In high performance computing, machine learning, and a growing set of other application areas, accelerated, heterogeneous systems are becoming the norm.
With that state come several parallel programming approaches; from OpenMP, OpenACC, OpenCL, CUDA, and others. The trick is choosing the right framework for maximum performance and efficiency—but also productivity.
There have been several studies comparing relative performance between the various frameworks over the last several years, but many take two head to head for compares on a single benchmark or application. A team from Linneaus University in Sweden took these comparisons a step further by developing a custom tool …
Parallel Programming Approaches for Accelerated Systems Compared was written by Nicole Hemsoth at The Next Platform.
International Business Machines has gone through so many changes in its eleven decades of existence, and it is important to remember that some days. If IBM’s recent changes are a bit bewildering, as they were in the late 1980s, the middle 1990s, and the early 2010s in particular, they are perhaps nothing compared the changes that were wrought to transform a maker of meat slicers, time clocks, and tabulating equipment derived from looms.
Yeah, and you thought turning GPUs into compute engines was a stretch.
Herman Hollerith, who graduated from Columbia University in 1879 when its engineering school was still …
International Cognitive And Cloud Business Machines was written by Timothy Prickett Morgan at The Next Platform.
Chip maker Intel is getting out of the business of trying to make money with a commercially supported release of the high-end Lustre parallel file system. Lustre is commonly used at HPC centers and is increasingly deployed by enterprises to take on their biggest file system jobs.
But don’t jump too far to any other conclusions. The core development and support team, minus a few key people who have already left, remains at Intel and will be working on Lustre for the foreseeable future.
Intel quietly announced its plans to shutter its Lustre commercialization efforts in a posting earlier this …
Intel Shuts Down Lustre File System Business was written by Timothy Prickett Morgan at The Next Platform.
It all started with a new twist on an old idea, that of a lightweight software container running inside Linux that would house applications and make them portable. And now Docker is coming full circle and completing its eponymous platform by opening up the tools to allow users to create their own minimalist Linux operating system that is containerized and modular above the kernel and that only gives applications precisely what they need to run.
The new LinuxKit is not so much a variant of Linux as a means of creating them. The toolkit for making Linuxes, which was unveiled …
Docker Completes Its Platform With DIY Linux was written by Timothy Prickett Morgan at The Next Platform.
Scaling the performance of machine learning frameworks so they can train larger neural networks – or so the same training a lot faster – has meant that the hyperscalers of the world who are essentially creating this technology have had to rely on increasingly beefy compute nodes, these days almost universally augmented with GPUs.
There is a healthy rivalry between the hyperscalers over who has the best machine learning framework and the co-designed iron to take the best advantage of its capabilities. At its F8 developer conference, Facebook not only rolled out a significantly tweaked variant of the open source …
Machine Learning Gets An InfiniBand Boost With Caffe2 was written by Timothy Prickett Morgan at The Next Platform.
So you are a system architect, and you want to make the databases behind your applications run a lot faster. There are a lot of different ways to accomplish this, and now, there is yet another — and perhaps more disruptive — one.
You can move the database storage from disk drives to flash memory, You can move from a row-based database to a columnar data store that segments data and speeds up accesses to it. And for even more of a performance boost, you can pull that columnar data into main memory to be read and manipulated at memory …
FPGAs To Shake Up Stodgy Relational Databases was written by Timothy Prickett Morgan at The Next Platform.
The fields where machine learning and neural networks can have positive impacts seem almost limitless. From healthcare and genomics to pharmaceutical development, oil and gas exploration, retail, smart cities and autonomous vehicles, the ability to rapidly and automatically find patterns in massive amounts of data promises to help solve increasingly complex problems and speed up discoveries that will improve lives, create a heathier world and make businesses more efficient.
Climate science is one of those fields that will see significant benefits from machine learning, and scientists in the field are pushing hard to see how the technology can help them …
Machine Learning Storms Into Climate Research was written by Jeffrey Burt at The Next Platform.
There is no question that plenty of companies are shifting their storage infrastructure from giant NAS and SAN appliances to more generic file, block, and object storage running on plain vanilla X86 servers equipped with flash and disk. And similarly, companies are looking to the widespread availability of dual-ported NVM-Express drives on servers to give them screaming flash performance on those storage servers.
But the fact remains that very few companies want to build and support their own storage servers, and moreover, there is still room for an appliance approach to these commodity components for enterprises that want to buy …
Hyperscaling With Consumer Flash And NVM-Express was written by Timothy Prickett Morgan at The Next Platform.
There is increasing interplay between the worlds of machine learning and high performance computing (HPC). This began with a shared hardware and software story since many supercomputing tricks of the trade play well into deep learning, but as we look to next generation machines, the bond keeps tightening.
Many supercomputing sites are figuring out how to work deep learning into their existing workflows, either as a pre- or post-processing step, while some research areas might do away with traditional supercomputing simulations altogether eventually. While these massive machines were designed with simulations in mind, the strongest supers have architectures that parallel …
China Pushes Breadth-First Search Across Ten Million Cores was written by Nicole Hemsoth at The Next Platform.
When Red Hat began building out its OpenShift cloud application platform more than five years ago, the open source software vendor found itself in a similar situation as others in the growing platform-as-a-service (PaaS) space: they were all using technologies developed in-house because there were no real standards in the industry that could be used to guide them.
That changed about three years ago, when Google officials decided to open source the technology – called Borg – they were using internally to manage the search giant’s clusters and make it available to the wider community. Thus was born Kubernetes, …
Red Hat Tunes Up OpenShift For Legacy Code In Kubernetes was written by Jeffrey Burt at The Next Platform.
Intel might have its own thoughts about the trajectory of Moore’s Law, but many leaders in the industry have views that variate slightly from the tick-tock we keep hearing about.
Sophie Wilson, designer of the original Acorn Micro-Computer in the 1970s and later developer of the instruction set for ARM’s low-power processors that have come to dominate the mobile device world has such thoughts. And when Wilson talks about processors and the processor industry, people listen.
Wilson’s message is essentially that Moore’s Law, which has been the driving force behind chip development in particular and the computer industry …
ARM Pioneer Sophie Wilson Also Thinks Moore’s Law Coming to an End was written by Jeffrey Burt at The Next Platform.
Just two years ago, supercomputing was thrust into a larger spotlight because of the surge of interest in deep learning. As we talked about here, the hardware similarities, particularly for training on GPU-accelerated machines and key HPC development approaches, including MPI to scale across a massive number of nodes, brought new attention to the world of scientific and technical computing.
What wasn’t clear then was how traditional supercomputing could benefit from all the framework developments in deep learning. After all, they had many of the same hardware environments and problems that could benefit from prediction, but what they lacked …
Supercomputing Gets Neural Network Boost in Quantum Chemistry was written by Nicole Hemsoth at The Next Platform.
Google created quite a stir when it released architectural details and performance metrics for its homegrown Tensor Processing Unit (TPU) accelerator for machine learning algorithms last week. But as we (and many of you reading) pointed out, comparing the TPU to earlier “Kepler” generation GPUs from Nvidia was not exactly a fair comparison. Nvidia has done much in the “Maxwell” and “Pascal” GPU generations specifically to boost machine learning performance.
To set the record straight, Nvidia took some time and ran some benchmarks of its own to put the performance of its latest Pascal accelerators, particularly the ones it aims …
Does Google’s TPU Investment Make Sense Going Forward? was written by Timothy Prickett Morgan at The Next Platform.
There has been much discussion about the “black box” problem of neural networks. Sophisticated models can perform well on predictive workloads, but when it comes to backtracking how the system came to its end result, there is no clear way to understand what went right or wrong—or how the model turned on itself to arrive a conclusion.
For old-school machine learning models, this was not quite the problem it is now with non-linear, hidden data structures and countless parameters. For researchers deploying neural networks for scientific applications, this lack of reproducibility from the black box presents validation hurdles, but for …
A Look at Facebook’s Interactive Neural Network Visualization System was written by Nicole Hemsoth at The Next Platform.
If you can’t beat the largest cloud players at economies of scale, the only option is to try to outrun them in performance, capabilities, or price.
While go head to head with Amazon, Google, Microsoft, or IBM on cloud infrastructure prices is a challenge, one way to gain an edge is by being the first to deliver bleeding-edge hardware to those users with emerging, high-value workloads. The trick is to be at the front of the wave, often with some of the most expensive iron, which is risky with AWS and others nipping at heels and quick to follow. It …
Risk or Reward: First Nvidia DGX-1 Boxes Hit the Cloud was written by Nicole Hemsoth at The Next Platform.
Containers are an extremely mobile, safe and reproducible computing infrastructure that is now ready for production HPC computing. In particular, the freely available Singularity container framework has been designed specifically for HPC computing. The barrier to entry is low and the software is free.
At the recent Intel HPC Developer Conference, Gregory Kurtzer (Singularity project lead and LBNL staff member) and Krishna Muriki (Computer Systems Engineer at LBNL) provided a beginning and advanced tutorial on Singularity. One of Kurtzer’s key takeaways: “setting up workflows in under a day is commonplace with Singularity”.
Many people have heard about code modernization and …
Singularity Containers for HPC, Reproducibility, and Mobility was written by Nicole Hemsoth at The Next Platform.
Here at The Next Platform, we tend to focus on deep learning as it relates to hardware and systems versus algorithmic innovation, but at times, it is useful to look at the co-evolution of both code and machines over time to see what might be around the next corner.
One segment of the deep learning applications area that has generated a great deal of work is in speech recognition and translation—something we’ve described in detail via efforts from Baidu, Google, Tencent, among others. While the application itself is interesting, what is most notable is how codes …
From Mainframes to Deep Learning Clusters: IBM’s Speech Journey was written by Nicole Hemsoth at The Next Platform.
If you want an object lesson in the interplay between Moore’s Law, Dennard scaling, and the desire to make money from selling chips, you need look no further than the past several years of Intel’s Xeon E3 server chip product lines.
The Xeon E3 chips are illustrative particularly because Intel has kept the core count constant for these processors, which are used in a variety of gear, from workstations (remote and local), entry servers to storage controllers to microservers employed at hyperscalers and even for certain HPC workloads (like Intel’s own massive EDA chip design and validation farms). …
Xeon E3: A Lesson In Moore’s Law And Dennard Scaling was written by Timothy Prickett Morgan at The Next Platform.
While a lot of the applications in the world run on clusters of systems with a relatively modest amount of compute and memory compared to NUMA shared memory systems, big iron persists and large enterprises want to buy it. That is why IBM, Fujitsu, Oracle, Hewlett Packard Enterprise, Inspur, NEC, Unisys, and a few others are still in the big iron racket.
Fujitsu and its reseller partner – server maker, database giant, and application powerhouse Oracle – have made a big splash at the high end of the systems space with a very high performance processor, the Sparc64-XII, and a …
Fujitsu Takes On IBM Power9 With Sparc64-XII was written by Timothy Prickett Morgan at The Next Platform.
Four years ago, Google started to see the real potential for deploying neural networks to support a large number of new services. During that time it was also clear that, given the existing hardware, if people did voice searches for three minutes per day or dictated to their phone for short periods, Google would have to double the number of datacenters just to run machine learning models.
The need for a new architectural approach was clear, Google distinguished hardware engineer, Norman Jouppi, tells The Next Platform, but it required some radical thinking. As it turns out, that’s exactly …
First In-Depth Look at Google’s TPU Architecture was written by Nicole Hemsoth at The Next Platform.