an added adrenalin shot. Still the execs put people skills in top three. The IBV analysts conclude: People and talent is coming back. Guess we’re not all going to be replaced soon with AI or cognitive computing, at least not yet.

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

IBM Jumps into the Next Gen Server Party with POWER9

February 15, 2018

IBM re-introduced its POWER9 lineup of servers  this week starting with 2-socket and 4-socket systems and more variations coming in the months ahead as IBM, along with the rest of the IT vendor community grapples with how to address changing data center needs. The first, the AC922, arrived last fall. DancingDinosaur covered it here. More, the S922/S914/S924 and H922/H924/L922, are promised later this quarter.

The workloads organizations are running these days are changing, often dramatically and quickly. One processor, no matter how capable or flexible or efficient will be unlikely to do the job going forward. It will take an entire family of chips.  That’s as true for Intel and AMR and the other chip players as IBM.

In some ways, IBM’s challenge is even qwerkier. Its chips will not only need to support Linux and Windows, but also IBMi and AIX. IBM simply cannot abandon its IBMi and AIX customer bases. So chips supporting IBMi and AIX are being built into the POWER9 family.

For IBMi the company is promising POWER9 exploitation for:

  • Expanding the secure-ability of IBMi with TLS, secure APIs, and logs for SIEM solutions
  • Expanded Install options with an installation process using USB 3.0 media
  • Encryption and compression for cloud storage
  • Increasing the productivity of developers and administrators

This may sound trivial to those who have focused on the Linux world and work with x86 systems too, but it is not for a company still mired in productive yet aging IBMi systems.

IBM also is promising POWER9 goodies for AIX, its legacy Unix OS, including:

  • AIX Security: PowerSC and PowerSC MFA updates for malware intrusion prevention and strong authentication
  • New workload acceleration with shared memory communications over RDMA (SMC-R)
  • Improved availability: AIX Live Update enhancements; GDR 1.2; PowerHA 7.2
  • Improved Cloud Mgmt: IBM Cloud PowerVC Manager for SDI; Import/Export;
  • AIX 7.2 native support for POWER9 – e.g. enabling NVMe

Again, if you have been running Linux on z or LinuxONE this may sound antiquated, but AIX has not been considered state-of-the-art for years. NVMe alone gives is a big boost.

But despite all the nice things IBM is doing for IBMi and AIX, DancingDinosaur believes the company clearly is betting POWER9 will cut into Intel x86 sales. But that is not a given. Intel is rolling out its own family of advanced x86 Xeon machines under the Skylake code name. Different versions will be packaged and tuned to different workloads. They are rumored, at the fully configured high end, to be quite expensive. Just don’t expect POWER9 systems to be cheap either.

And the chip market is getting more crowded. As Timothy Prickett Morgan, analyst at The Next Platform noted, various ARM chips –especially ThunderX2 from Cavium and Centriq 2400 from Qualcomm –can boost non-X86 numbers and divert sales from IBM’s POWER9 family. Also, AMD’s Epyc X86 processors have a good chance of stealing some market share from Intel’s Skylake. So the POWER9 will have to fight for every sale IBM wants.

Morgan went on: IBM differentiated the hardware and the pricing with its NVLink versions, depending on the workload and the competition, with its most aggressive pricing and a leaner and cheaper microcode and hypervisor stack reserved for the Linux workloads that the company is chasing. IBM very much wants to sell its Power-Linux combo against Intel’s Xeon-Linux and also keep AMD’s Epyc-Linux at bay. Where the Power8 chip had the advantage over the Intel’s Haswell and Broadwell Xeon E5 processors when it came to memory capacity and memory bandwidth per socket, and could meet or beat the Xeons when it came to performance on some workloads that is not yet apparent with the POWER9.

With the POWER9, however, IBM will likely charge a little less for companies buying its Linux-only variants, observes Morgan, effectively enabling IBM to win Linux deals, particularly where data analytics and open source databases drive the customer’s use case. Similarly, some traditional simulation and modeling workloads in the HPC and machine learning areas are ripe for POWER9.

POWER9 is not one chip. Packed into the chip are next-generation NVIDIA NVLink and OpenCAPI to provide significantly faster performance for attached GPUs. The PCI-Express 4.0 interconnect will be twice the speed of PCI-Express 3.0. The open POWER9 architecture also allows companies to mix a wide range of accelerators to meet various needs. Meanwhile, OpenCAPI can unlock coherent FPGAs to support varied accelerated storage, compute, and networking workloads. IBM also is counting on the 300+ members of the OpenPOWER Foundation and OpenCAPI Consortium to launch innovations for POWER9. Much is happening: Stay tuned to DancingDinosaur

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

IBM Halts Losing Quarterly Slide

January 25, 2018

With all respects to Casey at Bat author Ernest Thayer, joy may have returned to Mudville. IBM finally broke its 22 consecutive quarters losing streak and posted positive results in 4Q 17.  Fourth-quarter revenue of $22.5 billion, up 4 percent but that was just the start.

Watson and Weather Co. track flu

IBM is counting on its strategic imperatives to come through big and they did in 2017. Full-year strategic imperatives revenue of $36.5 billion, up 11 percent; represents 46 percent of IBM revenue. Similarly, IBM is making some gains in the highly competitive cloud business where IBM is fighting to position itself among the top ranks of formidable cloud players—Google, Amazon, and Microsoft. IBM did quite respectably in the cloud, posting $17 billion in cloud revenue, up 24 percent year to year.

DancingDinosaur readers will be interested to know that some of IBM’s various business segments, which have been a steady drain on IBM revenue turned things around in the 4th quarter. For example, Systems (systems hardware and operating systems software) saw revenues of $3.3 billion, up 32 percent driven by growth in IBM Z, Power Systems, and storage. That’s important to readers charged with planning their organization’s future with the Z or Power machines. They now can be confident that IBM mightn’t the sell the business tomorrow as it did with the x86 systems.

So where might IBM go in the future. “Our strategic imperatives revenue again grew at a double-digit rate and now represents 46 percent of our total revenue, and we are pleased with our overall revenue growth in the quarter.” said Ginni Rometty, IBM chairman, president, and CEO.  She then continued: “During 2017, we established IBM as the blockchain leader for business. Looking ahead, we are uniquely positioned to help clients use data and AI to build smarter businesses.”

Added James Kavanaugh, IBM CFO: “Over the past several years we have invested aggressively in technology and our people to reposition IBM.  2018 will be all about reinforcing IBM’s leadership position,” he continued, “in key high-value segments of the IT industry, including cloud, AI, security and blockchain.”

IBM has done well in some business and technology segments. Specifically, the company reported gains in revenues from analytics, up 9 percent, mobile, up 23 percent, and security, up a whopping 132 percent.

Other segments have not done as well. Technology Services & Cloud Platforms (includes infrastructure services, technical support services, and integration software) continue to lose money. A number of investment analysts are happy with IBM’s financials but are not optimistic about what they portend for IBM’s future.

For instance, Bert Hochfeld, long/short equity, growth, event-driven, research analyst, writes in Seeking Alpha, “the real reason why strategic imperatives and cloud showed relatively robust growth last quarter has nothing to do with IBM’s pivots and everything to do with the success of IBM’s mainframe cycle. IBM’s Z system achieved 71% growth last quarter compared to 62% in the prior quarter. New Z Systems are being delivered with pervasive encryption, they are being used to support hybrid cloud architectures, and they are being used to support Blockchain solutions… Right now, the mainframe performance is above the prior cycle (z13) and consistent with the z12 cycle a few years ago. And IBM has enjoyed some reasonable success with its all-flash arrays in the storage business. Further, the company’s superscalar offering, Power9, is having success and, as many of its workloads are used for AI, its revenues get counted as part of strategic initiatives. But should investors count on a mainframe cycle and a high-performance computer cycle in making a long-term investment decision regarding IBM shares?

He continued: “IBM management has suggested that some of the innovations in the current product range including blockchain, cryptography, security and reliability will make this cycle different, and perhaps longer, then other cycles. The length of the mainframe cycle is a crucial component in management’s earnings estimate. It needs to continue at elevated levels at least for another couple of quarters. While that is probably more likely, is it really prudent to base an investment judgement on the length of a mainframe cycle?

Of course, many DancingDinosaur readers are basing their career and employment decisions on the mainframe or Power Systems. Let’s hope this quarter’s success encourages them; it sure beats 22 consecutive quarters of revenue declines.

Do you remember how Thayer’s poem ends? With the hopes and dreams of Mudville riding on him, it is the bottom of the 9th; Casey takes a mighty swing and… strikes out! Let’s hope this isn’t IBM.

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.


%d bloggers like this: