The State of Enterprise Machine Learning

For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools.

To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a …

The State of Enterprise Machine Learning was written by Nicole Hemsoth at The Next Platform.

ARM Carves Path to IoT Driven Cloud Business

Chip design firm ARM is getting into the cloud business. The company whose designs power almost all of the world’s cell phones, has steadily pushed its designs into new ventures, including servers, as we have covered extensively. But on Tuesday it branched into something completely different.

It is selling cloud services to help a new breed of customers such as appliance makers connect devices to the internet of things in a secure fashion. The ARM mbed cloud is now available for customers that want to create a connected device that is easier to secure, track and get online.

The …

ARM Carves Path to IoT Driven Cloud Business was written by Nicole Hemsoth at The Next Platform.

ARM Carves Path to IoT Driven Cloud Business

Chip design firm ARM is getting into the cloud business. The company whose designs power almost all of the world’s cell phones, has steadily pushed its designs into new ventures, including servers, as we have covered extensively. But on Tuesday it branched into something completely different.

It is selling cloud services to help a new breed of customers such as appliance makers connect devices to the internet of things in a secure fashion. The ARM mbed cloud is now available for customers that want to create a connected device that is easier to secure, track and get online.

The …

ARM Carves Path to IoT Driven Cloud Business was written by Nicole Hemsoth at The Next Platform.

How Long Before Burst Buffers Push Past Supercomputing?

Over the last couple of years, we have been watching how burst buffers might be deployed at some of the world’s largest supercomputer sites. For some background on how these SSD devices boost throughput on large machines and aid in both checkpoint and application acceleration, you can read here, but the real question is how these might penetrate the market outside of the leading supercomputing sites.

There is clear need for burst buffer technology in other areas where users are matching a parallel file system with SSDs. While that is still an improvement over the disk days, a lot …

How Long Before Burst Buffers Push Past Supercomputing? was written by Nicole Hemsoth at The Next Platform.

How Long Before Burst Buffers Push Past Supercomputing?

Over the last couple of years, we have been watching how burst buffers might be deployed at some of the world’s largest supercomputer sites. For some background on how these SSD devices boost throughput on large machines and aid in both checkpoint and application acceleration, you can read here, but the real question is how these might penetrate the market outside of the leading supercomputing sites.

There is clear need for burst buffer technology in other areas where users are matching a parallel file system with SSDs. While that is still an improvement over the disk days, a lot …

How Long Before Burst Buffers Push Past Supercomputing? was written by Nicole Hemsoth at The Next Platform.

Turning OpenMP Programs into Parallel Hardware

Systems built from commodity hardware such as servers, desktops and laptops often contain so-called general-purpose processors (CPUs)—processors that specialize in doing many different things reasonably well. This is driven by the fact that users often perform various types of computations; the processor is expected to run an Operating System, browse the internet and even run video games.

Because general-purpose processors target such a broad set of applications, they require having hardware that supports all such application areas. Since hardware occupies silicon area, there is a limit to how many of these processor “cores” that can be placed—typically between 4 and …

Turning OpenMP Programs into Parallel Hardware was written by Nicole Hemsoth at The Next Platform.

Future Economies of Scale for Quantum Computing

Clustering together commodity servers has allowed the economies of scale that enable large-scale cloud computing, but as we look to the future of big infrastructure beyond Moore’s Law, how might bleeding edge technologies capture similar share and mass production?

To say that quantum computing is a success simply because a few machines manufactured by quantum device maker, D-Wave, would not necessarily be accurate. However, what the few purchases of such machines by Los Alamos National Lab, Google, and Lockheed Martin do show is that there is enough interest and potential to get the technology off the ground and …

Future Economies of Scale for Quantum Computing was written by Nicole Hemsoth at The Next Platform.

How Microsoft Fell Hard for FPGAs

Microsoft’s embrace of programmable chips knowns as FPGAs is well documented. But in a paper released Monday the software and cloud company provided a look into how it has fundamentally changed the economics of delivering hardware as a service thanks to these once-specialty pieces of silicon.

Field programmable gate arrays, or FPGAs, are chips where the logic and networking functions can be reconfigured after they’ve been manufactured. They are typically larger than similarly functioning chips and traditionally were made for small jobs where the performance advantage outweighed the higher engineering cost associated with designing them.

But thanks to the massive …

How Microsoft Fell Hard for FPGAs was written by Nicole Hemsoth at The Next Platform.

IEEE Reboots, Scans for Future Architectures

If there is any organization on the planet that has had a closer view of the coming demise of Moore’s Law, it is the Institute of Electrical and Electronics Engineers (IEEE). Since its inception in the 1960s, the wide range of industry professionals have been able to trace a steady trajectory for semiconductors, but given the limitations ahead, it is time to look to a new path—or several forks, to be more accurate.

This realization about the state of computing for the next decade and beyond has spurred action from a subgroup, led by Georgia Tech professor Tom Conte and …

IEEE Reboots, Scans for Future Architectures was written by Nicole Hemsoth at The Next Platform.

Disruptive Technologies on the Post Exascale Horizon

Although the timeline for reaching exascale class computing continues to stretch farther into the future, research teams are keeping an eye on what technologies will shape the machines of the post-exascale timeframe, which is in the 2022-2030 timeframe.

While many of the technologies stated in a comprehensive report about post-exascale supercomputers are already in process, albeit in various stages of development and adoption, there is little consensus about which mode of computing will lead us into an era of unprecedented data and simulation potential. Still, the effort on behalf of the EuroLab-4-HPC program is notable in its divisions between where …

Disruptive Technologies on the Post Exascale Horizon was written by Nicole Hemsoth at The Next Platform.

Ganging up Accelerators to Beat Scale Limits

It is not news that offloading work from CPUs to GPUs can grant radical speedups, but what can come as a surprise is that scaling of these workloads doesn’t change just because they run faster. Moving beyond a single node means encountering a performance wall, that is, unless something can glue everything together so it can scale at will.

There are already technologies that can take multiple units of compute and have them share work from supercomputing and other areas (consider ScaleMP, for instance) but there are limitations to these approaches and thus far, they haven’t extended to meet the …

Ganging up Accelerators to Beat Scale Limits was written by Nicole Hemsoth at The Next Platform.

Memory is the Next Platform

A new crop of applications is driving the market along some unexpected routes, in some cases bypassing the processor as the landmark for performance and efficiency. While there is no end in sight for the CPUs dominant role, at least not until Moore’s Law has been buried along the roadside, there is another path—this time, down memory lane.

Just as machine learning oriented applications represent the next development platform, memory appears to be the next platform for compute. While this won’t extend to all application areas, given the thrust of machine learning and memory bandwidth and capacity-strained applications, the more …

Memory is the Next Platform was written by Nicole Hemsoth at The Next Platform.

Exascale Code Performance and Portability in the Tune of C

Among the many challenges ahead for programming in the exascale era is the portability and performance of codes on heterogeneous machines.

Since the future plan for architectures includes new memory and accelerator capabilities, along with advances in general purpose cores, developing on a solid base that offers flexibility and support for many hardware architectures is a priority. Some contend that the best place to start is with C++, which has been gathering steam in HPC in recent years.

As our own Douglas Eadline noted back in January, choosing a programming language for HPC used to be an easy task. Select …

Exascale Code Performance and Portability in the Tune of C was written by Nicole Hemsoth at The Next Platform.

Exascale Capabilities Underpin Future of Energy Sector

Oil and natural resource discovery and production is an incredibly risky endeavor, with the cost of simply finding a new barrel of oil tripling over the last ten years. Discovery teams want to ensure they are only drilling in the most lucrative locations, which these days means looking to increasingly inaccessible (for a bevy of reasons) sources for hydrocarbons.

Even with renewable resources like wind, there are still major financial risks. An accurate prediction of shifting output and location for expensive turbines are two early-stage challenges, and maintaining, monitoring, and optimizing those turbines is an ongoing pressure.

The common thread …

Exascale Capabilities Underpin Future of Energy Sector was written by Nicole Hemsoth at The Next Platform.

Baidu’s New Yardstick for Deep Learning Hardware Makers

When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. Specifically, the company’s Silicon Valley AI Lab (SVAIL) has been the center of some noteworthy work on GPU-based deep learning as well as exploratory efforts using novel architectures specifically for ultra-fast training and inference.

It stands to reason that teams at SVAIL don’t simply throw hardware at the wall to see what sticks, even though they seem to have more to toss around than most. Over the last couple of years, they have broken down …

Baidu’s New Yardstick for Deep Learning Hardware Makers was written by Nicole Hemsoth at The Next Platform.

Baking Specialization into Hardware Cools CPU Concerns

As Moore’s Law spirals downward, ultra-high bandwidth memory matched with custom accelerators for specialized workloads might be the only saving grace for the pace of innovation we are accustomed to.

With advancements on both the memory and ASIC sides driven by machine learning and other workloads pushing greater innovation, this could be great news for big datacenters with inefficient legions of machines dedicated to ordinary processing tasks—jobs that could far more efficient with more tailored approaches.

We have described this trend in the context of architectures built on stacked memory with FPGAs and other custom accelerators inside recently, and we …

Baking Specialization into Hardware Cools CPU Concerns was written by Nicole Hemsoth at The Next Platform.

The Three Great Lies of Cloud Computing

It’s elastic! It’s on-demand! It scales dynamically to meet your needs! It streamlines your operations, gives you persistent access to data, and it’s always, always cheaper. It’s cloud computing, and it’s here to save your enterprise.

And yet, for all the promise of cloud, there are still segments of IT, such as HPC and many categories of big data analytics, that have been resistant to wholesale outsourcing to public cloud resources. At present cloud computing makes up only 2.4% of the HPC market by revenue, and although Intersect360 Research is forecast its growth at a robust 10.9%, that still keeps …

The Three Great Lies of Cloud Computing was written by Nicole Hemsoth at The Next Platform.

The Next Wave of Deep Learning Applications

Last week we described the next stage of deep learning hardware developments in some detail, focusing on a few specific architectures that capture what the rapidly-evolving field of machine learning algorithms require. This week we are focusing in on a trend that is moving faster than the devices can keep up with; the codes and application areas that are set to make this market spin in 2017.

It was with reserved skepticism that we listened, not even one year ago, to dramatic predictions about the future growth of the deep learning market—numbers that climbed into the billions despite the fact …

The Next Wave of Deep Learning Applications was written by Nicole Hemsoth at The Next Platform.

So, You Want to Program Quantum Computers?

The jury is still out when it comes to how wide-ranging the application set and market potential for quantum computing will be. Optimistic estimates project that in the 2020s it will be a billion-dollar field, while others expect the novelty will wear off and the one company behind the actual production of quantum annealing machines will go bust.

Ultimately, whichever direction the market goes with quantum computing will depend on two things. First, the ability for applications of sufficient value to warrant the cost of quantum systems have to be in place. Second, and connected to that point, is the …

So, You Want to Program Quantum Computers? was written by Nicole Hemsoth at The Next Platform.

When Will Containers Be the Total Package for HPC?

While containers are old news in enterprise circles, by and large, high performance computing centers have just recently begun to consider packaging up their complex applications. A few centers are known for their rapid progress in this area, but for smaller sites, especially those that serve users from a diverse domain base via medium-sized HPC clusters, progress has been slower—even though containers could zap some serious deployment woes and make collaboration simpler.

When it comes to containers in HPC, there are a couple of noteworthy efforts that go beyond the more enterprise-geared Docker and CoreOS options. These include Shifter out …

When Will Containers Be the Total Package for HPC? was written by Nicole Hemsoth at The Next Platform.