The next $100B+ opportunity in cloud computing and its first killer app: Deep Learning

As we have entered into 2017, the enterprise software industry is at the inflection point for ubiquitous cloud adoption as part of the $4 trillion dollar enterprise IT market transformation.  The reasons for enterprise IT to adopt cloud are compelling, and many assume this shift has already happened.  We are, however, just at the very beginning of this shift and the impact is fundamentally transforming  all elements of IT.  Industry experts estimate we are in fact only at about 6% enterprise cloud penetration today. 1  Most businesses have yet to realize the full value of this transformation.  It is clear that adoption of cloud in core business activities by enterprise hardware and software IT players strongly correlates with improved business performance and shareholder value.

Market growth chart

Driving market growth through embracing cloud (normalized historical 5-year market cap) 2

For instance, if we look at market cap changes over the past 5 years, companies that have fully embraced cloud (Amazon [AMZN], Adobe [ADBE], Salesforce [CRM]) have seen great success.  Similarly, companies that have recently embraced cloud (Microsoft (MSFT)) are starting to see strong recent growth and those that have failed to embrace or execute on cloud (Oracle [ORCL], HP [HPQ], and IBM [IBM]) are struggling to build further business value.  To be sure, there are many other drivers for market cap growth in these companies, but level of cloud products, business models, and expertise has been a clear driving factor for success while cloud capability has now become table stakes for every enterprise software business.

During this multi-decade transformative shift to enterprise cloud adoption, several significant macro phases are taking place, together delivering the promise and business value of cloud in the enterprise.  The first two foundational phases are SaaS (Cloud 1.0) transforming the delivery and business model, and Big Data (Cloud 2.0) unlocking the value in massive data sets.  SaaS and Big Data have each already created over $100 billion in value in the enterprise.  Now, Big Compute, built on these critical foundational layers, is at an inflection point and poised to be the third.

Cloud infrastructure and Infrastructure-as-a-Service (IaaS) has been the critical underlying layer enabling enterprise IT cloud transformation.  Amazon Web Services, Microsoft Azure, and Google Cloud Platform have led the way in delivering the technological and infrastructure backbone for SaaS, Big Data, and now Big Compute.  As with SaaS and Big Data, Big Compute will drive technological disruption along with a new wave of category-defining companies to pursue the massive opportunity in this new breakout category.  Furthermore, the existing category leaders driving billions of dollars of compute heavy workload revenue in the legacy on-premise high performance computing (HPC) market are facing the innovator’s dilemma needing to reinvent their entire business to provide effective Big Compute solutions in the space – providing a unique opportunity for the most innovative companies to become category leaders.

Cloud 1.0: SaaS

Transformation of the business model and delivery of enterprise software

SaaS-native companies such as Salesforce, NetSuite, and Workday have built category-defining and leading SaaS solutions for CRM, ERP, and HR, respectively, through business model innovation and frictionless software delivery.  These SaaS leaders have leveraged the cloud to disrupt the legacy players historically dominating these enterprise software categories.

Legacy players have also had the opportunity to reinvent their business and implement SaaS for their product line.  For example, Adobe reinvented their strategy and successfully transformed their organization into a SaaS leader.  Even under the constraints of operating as a public company, Adobe successfully shifted from legacy on-premise software to a cloud SaaS subscription and delivery model.

Adobe stock chart

Adobe’s stock surge after embracing cloud SaaS model (Adobe historical stock price 2012-2017) 3

Adobe pivoted the business model for their leading product line from a $2,500 annual license to a $50-100/month monthly subscription.4  The product team also transformed the delivery model of their software to a browser-based solution with continuous updates.  Long story short – customers were delighted.  While sacrificing meaningful short-term revenue, Adobe created significantly more future value.  With the SaaS transformation, Adobe expanded their market opportunity by demonstrating broader market appeal and increased willingness to pay due to better customer value, along with the critical ability to capture that value over the long-term.

Cloud 2.0: Big Data

Scaling and connectivity enablement for the full-stack data layer

As cloud infrastructure matured to support the rapid growth of SaaS companies, Infrastructure-as-a-Service (IaaS) provided a fundamental layer of scalable and ubiquitous storage and databases. This broad infrastructure capability enabled dramatic innovation on the data layer and is driving the Big Data transformation.

Leaders in the Big Data category such as Splunk, Cloudera, and Palantir have transformed our ability to extract insights from the past by scaling, manipulating, and connecting enterprise data sets.  At a basic level, Splunk provides the ability to drive insights from large and disparate enterprise application logs, Cloudera took this to the next level with the ability to deploy distributed storage and processing clusters for the enterprise, and finally Palantir has developed proprietary stacks to connect siloed and unstructured data sets in the public and private sectors.  Fundamentally, Big Data is a horizontal capability that can be applied across the enterprise to distill insights from existing data sets.

Heat map

Heat map showing Big Data opportunities across all major industries (For a more detailed analysis of Big Data, the comprehensive McKinsey Global Institute publication is an excellent resource: “Big data: The next frontier for innovation, competition, and productivity”)5

Cloud 3.0: Big Compute

Scaling and workload enablement for the full-stack compute layer

Big Compute is the next transformational shift for enterprise cloud computing.  Just like Big Data removed constraints on data and transformed major enterprise software categories, Big Compute eliminates constraints on compute hardware and provides the ability to scale computational workloads seamlessly on workload-optimized infrastructure configurations without sacrificing performance.

“Supercomputer in your pocket” fallacy

Moore’s Law and IaaS cloud compute have resulted in the perception that “everyone has a supercomputer in their pocket” and ubiquitous compute power is universally accessible by all.  However, while compute capabilities have been growing at an exponential rate and have become much more accessible, computing requirements for applications have also grown dramatically to meet the insatiable demand for complex, next-generation algorithms.  In fact, the iPhone “supercomputer in your pocket” is really a supercomputer from 25+ years ago,6 and in that same time the computing requirements for the software ecosystem have grown at a rate on par with or greater than Moore’s Law.

Many critical enterprise problems and innovations are still compute-bound

Despite popular belief, many of the world’s most challenging software problems are still compute-bound.  Since the dawn of enterprise software, compute limitations have highly constrained both the applications and scope of the work due to the multi-million dollar expense and expertise required for implementation of supercomputers and high performance computing systems in the enterprise.

High performance computing is used heavily in most major Fortune 500 companies, including verticals such as aerospace, automotive, energy, life sciences, and semiconductors. In almost all of these verticals, companies are faced with significant constraints in compute capacity. For example, in aerospace design, large-scale problems have complex computational fluid dynamics (CFD) algorithms that can take days or weeks to solve on large high performance computing clusters.  In automotive crash testing simulation analysis, scaling finite element analysis (FEA) physics to millions of components and interactions creates a massive computational problem.  And in life sciences, the massive computational requirements of molecular dynamics (MD) simulation restricts computational drug discovery, one of the largest opportunities for disruption for pharmaceutical companies.

Cloud computing innovations such as SaaS, Big Data, and IaaS are the building blocks that enable the Big Compute category.  A comprehensive Big Compute stack now enables frictionless scaling, application-centric compute hardware specialization, and performance-optimized workloads in a seamless way for both software developers and end-users.  Specifically, Big Compute transforms a broad set of full-stack software services on top of specialty hardware into a software-defined layer, which enables programmatic high performance computing capabilities at your fingertips, or more likely, as back-end function evaluations part of software you touch every day.  This approach flips the legacy approach of a one-size-fits-all compute cluster for a broad set of applications upside down, into an a-la-carte solution where the compute workloads are profiled and deployed onto an optimal specialized hardware solution for tailored to the workload.  Big Compute turns a homogenous cluster full of performance and cost trade-offs, into a heterogenous solution with no compromises.

Hardware fragmentation and specialization enabling Big Compute

Big Compute is a horizontal technology that enables enterprises to scale complex algorithms and workloads on flexible, specialized compute infrastructure.  Specialized compute infrastructure is a critical element for success, since there is a multiple magnitude performance gap between commodity infrastructure and specialty servers.  Many components of the servers can be specialized, such as processors, networking, memory, storage, etc.  Big Compute is democratizing the access to this level of specialty hardware from a software-defined point of view, thus enabling the ability to spin up a $10 million high performance computing cluster for just a few thousands of dollars per hour and integrate this capability into any enterprise software application while, most critically, still meeting the cutting-edge performance expectations for high-end applications.

Within the data center there are many areas of tuning and optimization from the physical racks, to the networking, and down to the specific server components.  For example, there is already a broad spectrum of processor capabilities that is fragmenting at an increasing rate.  The specialized capabilities of these processors is critical for specific Big Compute workloads that need to be mapped to the right architectures, depending on their algorithms and implementation.

Mapping timeline

Example mapping of increasing fragmentation in the spectrum of processor capabilities

Big Compute enables users, software applications, and algorithms to seamlessly take advantage of these capabilities without the need to understand or deploy the middleware and libraries required.  This accelerates the adoption cycle and deployment of specialized hardware capabilities in IaaS and data centers, but more importantly enables a dramatic new category of Big Compute enterprise software applications and tools.

Fundamentally, Big Compute provides the layer of software-defined computing that allows a more application-centric compute approach in the enterprise, bringing seamless optimization and scalability of the stack, while ultimately democratizing the Big Compute capability to developers and end-users.  This capability is resulting in a dramatic transformation within the enterprise, from multi-disciplinary teams of highly-trained experts in hardware, middleware, and the domain-specific software algorithms supported by large investments in computing resources, to a seamless service which allows the average enterprise user to focus on the application layer while relying on a highly complex but seamless Big Compute layer for execution.  This transformation allows organizations to move up a level of abstraction, from formulating workloads to meet the underlying hardware capabilities, to solving the higher level problems without any compute constraints.

The rise of Big Compute is not only a fundamental new capability for all enterprise software applications, but it provides the opportunity to shift from backward-looking analysis (as is typical for Big Data), to forward-looking analytics and simulations to drive predictive insights and predictions of the future states.  In fact, Big Compute can provide simulations that generate data sets which leverage Big Data tools to evaluate the potential quality of a future state a simulation generated.  An asynchronous process will work for the largest problems, but this will move toward real-time where the Big Compute stack can process faster than the required response time.  For example, in Deep Learning training (asynchronous) and inference (real-time), both running on a Big Compute stack of capabilities local on the edge, or centralized in the cloud if latencies allow.

Deep Learning, the first Big Compute killer app

The first real breakout in Big Compute has been the category of deep learning software. Deep learning is a subset of machine learning algorithms that requires massively parallel computations and is thus a great fit for GPU hardware, historically designed for parallel graphics thread processing.  Deep learning algorithms have been around for decades, but their recent success is attributable to highly specialized GPU hardware tailored for this workload along with the ability to spin up a turnkey full-stack solution on which the software algorithms operate without needing to be an expert on the implementation, parallelization, and orchestration of the algorithms on the specialty hardware.

Deep learning tools are now recognized as a must-have horizontal solution for the enterprise. These tools provide analytics, insights, and intelligence capabilities that were previously unattainable without massive investments and expertise across the stack, but are now universally accessible with a Big Compute stack of specialized hardware, middleware, and integrated algorithms.  These machine learning algorithms are now universally accessible on the latest generation of specialized scalable GPUs and can quickly be implemented as a solution to complement existing software capabilities with algorithmic intelligence across the enterprise, leveling up internal capabilities.  As the leader in the GPU market, NVIDIA has been “selling pickaxes during a gold rush” and seen tremendous growth and adoption fueled by the Big Compute layer making these specialized hardware capabilities quickly and easily accessible to anyone looking to use deep learning tools.

NVIDIA chart

Dramatic growth and success of NVIDIA, leaders in GPU chipsets (NVIDIA Historical Stock Price 2012 -2017) 7

Google (Alphabet) has famously seen deep learning as such a fundamental capability to remain the leader in search.  It has been considered so instrumental to their research and development efforts that they have implemented the TensorFlow algorithm broadly across their broad portfolio of endeavors unrelated to search.  So instrumental, that they have even developed their own custom Tensor Processing Unit (TPU) chip to accelerate the performance to levels not attainable with standard chipsets.  This clearly illustrates the critical importance of specialized hardware in the Big Compute stack for optimal algorithmic performance to drive new capabilities and innovation.

Deep Learning chart

Growing use of Deep Learning at Google 8

At the start of 2017, deep learning is likely at the peak of its hype cycle, but there is clear fundamental long-term value being created.  At a minimum, deep learning is creating value as a back-end process to optimize highly complex situations in which the datasets are too large for deterministic algorithms.  For instance, it is likely that Google recouped their $500 million acquisition of DeepMind through a single implementation of their machine learning tools to improve their data center cooling.9  It is also likely that deep learning will provide new capabilities and accelerate further innovation in autonomous driving, drug discovery, medical imaging, robotics, and many other exciting areas.  Deep learning is just a single software application category powered and enabled by the Big Compute stack.

The emerging Big Compute stack

The Big Compute stack is the critical enabling layer to bring Big Compute capabilities to any software application and handle the management overhead complexity to seamlessly execute workloads on the broad selection of specialized infrastructure.  There are entirely new application categories that have yet to be unlocked with the Big Compute stack, and analogous to deep learning, have algorithms which have historically been compute-bound requiring specialized hardware, complex middleware, and comprehensive tuning and optimization of the stack to become commercially viable to the enterprise.

Big Compute has the potential to bring a magnitude larger fundamental impact across the entire economic landscape beyond that which Big Data already has.  Not only will Big Compute bring the computation and algorithmic capabilities to amplify the power and impact of the Big Data analytics tools, but Big Compute will also drive an entirely new category of capabilities that were not possible before.

The success of deep learning as the first Big Compute killer app signals an inflection toward exponentially greater use of large-scale, specialized computing.  As with Big Data, Big Compute stack capabilities will be a must-have in the enterprise.

Here at Rescale, we believe Big Compute is the foundation upon which new future innovations will be built – driven by the algorithms and software that have historically been highly constrained by deep expertise and significant upfront investment, but have now been unleashed with the Big Compute stack.  We are building the universal platform to bring Big Compute capabilities to the enterprise – a critical capability we believe will be necessary to drive most major new innovations in the leading Fortune 500 enterprises across industry verticals.  From space exploration to electric vehicles to computational drug discovery, we are working with the leaders of industries which are implementing the Big Compute stack as a competitive advantage in their respective verticals.

The rise of Big Compute will unlock a broad spectrum of innovation and incredible business value in the enterprise previously gated by specialized hardware capability, accessibility, and scalability.  While late adopters of Big Compute capabilities will face tougher competition for undifferentiated capabilities, early leaders are finding the unique opportunity to leverage the existing internal expertise and core competencies of their organization and supercharge these with Big Compute capabilities to create the future innovations that will transform our world.

We look forward to a very exciting year of Big Compute in 2017 and beyond!

-Joris Poort, CEO, Rescale

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This article was written by Joris Poort.

supercomputing

We’re at an inflection point in the computer industry.

Today’s iPhones hold more processing power than the best Apollo-era computers. Moore’s Law tells us that the number of transistors on integrated circuits doubles every two years. We hold an unprecedented amount of computing power at our fingertips, and it’s still growing at an exponential rate.

This holds true for the engine of innovation: high-performance computing (HPC). HPC refers to the use of supercomputing and parallel processing techniques to solve massive, complex computational problems. Companies such as Cray, IBM, Intel, SGI, Sun and Thinking Machines have all made breakthroughs in this storied endeavor over the years, pushing the boundaries of what can be analyzed and designed using computers.

As a former engineer at Boeing, where I helped design the wing of the 787 Dreamliner, I can tell you the demand for compute power is increasing exponentially. As IT services migrate to the cloud, not only is raw compute power becoming highly accessible, but it is also available in hourly, pay-as-you-go models. Infinite compute power is now at users’ fingertips, enabling instant scalability, shortened product cycles and improved product quality. InfiniBand networking, GPUs and bare metal servers that were once considered too expensive or out of reach can now be easily accessed from the cloud.

Cloud computing has created a disruptive inflection point for HPC. By delivering a broad library of high-end HPC applications on the cloud, my company, Rescale, accelerates the adoption and democratization of HPC globally. We have undertaken the ambitious task of helping customers of all sizes deploy, monitor, manage and optimize their HPC resources, whether they are in the cloud, on premises, or in a hybrid environment.

At Rescale, we have built a cloud HPC platform that delivers:

  • A turnkey, zero-IT footprint solution that meets the highest security standards
  • A natively-integrated, pre-configured library of more than 180 simulation and machine learning software packages with on-demand and bring-your-own-license (BYOL) licensing options
  • The largest global network of HPC resources (57 data centers in nearly two dozen locations) with support across multiple IT environments

We’ve not only removed the barriers of entry to HPC, but we have also simplified the delivery of HPC services in an a la carte fashion. From automotive design to drug discovery to even actual rocket science, we’re empowering our customers to be leaders in their fields, accomplish more and innovate faster.

We are on a journey at Rescale to build the platform that enhances and accelerates the ideas of the world’s top engineers, scientists and innovators. Join us in empowering global innovation.

Rescale, a global leader in cloud HPC, recently selected IBM Cloud as a preferred cloud computing provider, expanding its global HPC infrastructure network. Explore high performance computing solutions on IBM Cloud.

This article was written by Joris Poort.

blog

Looking forward to 2015, we have some exciting new updates in the works here at Rescale.  Primarily based on customer feedback and alignment with our long term roadmap, you can expect some major updates in the following areas:

New Hardware & Lower Prices

We already have a great selection of hardware configurations at various competitive price points.  That said, one of the big benefits of being a Rescale customer is that you can take advantage of the new hardware technologies and reduced pricing as older configurations depreciate.  Very soon, we will be launching the newest Intel Haswell processors on Rescale which should provide up to 20% performance improvements for a typical simulation.  As we have consistently done in the past, we will continue to reduce prices as older hardware depreciates and pass the cost savings along to our customers.  Price sensitive users will also benefit from increased capacity of our Low Priority pool.

Expanded Network of Data Centers

As our international customers know well, we already have data centers in Asia and Europe.  With continued growth in these new regions, so does the requirement for certain companies to have their data reside locally in their specific region.  We are continuing to partner and build out capacity in new data centers to complement the existing 20+ locations we already have available today.

Enhanced Functionality for Developers

Just in the past few days, we’ve opened up our API and CLI to the public as the Rescale toolbelt for developers.  These exciting new tools make it possible for any engineer or developer to programmatically access the Rescale platform.  Now anyone can deeply integrate Rescale functionality into their internal systems.

Improved Visualization for Pre- and Post-Processing

New visualization, pre-processing, and post-processing tools are critical to our customers productivity, allowing for quick manipulation and investigation of models without the need to transfer the files away from Rescale’s platform.  Soon we will be launching full remote visualization capability for all users, allowing for easy pre- and post-processing without the need to transfer files locally to customers’ desktop or laptop.

Software and Scheduler Integrations

Deeper integration with the software packages allows our customers to more easily execute simulations directly from the GUIs of our partners and integrate the workflow capabilities seamlessly into the Rescale interface.  Hybrid cloud environments will be able to be deployed turnkey with the new native scheduler integrations we will be releasing in 2015.

Enhanced Enterprise Administration

Administrative functionality for managing Rescale within the enterprise is a big focus for us in 2015.  Customers can expect expanded administrative functionality, including, budgeting tools, group and user management, administrative monitoring dashboards, and much more.

If you’re interested in becoming an early adopter for any of the updates mentioned above, please don’t hesitate to contact us directly at support@rescale.com and we will make sure you are one of the first to be able to give it a try!

This article was written by Joris Poort.

blog

Every engineer running simulations has experienced the struggle of formulating a computation or simulation job and being unable to solve it due to hardware or software resource constraints.  At Rescale, our goal is to eliminate these constraints and ultimately empower engineers to move up a level of abstraction toward simulation- and model-based design [mbd].  On-demand access to hardware and software resources changes how engineers can approach and solve the next generation of engineering challenges.

Most engineers have a basic level of access to internal high performance computing (HPC) clusters and simulation software–but access to on-demand resources opens up a broad set of new engineering problems that can be solved.  Often, engineers are trained to solve their problems within these given constraints and do the best job they can with set timelines. This results in an under-representation for the real demand and opportunity if access to hardware and software were ubiquitous.  Access to on-demand hardware and simulation software resources dramatically changes how engineers are able to utilize simulation in the product development process.

Lowering barriers to entry
At the most basic level, access to on-demand hardware and software resources dramatically lowers the barrier to entry for engineers to start running computational analyses. Significant upfront investments in hardware infrastructure or annual software licensing fees are not required with on-demand access as they have been in the past.

Higher fidelity models
Simulation models with higher fidelity designs can often give more accurate and higher confidence simulation results.  With access to variable on-demand HPC resources, engineers can significantly increase the fidelity of simulation models without the need to invest in additional HPC hardware.

Broader design space exploration
Linearly scaling out simulations through parameter sweeps or designs of experiment (DOE) can be an extremely powerful way to better understand how specific elements of a model affect the outcome and provide critical engineering insights.  Access to on-demand resources has a dramatic impact on these applications–commoditizing the incremental cost of a simulation and providing engineers and scientists the ability to run as many scenarios concurrently as they desire.

Algorithmic design development
Engineering models for complex systems have millions of parts and parameters which can be varied by engineers–often making a complete brute force search [bfs] of the entire design space impractical.  Optimization techniques, statistical methods, machine learning algorithms, and other tools can be utilized to more intelligently explore these design spaces.  A single function evaluation for these tools can involve the execution of a full simulation model that requires significant hardware and software resources.  The ability to seamlessly execute these simulations is a critical enabler for simulation workflows that use this type of algorithmic approach.

Multidisciplinary analysis and co-simulation
Most complex products have several different engineering or scientific disciplines that influence the design.  Simulation tools are often utilized for multiple components of the design process, providing the opportunity to combine the simulation tools (e.g. using functional mockup interfaces [fmi]) to understand how design changes affect each discipline independently while exploring correlations and interactions between them.  When multiple simulation tools are connected together, the software and hardware requirements increase exponentially.  These types of multi-discipline approaches are often used in conjunction with algorithmic exploration due to the highly complex interactions between parameters (e.g. multidisciplinary design optimization [mdo]).  This often requires resource requirements beyond what has been traditionally accessible.  Co-simulation is another example of this, but a more tightly integrated concurrent approach.

Engineering management and budgeting
Executives and engineering management often undergo a very difficult resource allocation and budgeting process in an attempt to predict the hardware and software requirements for a one- to three-year period while investing in capital expenditures for HPC clusters and annual software licenses accordingly.  On-demand hardware and software resources allow for direct accounting and very granular budgeting approach.  With the ability to calculate returns on investment (ROI) directly for simulations and understand the computing costs on a project basis, management can empower engineering teams to dynamically allocate resources as engineering and business needs change.  This approach provides engineers the leverage to objectively assess the value of simulation tools and directly tie these investments to results.

Engineering and scientific simulation is becoming the critical tool that fuels the innovation pipeline for leading companies in industries and applications ranging from the development of new spacecraft and unmanned aerial vehicles (UAVs) in aerospace to revolutionary new drug discovery techniques in life sciences to advanced electronic design automation (EDA) for the next generation of semiconductor devices.  Access to on-demand resources for hardware and software provides engineers the tools to move up a level of abstraction in the development process. This dramatically improves the ability to handle large complex models and utilize simulation driven development as a key asset for innovation and breakthrough technologies.

[mbd]: http://en.wikipedia.org/wiki/Model-based_design
[bfs]: http://en.wikipedia.org/wiki/Brute-force_search
[fmi]: http://en.wikipedia.org/wiki/Functional_Mock-up_Interface
[mdo]: http://en.wikipedia.org/wiki/Multidisciplinary_design_optimization

This article was written by Joris Poort.