In this episode of The Interview from The Next Platform, we talk with Andrew Jones from independent high performance computing consulting firm, N.A.G. about processor and system acquisition trends in HPC for users at the smaller commercial end of the spectrum up through the large research centers.
In the course of the conversation, we cover how acquisition trends are being affected by machine learning entering the HPC workflow in the coming years, the differences over time between commercial HPC and academic supercomputing, and some of the issues around processor choices for both markets.
Given his experiences talking to end users …
HPC System & Processor Trends for 2018 was written by Nicole Hemsoth at The Next Platform.
The combination of the excitement for new video games, the machine learning software revolution, the buildout of very large supercomputers based on hybrid CPU-GPU architectures, and the mining of cryptocurrencies like Bitcoin and Ethereum have combined into a quadruple whammy that is driving Nvidia to new heights for revenues, profits, and market capitalization. And thus it is no surprise Nvidia is one of the few companies that is bucking the trend in a very tough couple of weeks on Wall Street.
But having demand spiking for both its current “Volta” GPUs, which are currently aimed at HPC and AI compute, …
Just How Large Can Nvidia’s Datacenter Business Grow? was written by Timothy Prickett Morgan at The Next Platform.
Co-design is all the rage these days in systems design, where the hardware and software components of a system – whether it is aimed at compute, storage, or networking – are designed in tandem, not one after the other, and immediately affect how each aspect of a system are ultimate crafted. It is a smart idea that wrings the maximum amount of performance out of a system for very precise workloads.
The era of general purpose computing, which is on the wane, brought an ever-increasing amount of capacity to bear in the datacenter at an ever -lower cost, enabling an …
Different Server Workhorses For Different Workload Courses was written by Timothy Prickett Morgan at The Next Platform.
Cloud datacenters in many ways are like melting pots of technologies. The massive facilities hold a broad array of servers, storage systems, and networking hardware that come in a variety of sizes. Their components come with different speeds, capacities, bandwidths, power consumption, and pricing, and they are powered by different processor architectures, optimized for disparate applications, and carry the logos of a broad array of hardware vendors, from the largest OEMs to the smaller ODMs. Some hardware systems are homegrown or built atop open designs.
As such, they are good places to compare and contrast how the components of these …
A Statistical View Of Cloud Storage was written by Jeffrey Burt at The Next Platform.
Compute is being embedded in everything, and there is another wave of distributed computing pushing out from the datacenter into all kinds of network, storage, and other kinds of devices that collect and process data in their own right as well as passing it back up to the glass house for final processing and permanent storage.
The computing requirements at the edge are different from the core compute in the datacenter, and it is very convenient indeed that they align nicely with some of the more modest processing needs of network devices, storage clusters, and more modest jobs in the …
Intel Sharpens The Edge With Skylake Xeon D was written by Timothy Prickett Morgan at The Next Platform.
While it is possible to reap at least some benefits from persistent memory, for those that are performance focused, the work to establish an edge is getting underway now with many of the OS and larger ecosystem players working together on new standards for existing codes.
Before we talk about some of the efforts to bring easier programming for persistent memory closer, it is useful to level-set about what it is, isn’t, how it works, and who will benefit in the near term. The most common point of confusion is that persistent memory is not necessarily about hardware, a fact …
Momentum Gathers for Persistent Memory Preppers was written by Nicole Hemsoth at The Next Platform.
In a way, the processor market started moving in slow motion through 2017 as server makers and their customers were awaiting a veritable cornucopia of processor options, something the industry has not seen in many a year. We have been predicting that there would be a Cambrian Explosion of compute, first in 2017, but it has taken a bit longer for many of these processors to come to market and it looks like 2018 might be the year.
This might be, in fact, the year when IBM’s Power RISC processors see a long-awaited resurgence, and frankly, if it doesn’t happen …
IBM’s 2018 Rollout Plan For Power9 Systems was written by Timothy Prickett Morgan at The Next Platform.
From DRAM to NUMA to memory non-volatile, stacked, remote, or even phase change, the coming years will bring big changes to code developers on the world’s largest parallel supercomputers.
While these memory advancements can translate to major performance leaps, the code complexity these devices will create pose big challenges in terms of performance portability for legacy and newer codes alike.
While the programming side of the emerging memory story may not be as widely appealing as the hardware, work that people like Ron Brightwell, R&D manager at Sandia National Lab and head of numerous exascale programming efforts do to expose …
New Memory Challenges Legacy Approaches to HPC Code was written by Nicole Hemsoth at The Next Platform.
The Carlyle Group, the publicly traded investment firm that has invested in nearly 300 companies that have a net worth of $170 billion and which itself could make around $4 billion in management fees and income from those investments for 2017, does not invest in any technology lightly.
So the fact that it has acquired the X Gene server processor assets that were created over many years by Applied Micro and briefly owned last year by Chinese IT supplier MACOM means that Carlyle believes Arm servers have a shot in the datacenter and that its investors want to get a …
Private Equity Amps Up Arm Servers With Applied X86 Techies was written by Timothy Prickett Morgan at The Next Platform.
DARPA has always been about driving the development of emerging technologies for the benefit of both the military and the commercial world at large.
The Defense Advanced Research Projects Agency has been a driving force behind U.S. efforts around exascale computing and in recent years has targeted everything from robotics and cybersecurity to big data to technologies for implantable technologies. The agency has doled out millions of dollars to vendors like Nvidia and Rex Computing as well as national laboratories and universities to explore new CPU and GPU technologies for upcoming exascale-capable systems that hold the promise of 1,000 …
DARPA’s $200 Million JUMP Into Future Microelectronics was written by Jeffrey Burt at The Next Platform.
It will not happen for a long time, if ever, but we surely do wish that Amazon Web Services, the public cloud division of the online retailing giant, was a separate company. Because if AWS was a separate company, and it was a public company at that, it would have finer grained financial results that might give us some insight into exactly what more than 1 million customers are actually renting on the AWS cloud.
As it is, all that the Amazon parent tells Wall Street about its AWS offspring is the revenue stream and operating profit levels for each …
Navigating The Revenue Streams And Profit Pools Of AWS was written by Timothy Prickett Morgan at The Next Platform.
There has been much recent talk about the near future of code writing itself with the help of trained neural networks but outside of some limited use cases, that reality is still quite some time away—at least for ordinary development efforts.
Although auto-code generation is not a new concept, it has been getting fresh attention due to better capabilities and ease of use in neural network frameworks. But just as in other areas where AI is touted as being the near-term automation savior, the hype does not match the technological complexity need to make it reality. Well, at least not …
AI Will Not Be Taking Away Code Jobs Anytime Soon was written by Nicole Hemsoth at The Next Platform.
If you thought the up-front costs and risks were high for a silicon startup, consider the economics of building a full-stack quantum computing company from the ground-up—and at a time when the applications are described in terms of their potential and the algorithms still in primitive stages.
Quantum computing company, D-Wave managed to bootstrap its annealing-based approach and secure early big name customers with a total of $200 million over the years but as we have seen with a range of use cases, they have been able to put at least some funds back in investor pockets with system sales …
What It Takes to Build a Quantum Computing Startup was written by Nicole Hemsoth at The Next Platform.
Just before the large-scale GPU accelerated Titan supercomputer came online in 2012, the first use cases of the OpenACC parallel programming model showed efficient, high performance interfacing with GPUs on big HPC systems.
At the time, OpenACC and CUDA were the only higher-level tools for the job. However, OpenMP, which has had twenty-plus years to develop roots in HPC, was starting to see the opportunities for GPUs in HPC at about the same time of OpenACC was forming. As legend has it, OpenACC itself was developed based on early GPU work done in an OpenMP accelerator subcommittee, generating some bad …
OpenMP Has More in Store for GPU Supercomputing was written by Nicole Hemsoth at The Next Platform.
There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics.
We have talked about how industries like telecommunications and transportation are looking at recurrent neural networks for helping to better forecast resource demand in supply chains. However, adopting AI and machine learning comes with its share of challenges. Companies whose datacenters are crowded with traditional systems powered by CPUs now have to consider buying and bringing in GPU-based hardware that is better situated to …
The Machine Learning Opportunity in Manufacturing, Logistics was written by Jeffrey Burt at The Next Platform.
It would be hard to find a business that has been more proprietary, insular, and secretive than the networking industry, and for good reasons. The sealed boxes that switch vendors sell, and that are the very backbone of the Internet, have been wickedly profitable – and in a way that neither servers nor storage have been.
There are so many control points in the networking stack that it is no wonder the hyperscalers and cloud builders have been leaning so heavily on switch ASIC vendors to open up their entire stack. The only reason they don’t build their own switch …
Prying The Lid Off Black Box Switch SDKs was written by Timothy Prickett Morgan at The Next Platform.
Researcher Olexandr Isayev wasn’t just impressed to see an AI framework best the top player of a game so complex it was considered impossible for an algorithm to track. He was inspired.
“The analogy of the complexity of chemistry, the number of possible molecule we don’t know about, is roughly the same order of complexity of Go, the University of North Carolina computational biology and chemistry expert explained.
“Instead of playing with checkers on a board, we envisioned a neural network that could play the game of generating molecules—one that did not rely on human intuition for this initial but …
How AlphaGo Sparked a New Approach to De Novo Drug Design was written by Nicole Hemsoth at The Next Platform.
Even though graph analytics has not disappeared, especially in the select areas where this is the only efficient way to handle large-scale pattern matching and analysis, the attention has been largely silenced by the new wave machine learning and deep learning applications.
Before this newest hype cycle displaced its “big data” predecessor, there was a small explosion of new hardware and software approaches to tackling graphs at scale—from system-level offerings from companies like Cray with their Eureka appliance (which is now available as software on its standard server platforms) to unique hardware startups (ThinCI, for example) and graph …
Putting Graph Analytics Back on the Board was written by Nicole Hemsoth at The Next Platform.
It is a renaissance for companies that sell GPU-dense systems and low-power clusters that are right for handling AI inference workloads, especially as they look to the healthcare market–one that for a while was moving toward increasing compute on medical devices.
The growth of production deep learning in medical imaging and diagnostics has spurred investments in hospitals and research centers, pushing high performance systems for medicine back to the forefront.
We have written quite a bit about some of the emerging use cases for deep learning in medicine with an eye on the systems angle in particular, and while these …
Deep Learning is the Next Platform for Pathology was written by Jeffrey Burt at The Next Platform.
The container craze on Linux platforms just took an interesting twist now that Red Hat is sheling out $250 million to acquire its upstart rival in Linux and containers, CoreOS.
As the largest and by far the most profitable open source software company in the world – it had $2.4 billion in sales in fiscal 2017, brought $253.7 million of that to the bottom line, and ended that fiscal year in February with a $2.7 billion subscription and services backlog – Red Hat has not been afraid to spend some money to get its hands on control of key open …
Red Hat Shakes Up Container Ecosystem With CoreOS Deal was written by Timothy Prickett Morgan at The Next Platform.