Being too dependent on one source for a key component is not just a bad idea because of supply chain risks, but because it can result in higher prices.
Intel customers don’t need to be reminded of the lack of direct competitive pressure in the X86 chip market for servers, because they remember what competition that felt like. And customers and system makers that had taken a risk with AMD Opteron processors a decade ago don’t need to be reminded of either of these facts, particularly after AMD walked away from the server business in the wake of technical problems …
AMD Strikes A Balance – And Strikes Back – With Zen was written by Timothy Prickett Morgan at The Next Platform.
For those interested in novel architectures for large-scale datacenters and complex computing domains, this year has offered plenty of fodder for exploration.
From a rise in custom ASICs to power next generation deep learning, to variations on FPGAs, DSPs, and ARM processor cores, and advancements in low-power processors for webscale datacenters, it is clear that the Moore’s Law death knell is clanging loud enough to spur faster, more voluminous action.
At the Hot Chips conference this week, we analyzed the rollout of a number of new architectures (more on the way as the week unfolds), but one that definitely grabbed …
Inside the Manycore Research Chip That Could Power Future Clouds was written by Nicole Hemsoth at The Next Platform.
While much of the work at Baidu we have focused on this year has centered on the Chinese search giant’s deep learning initiatives, many other critical, albeit less bleeding edge applications present true big data challenges.
As Baidu’s Jian Ouyang detailed this week at the Hot Chips conference, Baidu sits on over an exabyte of data, processes around 100 petabytes per day, updates 10 billion webpages daily, and handles over a petabyte of log updates every 24 hours. These numbers are on par with Google and as one might imagine, it takes a Google-like approach to problem solving at …
Baidu Takes FPGA Approach to Accelerating SQL at Scale was written by Nicole Hemsoth at The Next Platform.
Intel has the kind of control in the datacenter that only one vendor in the history of data processing has ever enjoyed. That other company is, of course, IBM, and Big Blue wants to take back some of the real estate it lost in the datacenters of the world in the past twenty years.
The Power9 chip, unveiled at the Hot Chips conference this week, is the best chance the company has had to make some share gains against X86 processors since the Power4 chip came out a decade and a half ago and set IBM on the path to …
Big Blue Aims For The Sky With Power9 was written by Timothy Prickett Morgan at The Next Platform.
Over the last couple of years, the idea that the most efficient and high performance way to accelerate deep learning training and inference is with a custom ASIC—something designed to fit the specific needs of modern frameworks.
While this idea has racked up major mileage, especially recently with the acquisition of Nervana Systems by Intel (and competitive efforts from Wave Computing and a handful of other deep learning chip startups), yet another startup is challenging the idea that a custom ASIC is the smart, cost-effective path.
The argument is a simple one; deep learning frameworks are not unified, they are …
FPGA Based Deep Learning Accelerators Take on ASICs was written by Nicole Hemsoth at The Next Platform.
If the ARM processor in its many incarnations is to take on the reigning Xeon champ in the datacenter and the born again Power processor that is also trying to knock Xeons from the throne, it is going to need some bigger vector math capabilities. This is why, as we have previously reported, supercomputer maker Fujitsu has teamed up with ARM holdings to add better vector processing to the ARM architecture.
Details of that new vector format, known as Scalable Vector Extension (SVE), were revealed by ARM at the Hot Chips 28 conference in Silicon Valley, and any licensee …
ARM Puts Some Muscle Into Vector Number Crunching was written by Timothy Prickett Morgan at The Next Platform.
Back in 2010, when the term “cloud computing” was still laden with peril and mystery for many users in enterprise and high performance computing, HPC cloud startup, Nimbix, stepped out to tackle that perceived risk for some of the most challenging, latency-sensitive applications.
At the time, there were only a handful of small companies catering to the needs of high performance computing applications and those that existed were developing clever middleware to hook into AWS infrastructure. There were a few companies offering true “HPC as a service” (distinct datacenters designed to fit such workloads that could be accessed via a …
Specialized Supercomputing Cloud Turns Eye to Machine Learning was written by Nicole Hemsoth at The Next Platform.
If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. Quite the opposite, and increasingly so.
As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today.
Software will run most efficiently on hardware that is tuned for …
Why Intel Is Tweaking Xeon Phi For Deep Learning was written by Timothy Prickett Morgan at The Next Platform.
In this day and age when the X86 server has pretty much taken over compute in the datacenter, enterprise customers still have their preferences and prejudices when it comes to the make and model of X86 machine that they deploy to run their applications. So a company that is trying to get its software into the datacenter, as server-storage hybrid Nutanix is, needs to befriend the big incumbent server makers and get its software onto their boxes.
This is not always an easy task, given that some of these companies have their own hyperconverged storage products or they have a …
Growing Hyperconverged Platforms Takes Patience, Time, And Money was written by Timothy Prickett Morgan at The Next Platform.
Seven years ago, it was the end for SGI. The legendary company had gone bankrupt, its remains were up for liquidation, and its relatively few remaining loyal customers were left in limbo.
This week, SGI reached a new ending, significantly different from its last one, as HPE announced an intended deal to purchase the company for approximately $275 million.
SGI was reincarnated in 2009 when Rackable bought its assets, including its brand, off the scrap heap, for only $42.5 million (originally reported as $25 million at the time, but later updated). Rackable—that is to say, the new SGI—protected employees, key …
Seven Years Later, SGI Finds a New Ending was written by Nicole Hemsoth at The Next Platform.
Computing has gone through a few waves. There was human to human computing in the first few decades, and in recent years it has been dominated by human to machine computing with hyperscale consumer-facing applications, and we are on the cusp of a third wave of machine to machine computing that will swell compute, storage, and networking to untold zettabytes of traffic.
Under such data strain, there is an explosive need for bandwidth across datacenters as a whole, but particularly among hyperscalers with their hundreds of millions to billions of users. (Ironically, some datacenters are only now moving to 10 …
Intel Leverages Chip Might To Etch Photonics Future was written by Timothy Prickett Morgan at The Next Platform.
Many startups have come and gone since the early days of cloud, but when it comes to those that started small and grown organically with the expansion of use cases, Cycle Computing still stands tall.
Tall being relative, of course. As with that initial slew of cloud startups, a lot of investment money has sloshed around as well. As Cycle Computing CEO, Jason Stowe, reminds The Next Platform, the small team started with an $8,000 credit card bill with sights on the burgeoning needs of scientific computing users in need of spare compute capacity and didn’t take funding until …
The Cloud Startup that Just Keeps Kicking was written by Nicole Hemsoth at The Next Platform.
Data scientists and deep and machine learning researchers rely on frameworks and libraries such as Torch, Caffe, TensorFlow, and Theano. Studies by Colfax Research and Kyoto University have found that existing open source packages such as Torch and Theano deliver significantly faster performance through the use of Intel Scalable System Framework (Intel SSF) technologies like the Intel compiler and performance libraries for Intel Math Kernel Library (Intel MKL), Intel MPI (Message Passing Interface), and Intel Threading Building Blocks (Intel TBB), and Intel Distribution for Python (Intel Python).
Andrey Vladimirov (Head of HPC Research, Colfax Research) noted …
Intel SSF Optimizations Boost Machine Learning was written by Nicole Hemsoth at The Next Platform.
The polyphonic weavings of a fugue in baroque music is a beautiful thing and an apt metaphor for how we want orchestration on cloud infrastructure to behave in a harmonic fashion. Unfortunately, most cloudy infrastructure is in more of a fugue state, complete with multiple personalities and amnesia.
A startup founded by some architects and engineers from Amazon Web Services wants to get the metaphor, and therefore the tools, right and have just popped out of stealth mode with a company aptly called Fugue to do just that.
Programmers are in charge of some of the largest and most profitable …
Getting Cloud Out Of A Fugue State was written by Timothy Prickett Morgan at The Next Platform.
It is a coincidence, but one laden with meaning, that Nvidia is setting new highs selling graphics processors at the same time that SGI, one of the early innovators in the fields of graphics and supercomputing, is being acquired by Hewlett Packard Enterprise.
Nvidia worked up from GPUs for gaming PCs to supercomputers, and has spread its technology to deep learning, visualization, and virtual desktops, all with much higher margins than GPUs for PCs or any other client device could deliver. SGI, in its various incarnations, stayed at the upper echelons of computing where there is, to a certain …
Deep Learning Drives Nvidia’s Tesla Business To New Highs was written by Timothy Prickett Morgan at The Next Platform.
Rajeeb Hazra, VP of Intel’s Datacenter Group, is a car buff. Why is that important to HPC? Because autonomous cars are the future, and it will take a phenomenal amount of compute to support them.
Hazra recently shared that some estimates to accurately support 20,000 autonomous cars would require an exaflop of sustained compute. This level of supercomputing is needed, considering the network of millions of sensors inside and outside the cars and their interpretation, plus the deep learning needed to constantly stay aware of the world around them and the drivers inside them, and repeatedly pass new models to …
Intel’s VP of Datacenter Group on “AI—and More—on IA” was written by Nicole Hemsoth at The Next Platform.
Supercomputer maker SGI has been going it alone in the upper echelons of the computing arena for decades and has brought much innovation to bear on some of the most intractable simulation, modeling, and analytics problems in the world. But the one thing it could never do was get enough feet on the street to sell its gear.
Now that Hewlett Packard Enterprise has acquired SGI, that will no longer be a problem, but the downside, as far as the variety in the IT ecosystem is concerned, is that yet another independent company will be subsumed into a much larger …
HPE Expands HPC Reach With SGI Buy was written by Timothy Prickett Morgan at The Next Platform.
Not long ago, we took a look back at the last decade of Amazon Web Services and its growth, particularly in terms of its reach into high performance computing and large-scale enterprise workloads. While the startup story is easier to tell for AWS in terms of the capex/opex advantage to compete with far larger companies, the enterprise use case growth of AWS is still a stunning story over time.
This morning during his AWS Summit New York keynote, AWS Chief Technology Officer, Werner Vogels shared growth highlights of the company over the last ten years, noting that the message is …
AWS CTO on How Startups Define Large-Scale Competitiveness was written by Nicole Hemsoth at The Next Platform.
It is going to take a lot of different things to build an exascale system. One of them is money, and the other is a lot of good – and perhaps unconventional – ideas. It may also take more cooperation between the hyperscale and HPC communities, who both stand to benefit from the innovation.
As a professor of computer architectures at the University of Manchester, the director of technology and systems at chip designer ARM, and the founder of a company called Kaleao to create microservers that implement many of his architectural ideas, John Goodacre has some strong opinions about …
Melding Hyperscale And HPC To Reach Exascale was written by Timothy Prickett Morgan at The Next Platform.
Following yesterday’s acquisition of deep learning chip startup Nervana Systems by Intel, we talked with the company’s CEO, Naveen Rao, about what plans are for both the forthcoming hardware and internally developed Neon software stack now that the technology is under a much broader umbrella.
Media outlets yesterday reported the acquisition was $350 million, but Rao tells The Next Platform it was not reported correctly and is actually more than that. He was not allowed to state the actual amount but said it was quite a bit higher than the figure given yesterday.
Nervana had been seeking a way to …
Nervana CEO on Intel Acquisition, Future Technology Outlook was written by Nicole Hemsoth at The Next Platform.