According to a recent Jefferies report, the fourth wave of computing has started and it is being driven by the adoption of IoT with parallel processing as the solution. Tectonic shifts in computing have been caused by major forces dating back to the 1960s.
With each shift, new solution providers have emerged as prominent suppliers. The latest power often cited with the fourth wave is Nvidia and its parallel processing platform for HPC and artificial intelligence (AI), namely GPUs and the CUDA programming platform. The growth of the data center segment of Nvidia’s business – from $339 million in …
The Rise Of The Fourth Wave Of Computing was written by Timothy Prickett Morgan at The Next Platform.
There are a lot of different ways to skin the deep learning cat. But for hyperscalers and cloud providers who want to use a single platform internally as well as providing deep learning services to customers externally, they really want to have as few different architectures as possible in their datacenters to maximize efficiencies and to lower both capital and operational costs. This is particularly true when the hyperscaler is also a cloud provider.
If Moore’s Law had not run out of gas – or at least shifted to lower octane fuel – then the choice would have been easy. …
Drilling Into Microsoft’s BrainWave Soft Deep Learning Chip was written by Timothy Prickett Morgan at The Next Platform.
Many have tried to wrench the door of the datacenter open with ARM processors, but Qualcomm, which knows a thing or two about creating and selling chips for smartphones and other client devices, has perhaps the best chance of actually selling ARM chips in volume inside of servers.
The combination of a rich and eager target market with a good product design tailored for that market and enough financial strength and stability to ensure many generations of development are what are necessary to break into the datacenter, and the “Falkor” cores that were unveiled this week at Hot Chips were …
ARM Servers: Qualcomm Is Now A Contender was written by Timothy Prickett Morgan at The Next Platform.
Propping up a successful silicon startup is no simple feat, but venture-backed Wave Computing has managed to hold its own in the small but critical AI training chip market–so far.
Seven years after its founding and the company’s early access program for beta machines based on its novel DPU manycore architecture is now open, which is prompting Wave to be more forthcoming about the system and chip architecture for deep learning-focused dataflow architecture.
Dr. Chris Nicol, Wave Computing CTO and lead architect of the Dataflow Processing Unit (DPU) admitted to the crowd at Hot Chips this week that maintaining funding …
First In-Depth View of Wave Computing’s DPU Architecture, Systems was written by Nicole Hemsoth at The Next Platform.
Professor Satoshi Matsuoka, of the Tokyo Institute of Technology (Tokyo Tech) researches and designs large scale supercomputers and similar infrastructures. More recently, he has worked on the convergence of Big Data, machine/deep learning, and AI with traditional HPC, as well as investigating the Post-Moore Technologies towards 2025.
He has designed supercomputers for years and has collaborated on projects involving basic elements for the current and more importantly future Exascale systems. I talked with him recently about his work with the Tsubame supercomputers at Tokyo Tech. This is the first in a two-part article. For background on the Tsubame 3 system …
Inside View: Tokyo Tech’s Massive Tsubame 3 Supercomputer was written by Nicole Hemsoth at The Next Platform.
In the U.S. it is easy to focus on our native hyperscale companies (Google, Amazon, Facebook, etc.) and how they design and deploy infrastructure at scale.
But as our regular readers understand well, the equivalent to Google in China, Baidu, has been at the bleeding edge with chips, systems, and software to feed its own cloud-delivered and research operations.
We’ve written much over the last few years about the company’s emphasis on streamlining deep learning processing, most notably with GPUs, but Baidu has a new processor up its sleeve called the XPU. For now, the device has just been demonstrated …
An Early Look at Baidu’s Custom AI and Analytics Processor was written by Nicole Hemsoth at The Next Platform.
In this fast-paced global economy, enhanced speed, productivity, and intelligence are more important than ever to success. Machines are now being leveraged to augment human capabilities in order to drive business growth or accelerate innovation. Businesses need leading-edge IT to achieve superhuman levels of performance.
Today’s enterprises and organizations are deploying high performance computing (HPC) technologies to reach the new frontier of IT intelligence. Backed by HPC solutions, users can leverage artificial intelligence (AI) tools to predict and solve problems in real time, streamline IT operations, and drive more informed, data-driven decision-making.
Streamlining Medical Research With Machine Learning was written by Timothy Prickett Morgan at The Next Platform.
There are so many companies that claim that their storage systems are inspired by those that have been created by the hyperscalers – particularly Google and Facebook – that it is hard to keep track of them all.
But if we had to guess, and we do because the search engine giant has never revealed the nitty gritty on the hardware architecture and software stack underpinning its storage, we would venture that the foundation of the current Google File System and its Colossus successor looks a lot like what storage upstart Datrium has finally, after many years of development, brought …
How To Do Stateless Compute, Clustered Storage Like A Hyperscaler was written by Timothy Prickett Morgan at The Next Platform.
Every new paradigm of computing has its own framework, and it is the adoption of that framework that usually makes it consumable for the regular enterprises that don’t have fleets of PhDs on hand to create their own frameworks before a technology is mature.
Serverless computing – something that strikes fear in the hearts of many whose living is dependent on the vast inefficiencies that still lurk in the datacenter – and event-driven computing are two different and often associated technologies where the frameworks are still evolving.
The serverless movement, which we have discussed before in analyzing the Lambda efforts …
Where Serverless And Event Driven Computing Collide was written by Timothy Prickett Morgan at The Next Platform.
The more things change, the more they stay the same.
While exascale supercomputers mark a next step in performance capability, at the broader architectural level, the innovations that go into such machines will be the result of incremental improvements to the same components that have existed on HPC systems for several years.
In large-scale supercomputing, many performance trends have jacked up capability and capacity—but the bottlenecks have not changed since the dawn of computing as we know it. Memory latency and memory bandwidth remain the gating factors to how fast, efficiently, and reliably big sites can run—and there is still …
An Exascale Timeline for Storage and I/O Systems was written by Nicole Hemsoth at The Next Platform.
One of the reasons that the University of California at Berkeley was been a hotbed of software technology back in the 1970s and 1980s is Michael Stonebraker, who was one of the pioneers in relational database technology and one of the industry’s biggest – and most vocal – shakers and movers and one of its most prolific serial entrepreneurs.
Like other database pioneers, Stonebraker read the early relational data model papers by IBMer Edgar Codd, and in 1973 started work on the Ingres database along IBM’s own System R database, which eventually became DB2, and Oracle’s eponymous database, which entered …
How Hardware Drives The Shape Of Databases To Come was written by Timothy Prickett Morgan at The Next Platform.
It would be surprising to find a Hadoop shop that builds a cluster based on the high-end 68+ core Intel Knights Landing processors—not just because of the sheer horsepower (read as “expense”) for workloads that are more data-intensive versus compute-heavy, but also because of a mismatch between software and file system elements.
Despite these roadblocks, work has been underway at Intel’s behest to prime Knights Landing clusters for beefier Hadoop/MapReduce and machine learning jobs at one of its parallel computing centers at Indiana University.
According to Judy Qiu, associate professor of intelligent systems engineering in IU’s computing division, it is …
Hadoop Platform Raised to Knights Landing Height was written by Nicole Hemsoth at The Next Platform.
There is no question any longer that flash memory has found its place – in fact, many places – in the datacenter, even though the debate is still raging about when or if solid state memory will eventually replace disk drives in all datacenters of the world.
Sometime between tomorrow and never is a good guess.
Flash is still a hot commodity, so much so that the slower-than-expected transition to 3D NAND has caused a shortage in supply that is driving up the price of enterprise-grade flash – unfortunately at the same time that memory makers are having trouble cranking …
The Ironic – And Fleeting – Volatility In NVM Storage was written by Timothy Prickett Morgan at The Next Platform.
If anything has become clear over the last several years of watching infrastructure and application trends among SaaS-businesses, it is that nothing is as simple as it seems. Even relatively straightforward services, like transactional email processing, have some hidden layers of complexity, which tends to equal cost.
For most businesses providing web-based services, the solution for complexity was found by offloading infrastructure concerns to the public cloud. This provided geographic availability, pricing flexibility, and development agility, but not all web companies went the cloud route out of the gate. Consider SendGrid, which pushes out over 30 billion emails per month. …
When Agility Outweighs Cost for Big Cloud Operations was written by Nicole Hemsoth at The Next Platform.
The software ecosystem in high performance computing is set to be more complex with the leaps in capability coming with next generation exascale systems. Among several challenges is making sure that applications retain their performance as they scale to higher core counts and accelerator-rich systems.
Software development and performance profiling company, Allinea, which has been around for almost two decades in HPC, was recently acquired by ARM to add to the company’s software ecosystem story. We talked with one of the early employees of Allinea, VP of Product Development, Mark O’Connor about what has come before—and what the software performance …
Performance Portability on the Road to Exascale was written by Nicole Hemsoth at The Next Platform.
In a properly working capitalist economy, innovative companies make big bets, help create new markets, vanquish competition or at least hold it at bay, and profit from all of the hard work, cleverness, luck, and deal making that comes with supplying a good or service to demanding customers.
There is no question that Nvidia has become a textbook example of this as it helped create and is now benefitting from the wave of accelerated computing that is crashing into the datacenters of the world. The company is on a roll, and is on the very laser-sharp cutting edge of its …
Nvidia Is A Textbook Case Of Sowing And Reaping Markets was written by Timothy Prickett Morgan at The Next Platform.
While it is not likely we will see large supercomputers on the International Space Station (ISS) anytime soon, HPE is getting a head start on providing more advanced on-board computing capabilities via a pair of its aptly-named “Apollo” water-cooled servers in orbit.
The two-socket machines, connected with Infiniband will put Broadwell computing capabilities on the ISS, mostly running benchmarks, including High Performance Linpack (HPL), the metric that determines the Top 500 supercomputer rankings. These tests, in addition to the more data movement-centric HPCG benchmark and NASA’s own NAS parallel benchmark will determine what performance changes, if any, are to be …
One Small Step Toward Supercomputers in Space was written by Nicole Hemsoth at The Next Platform.
In the following interview, Dr. Matt Leininger, Deputy for Advanced Technology Projects at Lawrence Livermore National Laboratory (LLNL), one of the National Nuclear Security Administration’s (NNSA) Tri Labs describes how scientists at the Tri Labs—LLNL, Los Alamos National Laboratory (LANL), and Sandia National Laboratories (SNL)—carry out the work of certifying America’s nuclear stockpile through computational science and focused above-ground experiments.
We spoke with Dr. Leininger about some of the workflow that Tri Labs scientists follow, how the Commodity Technology Systems clusters are used in their research, and how machine learning is helping them.
The overall goal is to demonstrate a …
A Look Inside U.S. Nuclear Security’s Commodity Technology Systems was written by Nicole Hemsoth at The Next Platform.
One of the luckiest coincidences in the past decade has been that the hybrid machines designed for traditional HPC simulation and modeling workloads. which combined the serial processing performance of CPUs and the parallel processing and massive memory bandwidth of GPUs, we also well suited to run machine learning training applications.
If the HPC community had not made the investments in hybrid architectures, the hyperscalers and their massive machine learning operations, which drive just about all aspects of their businesses these days, would not have seen such stellar results. (And had that not happen, many of us would have had …
Fujitsu Bets On Deep Leaning And HPC Divergence was written by Timothy Prickett Morgan at The Next Platform.
The golden grail of deep learning has two handles. On the one hand, developing and scaling systems that can train ever-growing model sizes is one concern. And on the other side, cutting down inference latencies while preserving accuracy of trained models is another issue.
Being able to do both on the same system represents its own host of challenges, but for one group at IBM Research, focusing on the compute-intensive training element will have a performance and efficiency trickle-down effect that speed the entire deep learning workflow—from training to inference. This work, which is being led at the T.J. Watson …
IBM Highlights PowerAI, OpenPower System Scalability was written by Nicole Hemsoth at The Next Platform.