Networking chip maker Cavium is one of the ARM server chip upstarts that is taking on Intel’s hegemony in the datacenter, and is probably getting the most traction among its ARM peers in the past year with its ThunderX multicore processors.
The first-generation ThunderX chips are seeing the most interest from hyperscalers and HPC centers, plus a few cloud builders, telcos, and large enterprises, that want to explore the possibilities of a different server architecture, and they will be even more intrigued by the second-generation ThunderX2 processors, which Cavium unveiled earlier this week at the Computex trade show in Taipei, …
Next-Generation ThunderX2 ARM Targets Skylake Xeons was written by Timothy Prickett Morgan at The Next Platform.
During a trip to Dell in Austin, Texas this week, little did The Next Platform know that the hardware giant and nearby Texas Advanced Computing Center (TACC) had major news to share on the supercomputing front.
It appears the top ten-ranked Stampede supercomputer is set for an extensive, rolling upgrade—one that will keep TACC’s spot in the top tier of supercomputing sites and which will feature the latest Knights Landing processors and over time, a homogeneous Omni-Path fabric. The net effect of the upgrade will be a whopping 18 petaflops of peak performance by 2018.
The new system will begin …
Knights Landing Upgrade Will Push TACC Supercomputer to 18PF was written by Nicole Hemsoth at The Next Platform.
While online auctioneer eBay does not run the largest search engine in the world, search is a very key component of its service, which has over 162 million active buyers and 900 million product listings. Looking to improve upon its current hardware infrastructure underpinning its search engine, eBay has tapped Dell to create a new water-cooled, hyperscale-style rack system that will let it overclock its servers and boost their performance on compute-intensive search algorithms.
The hyperscalers are all a bit cagey about the search engine infrastructure that they use because it is such a critical component of what they do, …
eBay Taps Dell Triton Systems To Overclock Search Engines was written by Timothy Prickett Morgan at The Next Platform.
Back in 2009, when yours truly was assigned the primary beat of covering supercomputing on remote hardware (then dubbed the mysterious “cloud”), the possibility that cloud-based high performance computing was little more than a pipe dream.
At that time, most scientific and technical computing communities had already developed extensive grids to extend their research beyond physical borders, and the idea of introducing new levels of latency, software, and management interfaces did not appear to be anything most HPC centers were looking forward to—even with the promise of cost-savings (as easy “bursting” was still some time off).
Just as Amazon Web …
AWS Brings Supercomputing Set Further into Fold was written by Nicole Hemsoth at The Next Platform.
Computing for neuroscience, which has aided in our understanding of the structure and function of the brain, has been around for decades already. More recently, however, there has been neuroscience for computing, or the use of computational principles of the brain for generic data processing. For each of these neuroscience-driven areas there is a key limitation—scalability.
This is not just scalability in terms of software or hardware systems, but on the application side, limits in terms of efficiently deploying computational tools at sufficient size and time scales to yield far greater insight. While adding more compute to the problem …
Strong Scaling Key to Redrawing Neuroscience Borders was written by Nicole Hemsoth at The Next Platform.
The datacenter is a battleground with many fronts these days, with intense competition between compute, memory, storage, and networking components. In terms of revenues, profits, and prestige, the compute territory is the most valuable that chip makers and their system partners are fighting for, and the ARM and OpenPower collectives are doing their best to take some ground from a very powerful Intel.
As such, chip makers end up comparing themselves to Intel Xeon or Atom processors, and Intel sometimes makes comparisons back. At the high end, Intel is battling the Power8 processor championed by IBM and to a lesser …
Intel Lines Up ThunderX ARM Against Xeons was written by Timothy Prickett Morgan at The Next Platform.
Having made the improbable jump from the game console to the supercomputer, GPUs are now invading the datacenter. This movement is led by Google, Facebook, Amazon, Microsoft, Tesla, Baidu and others who have quietly but rapidly shifted their hardware philosophy over the past twelve months. Each of these companies have significantly upgraded their investment in GPU hardware and in doing so have put legacy CPU infrastructure on notice.
The driver of this change has been deep learning and machine intelligence, but the movement continues to downstream into more and more enterprise-grade applications – led in part by the explosion …
The Age of the GPU is Upon Us was written by Nicole Hemsoth at The Next Platform.
Any aspirations that the Hewlett-Packard that we knew for nearly a decade and a half to build a conglomerate that resembled IBM in its own former enterprise breadth and depth of software, services, and systems is now over with the company spinning out its Enterprise Services business and focusing very tightly on its core hardware and related software businesses.
In conjunction with the posting of its financial results for the first quarter of its fiscal 206, the trimmed down Hewlett Packard Enterprise, which has not included the PC and printer businesses since last year, announced that it was going to …
HPE Hunkers Down On Datacenter Hardware was written by Timothy Prickett Morgan at The Next Platform.
Don’t just call it “the cloud.” Even if you think you know what cloud means, the word is fraught with too many different interpretations for too many people. Nevertheless, the effect of cloud computing, the web, and their assorted massive datacenters has had a profound impact on enterprise computing, creating new application segments and consolidating IT resources into a smaller number of mega-players with tremendous buying power and influence.
Welcome to the hyperscale market.
At the top end of the market, ten companies – behemoths like Google, Amazon, eBay, and Alibaba – each spend over $1 billion per year on …
The Hyperscale Effect: Tracking the Newest High-Growth IT Segment was written by Nicole Hemsoth at The Next Platform.
Having access to fairly reliable 10-day forecasts is a luxury, but it comes with high computational costs for centers in the business of providing predictability. This ability to accurately predict weather patterns, dangerous and seasonal alike, has tremendous economic value and accordingly, significant investment goes into powering ever-more extended and on-target forecast.
What is interesting on the computational front is that the future of weather prediction accuracy, timeliness, efficiency, and scalability seems to be riding a curve not so dissimilar to that of Moore’s Law. Big leaps, followed by steady progress up the trend line, and a moderately predictable sense …
Large-Scale Weather Prediction at the Edge of Moore’s Law was written by Nicole Hemsoth at The Next Platform.
While legacy monolithic applications will linger in virtual machines for an incredibly long time in the datacenter, new scale-out applications run best on new architectures. And that means the underlying hardware will look a lot more like what the hyperscalers have built than traditional siloed enterprise systems.
But most enterprises can’t design their own systems and interconnects, as Google, Facebook, and others have done, and as such, they will rely on others to forge their machines. A group of hot-shot system engineers that were instrumental in creating systems at Sun Microsystems and Cisco Systems in the past two decades have …
Driving Compute And Storage Scale Independently was written by Timothy Prickett Morgan at The Next Platform.
For those in enterprise circles who still conjure black and white images of hulking supercomputers when they hear the name “Cray,” it is worth noting that the long-standing company has done a rather successful job of shifting a critical side of its business to graph analytics and large-scale data processing.
In addition to the data-driven capabilities cooked into its XC line of supercomputers, and now with their DataWarp burst buffer adding to the I/O bottom line on supercomputers including Cori, among others, Cray has managed to take supercomputing to the enterprise big data set by blending high performance hardware with …
Cray Sharpens Approach to Large-Scale Graph Analytics was written by Nicole Hemsoth at The Next Platform.
No one expects that setting up management tools for complex distributed computing frameworks to be an easy thing, but there is always room for improvement–and always a chance to take out unnecessary steps and improve the automated deployment of such tools.
The hassle of setting up such frameworks, such as Hadoop for data analytics, OpenStack for virtualized infrastructure, or Kubernetes or Mesos for software container management is an inhibitor to the adoption of those new technologies. Working with raw open source software and weaving it together into a successful management control plane is not something all enterprises have the skills …
Samsung Experts Put Kubernetes Through The Paces was written by Timothy Prickett Morgan at The Next Platform.
Accelerators and coprocessors are proliferating in the datacenter, and it has been a boon for speeding up certain kinds of workloads and, in many cases, making machine learning or simulation jobs possible at scale for the first time. But ultimately, in a hybrid system, the processors and the accelerators have to share data, and moving it about is a pain in the neck.
Having the memory across these devices operate in a coherent manner – meaning that all devices can address all memory attached to those devices in a single, consistent way – is one of the holy grails of …
Chip Upstarts Get Coherent With Hybrid Compute was written by Timothy Prickett Morgan at The Next Platform.
Training a machine learning algorithm to accurately solve complex problems requires large amounts of data. The previous article discussed how scalable distributed parallel computing using a high-performance communications fabric like Intel Omni-Path Architecture (Intel OPA) is an essential part of what makes the training of deep learning on large complex datasets tractable in both the data center and within the cloud. Preparing large unstructured data sets for machine learning can be as intensive a task as the training process – especially for the file-system and storage subsystem(s). Starting (and restarting) big data training jobs using tens of thousands of clients …
Lustre to DAOS: Machine Learning on Intel’s Platform was written by Nicole Hemsoth at The Next Platform.
At the tail end of Google’s keynote speech at its developer conference Wednesday, Sundar Pichai, Google’s CEO mentioned that Google had built its own chip for machine learning jobs that it calls a Tensor Processing Unit, or TPU.
The boast was that the TPU offered “an order of magnitude” improvement in the performance per watt for machine learning. Any company building a custom chip for a dedicated workload is worth noting, because building a new processor is a multimillion-dollar effort when you consider hiring a design team, the cost of getting a chip to production and building the hardware and …
Google Takes Unconventional Route with Homegrown Machine Learning Chips was written by Nicole Hemsoth at The Next Platform.
As we have noted over the last year in particular, GPUs are set for another tsunami of use cases for server workloads in high performance computing and most recently, machine learning.
As GPU maker Nvidia’s CEO stressed at this year’s GPU Technology Conference, deep learning is a target market, fed in part by a new range of their GPUs for training and executing deep neural networks, including the Tesla M40, M4, the existing supercomputing-focused K80, and now, the P100 (Nvidia’s latest Pascal processor, which is at the heart of a new appliance specifically designed for deep learning workloads).
While …
IBM Extends GPU Cloud Capabilities, Targets Machine Learning was written by Nicole Hemsoth at The Next Platform.
Over the last year, stories pointing to a bright future for deep neural networks and deep learning in general have proliferated. However, most of what we have seen has been centered on the use of deep learning to power consumer services. Speech and image recognition, video analysis, and other features have spun from deep learning developments, but from the mainstream view, it would seem that scientific computing use cases are still limited.
Deep neural networks present an entirely different way of thinking about a problem set and the data that feeds it. While there are established approaches for images and …
Climate Research Pulls Deep Learning Onto Traditional Supercomputers was written by Nicole Hemsoth at The Next Platform.
Hyperscalers and the academics that often do work with them have invented a slew of distributed computing methods and frameworks to get around the problem of scaling up shared memory systems based on symmetric multiprocessing (SMP) or non-uniform memory access (NUMA) techniques that have been in the systems market for decades. SMP and NUMA systems are expensive and they do not scale to hundreds or thousands of nodes, much less the tens of thousands of nodes that hyperscalers require to support their data processing needs.
It sure would be convenient if they did. But for those who are not hyperscalers, …
In-Memory Breathes New Life Into NUMA was written by Timothy Prickett Morgan at The Next Platform.
There is no question that the memory hierarchy in systems is being busted wide open and that new persistent memory technology that can be byte addressable like DRAM or block addressable like storage are going to radically change the architecture of machines and the software that runs on them. Picking what memory might go mainstream is another story.
It has been decades since IBM made its own DRAM, but the company still has a keen interest in doing research and development on core processing and storage technologies and in integrating new devices with its Power-based systems.
To that end, IBM …
IBM Throws Weight Behind Phase Change Memory was written by Timothy Prickett Morgan at The Next Platform.