The science fiction of a generation ago predicted a future in which humans were replaced by the reasoning might of a supercomputer. But in an unexpected twist of events, it appears the it is the supercomputer’s main output—scientific simulations—that could be replaced by an even higher order of intelligence.
While we will always need supercomputing hardware, the vast field of scientific computing, or high performance computing, could also be in the crosshairs for disruptive change, altering the future prospects for scientific code developers, but opening new doors in more energy-efficient, finer-grained scientific discovery. With code that can write itself based …
When Will AI Replace Traditional Supercomputing Simulations? was written by Nicole Hemsoth at The Next Platform.
GPU computing has deep roots in supercomputing, but Nvidia is using that springboard to dive head first into the future of deep learning.
This changes the outward-facing focus of the company’s Tesla business from high-end supers to machine learning systems with the expectation that those two formerly distinct areas will find new ways to merge together given the similarity in machine, scalability, and performance requirements. This is not to say that Nvidia is failing the HPC set, but there is a shift in attention from what GPUs can do for Top 500 class machines to what graphics processors can do …
The Year Ahead for GPU Accelerated Supercomputing was written by Nicole Hemsoth at The Next Platform.
Big data, data science, machine learning, and now deep learning are all the rage and have tons of hype, for better—and in some ways, for worse. Advancements in AI such as language understanding, self-driving cars, automated claims, legal text processing, and even automated medical diagnostics are already here or will be here soon.
In Asia, several countries have made significant advancements and investments into AI, leveraging their historical work in HPC.
China now owns the top three positions in the Top500 with Sunway TaihuLight, Tianhe-2, and Tianhe, and while Tianhe-2 and Tianhe were designed for HPC style workloads, TaihuLight is …
HPC to Deep Learning from an Asian Perspective was written by Nicole Hemsoth at The Next Platform.
Graphics chip maker Nvidia has taken more than a year and carefully and methodically transformed its GPUs into the compute engines for modern HPC, machine learning, and database workloads. To do so has meant staying on the cutting edge of many technologies, and with the much-anticipated but not very long-awaited “Volta” GP100 GPUs, the company is once again skating on the bleeding edge of several different technologies.
This aggressive strategy allows Nvidia to push the performance envelope on GPUs and therefore maintain its lead over CPUs for the parallel workloads it is targeting while at the same time setting up …
Nvidia’s Tesla Volta GPU Is The Beast Of The Datacenter was written by Timothy Prickett Morgan at The Next Platform.
Moving data is the biggest problem in computing, and probably has been since there was data processing if we really want to be honest about it. Because of the cost of bandwidth, latency, energy, and iron to do multiple stages of processing on information in a modern application that might include a database as well as machine learning algorithms against stuff stored there as well as from other sources, you want to try to do all your computation from the memory of one set of devices.
That, in a nutshell, is what the GPU Open Analytics Initiative is laying the …
GOAI: Keeping Databases, Analytics, And Machine Learning All On The GPU was written by Timothy Prickett Morgan at The Next Platform.
When Dell acquired EMC in its massive $60 billon-plus deal last year, it boasted that Dell was inheriting a boatload of new technologies that would help propel forward its capabilities and ambitions with larger enterprises.
That included offerings ranging from VMware’s NSX software-defined networking (SDN) platform to VirtuStream and its cloud technologies for running mission critical applications from the likes of Oracle, SAP and Microsoft off-premises. In particular, Dell was acquiring EMC’s broad and highly popular storage portfolio, in particular the high-end VMAX, XtremeIO, and newer ScaleIO lineups as well as its Isilon storage arrays for high performance workloads.
Dell …
Dell EMC Upgrades Flash in High-End Storage While Eyeing NVMe was written by Jeffrey Burt at The Next Platform.
There may be a shortage in the supply of DRAM main memory and NAND flash memory that is having an adverse effect on the server and storage markets, but there is no shortage of vendors who are trying to push the envelope on clustered storage using a mix of these memories and others such as the impending 3D XPoint.
Micron Technology, which makes and sells all three of these types of memories, is so impatient with the rate of technological advancement in clustered flash arrays based on the NVM-Express protocol that it decided to engineer and launch its own product …
Impatient For Fabrics, Micron Forges Its Own NVM-Express Arrays was written by Timothy Prickett Morgan at The Next Platform.
The last two years have delivered a new wave of deep learning architectures designed specifically for tackling both training and inference sides of neural networks. We have covered many of them extensively, but only a few have seen major investment or acquisition—the most notable of which was Nervana Systems over a year ago.
Among the string of neural network chip startups, Graphcore stood out with its manycore approach to handling both training and inference on the same manycore chip. We described the hardware architecture in detail back in March and while its over $30 million in funding from a …
A Dive into Deep Learning Chip Startup Graphcore’s Software Stack was written by Nicole Hemsoth at The Next Platform.
While it is always best to have the right tool for the job, it is better still if a tool can be used by multiple jobs and therefore have its utilization be higher than it might otherwise be. This is one of the reasons why general purpose, X86-based computing took over the datacenter. Economies of scale trumped the efficiency that can come from limited scope or just leaving legacy applications alone in place on alternate platforms.
The idea of offloading computational tasks from CPUs to GPU accelerators took off in academia a little more than a decade ago, and …
Crunching Machine Learning And Databases Together On GPUs was written by Timothy Prickett Morgan at The Next Platform.
Industrial companies have replaced people with machines, systems analysts with simulations, and now the simulations themselves could be outpaced by machine learning—albeit with a human in the loop, at the beginning at least.
The new holy grail of machine learning and deep learning, as with almost any other emerging technology set, is to mask enough of the complexity to make it broadly applicable without lose the performance and other features that can be retained by taking a low-level approach. If this kind of deep generalization can happen, a new mode of considering how data is used in research and enterprise …
Generalizing a Hardware, Software Platform for Industrial AI was written by Nicole Hemsoth at The Next Platform.
Enterprise spending on servers was a bit soft in the first quarter, as evidenced by the financial results posted by Intel and by its sometime rival IBM, but the hyperscale and HPC markets, at least when it comes to networking, was a bit soft, according to high-end network chip and equipment maker Mellanox Technologies.
In the first quarter ended March 31, Mellanox had a 4.1 percent revenue decline, to $188.7 million, and because of higher research and development costs, presumably associated with the rollout of 200 Gb/sec Quantum InfiniBand technology (which the company has talked about) and …
HPC System Delays Stall InfiniBand was written by Timothy Prickett Morgan at The Next Platform.
Energy efficiency and operating costs for systems are as important as raw performance in today’s datacenters. Everyone from the largest hyperscalers and high performance computing centers to large enterprises that are sometimes like them are trying squeeze as much performance as they can from their infrastructure while reining in power consumption and the costs associated with keeping it all from overheating.
Throw in the slowing down of Moore’s Law and new emerging workloads like data analytics and machine learning, and the challenge to these organizations becomes apparent.
In response, organizations on the cutting edge have embraced accelerators like GPUs and …
Rambus, Microsoft Put DRAM Into Deep Freeze To Boost Performance was written by Timothy Prickett Morgan at The Next Platform.
If any new hardware technology is going to get traction in the datacenter, it has to have the software behind it. And as the dominant supplier of commercial Linux, Red Hat’s support of ARM-based servers gives the upstart chip makers like Applied Micro, Cavium, and Qualcomm the leverage to help pry the glasshouse doors open and get a slice of the server and storage business that is so utterly dominated by Intel’s Xeon processors today.
It is now or never for ARM in the datacenter, and that means Red Hat has to go all the way and not just support …
Red Hat Is The Gatekeeper For ARM In The Datacenter was written by Jeffrey Burt at The Next Platform.
We have been saying for the past two year that the impending “Skylake” Xeon processors represented the biggest platform architectural change in the Xeon processor business at Intel since the transformational “Nehalem” Xeon 5500s that debuted back in March 2009 into the gaping maw of the Great Recession.
There is no global recession breathing down the IT sector’s neck like a hungry wolf here in 2017, eight years and seven chip generations later. But Intel is facing competitive pressures from AMD’s Naples Opterons, IBM’s Power9, and the ARM collective (mainly Cavium and Qualcomm at this point, but Applied Micro is …
Intel Melds Xeon E5 And E7 With Skylake was written by Timothy Prickett Morgan at The Next Platform.
When it comes to large media in the U.S. with a broad reach into television and digital, the Scripps Networks Interactive brand might not come to mind first, but many of the channels and sources are household names, including HGTV, Food Network, and The Travel Channel, among others.
Delivering television and web-based content and services is a data and computationally intensive task, which just over five years ago was handled by on-premises machines in the company’s two local datacenters. In order to keep up with peaks in demand during popular events or programs, Scripps Interactive had to overprovision with those …
An Inside Look at One Major Media Outlet’s Cloud Transition was written by Nicole Hemsoth at The Next Platform.
This fall will mark twenty years since the publication of the v1.0 specification of OpenMP Fortran. From early loop parallelism to a heterogeneous, exascale future, OpenMP has apparently weathered well the vicissitudes and tumultuous changes of the computer industry over that past two decades and appears to be positioned to address the needs of our exascale future.
In the 1990s when the OpenMP specification was first created, memory was faster than the processors that performed the computation. This is the exact opposite of today’s systems where memory is the key bottleneck and the HPC community is rapidly adopting faster memory …
OpenMP: From Parallel Loops To Exaflops was written by Timothy Prickett Morgan at The Next Platform.
During the five years that Red Hat has been building out its OpenShift cloud applications platform, much of the focus has been on making it easier to use by customers looking to adapt to an increasingly cloud-centric world for both new and legacy applications. Just as it did with the Linux operating system through Red Hat Enterprise Linux and related middleware and tools, the vendor has worked to make it easier for enterprises to embrace OpenShift.
That has included a major reworking of the platform with the release of version 3.0 last year, which ditched Red Hat’s in-house technologies for …
Red Hat Gears Up OpenShift For Developers was written by Jeffrey Burt at The Next Platform.
Distributed applications, whether they are containerized or not, have a lot of benefits when it comes to modularity and scale. But in a world of feature creep on all applications, whether they are internally facing ones running a business or hyperscale consumer applications like Google’s search engine or Facebook’s social media network, these distributed applications put a huge strain on the network.
This, more than any other factor, is why network costs are rising faster than any other aspect of the datacenter. Gone are the days when everything was done in three or four tiers, with a Web server like …
Lessons Learned From Facebook’s Split Network Backbone was written by Timothy Prickett Morgan at The Next Platform.
If the public cloud computing market were our solar system, then Amazon Web Services would be Jupiter and Saturn together and the remaining five fast-growing big clouds would be like the inner planets like Mercury, Venus, Earth, Mars, and that pile of rocks that used to be a planet mixed up with those clouds that are finding growth a bit more challenging – think Uranus and Neptune and maybe even Pluto if you still want to count it a planet.
This analogy came to us in the wake of Amazon’s reporting of its financial results for the first quarter of …
The Datacenter Does Not Revolve Around AWS, Despite Its Gravity was written by Timothy Prickett Morgan at The Next Platform.
Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by chewing on large-scale deep learning workloads—and this requires a specialized approach to get high utilization, efficiency, and performance.
Nearly all of the cluster management tools from the high performance computing community are being bent in the machine learning direction, but for production deep learning shops, there appears to be a DIY tendency. This is not as complicated as it might sound, given the range of container-based open source tools, …
Cluster Management for Distributed Machine Learning at Scale was written by Nicole Hemsoth at The Next Platform.