Ian Buck doesn’t just run the Tesla accelerated computing business at Nvidia, which is one of the company’s fastest-growing and most profitable products in its twenty five year history. The work that Buck and other researchers started at Stanford University in 2000 and then continued at Nvidia helped to transform a graphics card shader into a parallel compute engine that is helping to solve some of the world’s toughest simulation and machine learning problems.
The annual GPU Technology Conference was held by Nvidia last week, and we sat down and had a chat with Buck about a bunch of things …
The Buck Stops – And Starts – Here For GPU Compute was written by Timothy Prickett Morgan at The Next Platform.
Bioinspired computing is nothing new but with the rise in mainstream interest in machine learning, these architectures and software frameworks are seeing fresh light. This is prompting a new wave of young companies that are cropping up to provide hardware, software, and management tools—something that has also spurred a new era of thinking about AI problems.
We most often think of these innovations happening at the server and datacenter level but more algorithmic work is being done (to suit better embedded hardware) to deploy comprehensive models on mobile devices that allow for long-term learning on single instances of object recognition …
Momentum for Bioinspired GPU Computing at the Edge was written by Nicole Hemsoth at The Next Platform.
A major transformation is happening now as technological advancements and escalating volumes of diverse data drive change across all industries. Cutting-edge innovations are fueling digital transformation on a global scale, and organizations are leveraging faster, more powerful machines to operate more intelligently and effectively than ever.
Recently, Hewlett Packard Enterprise (HPE) has announced the new HPE Apollo 6500 Gen10 server, a groundbreaking platform designed to tackle the most compute-intensive high performance computing (HPC) and deep learning workloads. Deep learning – an exciting development in artificial intelligence (AI) – enables machines to solve highly complex problems quickly by autonomously analyzing …
Fueling AI With A New Breed of Accelerated Computing was written by Timothy Prickett Morgan at The Next Platform.
If the history of high performance computing has taught us anything, it is that we cannot focus too much on compute at the expense of storage and networking. Having all of the compute in the world doesn’t mean diddlysquat if the storage can’t get data to the compute elements – whatever they might be – in a timely fashion with good sustained performance.
Many organizations that have invested in GPU accelerated servers are finding this out the hard way when their performance comes up short when they get down to do work training their neural networks, and this is particularly …
Removing The Storage Bottleneck For AI was written by Timothy Prickett Morgan at The Next Platform.
When pre-split Hewlett-Packard bought Aruba Networks three years ago for $3 billion, the goal was to create a stronger and larger networking business that combined both wired and wireless networking capabilities and could challenge market leader Cisco Systems at a time when enterprises were more fully embracing mobile computing and public clouds.
Aruba was launched in 2002 and by the time of the acquisition had established itself as a leading vendor in the wireless networking market and had an enthusiastic following of users who call themselves “Airheads.” The worry among many of them was that once the deal was closed, …
Aruba Networks Leads HPE to the Edge was written by Nicole Hemsoth at The Next Platform.
For those who might expect Microsoft to favor its own Windows-centric platforms and tools to power comprehensive infrastructure for serving AI compute and software services for internal R&D groups, plan on being surprised.
While Microsoft does rely on some core windows features and certainly its Azure cloud services, much of its infrastructure is powered by a broad suite of open source tools. As Jim Jernigan, senior R&D systems engineer at Microsoft Research told us at the GPU Technology Conference (GTC18) this week, the highest volume of workloads running on the diverse research clusters Microsoft uses for AI development are running …
An Inside Look at What Powers Microsoft’s Internal Systems for AI R&D was written by Nicole Hemsoth at The Next Platform.
For more than a decade, GE has partnered with Nvidia to support their healthcare devices. Increasing demand for high quality medical imaging and mobile diagnostics alone has resulted in building a $4 billion segment of the $19 billion total life sciences budget within GE Healthcare.
This year at the GPU Technology Conference (GTC18), The Next Platform sat in as Keith Bigelow, GM & SVP of Analytics, and Erik Steen, Chief Engineer at GE Healthcare, discussed the challenges of deploying AI focusing on cardiovascular ultrasound imaging.
There are a wide range of GPU accelerated medical devices as well as those that …
Getting to the Heart of HPC and AI at the Edge in Healthcare was written by James Cuff at The Next Platform.
Big iron aficionados packed the room when ORNL’s Jack Wells gave the latest update on the upcoming 207 petaflop Summit supercomputer at the GPU Technology Conference (GTC18) this week.
In just eight years, the folks at Oak Ridge have pushed the high performance bar from the 18.5 teraflop Phoenix system to the 27 petaflop Titan. That’s a 1000x + improvement in eight years.
Summit will deliver 5-10x more performance than the existing Titan machine, but what is noteworthy is how Summit will do this. The system is set to have far fewer nodes (18,688 for Titan vs. ~4,800 for Summit) …
A First Look at Summit Supercomputer Application Performance was written by Nicole Hemsoth at The Next Platform.
When Nvidia co-founder and chief executive officer Jensen Huang told the assembled multitudes at the keynote opening to the GPU Technology Conference that the new DGX-2 system, weighing in at 2 petaflops at half precision using the latest Tesla GPU accelerators, would cost $1.5 million when it became available in the third quarter, the audience paused for a few seconds, doing the human-speed math to try to reckon how that stacked up to the DGX-1 servers sporting eight Teslas.
This sounded like a pretty high price, even for such an impressive system – really a GPU cluster with some CPU …
Nvidia’s DGX-2 System Packs An AI Performance Punch was written by Timothy Prickett Morgan at The Next Platform.
It has happened time and time again in computing in the past three decades in the datacenter: A device scales up its capacity – be it compute, storage, or networking – as high as it can go, and then it has to go parallel and scale out.
The NVLink interconnect that Nvidia created to lash together its “Pascal” and “Volta” GPU accelerators into a kind of giant virtual GPU were the first phase of this scale out for Tesla compute. But with only six NVLink ports on a Volta SXM2 device, there is a limit to how many Teslas can …
Nvidia Memory Switch Welds Together Massive Virtual GPU was written by Timothy Prickett Morgan at The Next Platform.
Machine learning has moved from prototype to production across a wide range of business units at financial services giant Capital One due in part to a centralized approach to evaluating and rolling out new projects.
This is no easy task given the scale and scope of the enterprise but according to Zachary Hanif who is director of Capitol One’s machine learning “center for excellence”, the trick is to define use cases early that touch as broad of a base within the larger organization as possible and build outwards. This is encapsulated in the philosophy Hanif spearheads—locating machine learning talent in …
Capital One Machine Learning Lead on Lessons at Scale was written by Nicole Hemsoth at The Next Platform.
The more things change, the more they remain the same — as do the two most critical issues for successful software execution. First, you remove the bugs, then you profile. And while debugging and profiling are not new, they are needed now more than ever, albeit in a modernized form.
The first performance analysis tools were first found on early IBM platforms in the early 1970s. These performance profiles were based on timer interrupts that recorded “status words” set at predetermined specific intervals in an attempt to detect “hot spots” inside running code.
Profiling is even more critical today, …
Mounting Complexity Pushes New GPU Profiling Tools was written by James Cuff at The Next Platform.
The expression, the tail wags the dog, is used when a seemingly unimportant factor or infrequent event actually dominates the situation. It turns out that in modern datacenters, this is precisely the case – with relatively rare events determining overall performance.
As the world continues to undergo a digital transformation, one of the most pressing challenges faced by cloud and web service providers is building hyperscale datacenters to handle the growing pace of interactive and real-time requests, generated by the enormous growth of users and mobile apps. With the increasing scale and demand for services, IT organizations have turned …
In Modern Datacenters, The Latency Tail Wags The Network Dog was written by Timothy Prickett Morgan at The Next Platform.
There are few people as visible in high performance computing programming circles as Michael Wolfe—and fewer still with level of experience. With 20 years working on PGI compilers and another 20 years before that working on languages and HPC compilers in industry, when he talks about the past, present and future of programming supercomputers, it is worthwhile to listen.
In his early days at PGI (formerly known as The Portland Group) Wolfe focused on building out the company’s suite of Fortran, C, and C++ compilers for HPC, a role that changed after Nvidia Tesla GPUs came onto the scene and …
The Future of Programming GPU Supercomputers was written by Nicole Hemsoth at The Next Platform.
Six years ago, when Google decided to get involved with the OpenPower consortium being put together by IBM as its third attempt to bolster the use of Power processors in the datacenter, the online services giant had three applications that had over 1 billion users: Gmail, YouTube, and the eponymous search engine that has become the verb for search.
Now, after years of working with Rackspace Hosting on a Power9 server design, Google is putting systems based on IBM’s Power9 processor into production, and not just because it wants pricing leverage with Intel and other chip suppliers. Google now has …
Google And Its Hyperscale Peers Add Power To The Server Fleet was written by Timothy Prickett Morgan at The Next Platform.
When IBM launched the OpenPower initiative publicly five years ago, to many it seemed like a classic case of too little, too late. But hope springs eternal, particularly with a datacenter sector that is eagerly and actively seeking an alternative to the Xeon processor to curtail the hegemony that Intel has in the glass house.
Perhaps the third time will be the charm. Back in 1991, Apple and IBM and Motorola teamed up to create the AIM Alliance, which sought to create a single unified computing architecture that was suitable for embedded and desktop applications, replacing the Motorola 68000 processors …
OpenPower At The Inflection Point was written by Timothy Prickett Morgan at The Next Platform.
The dark and mysterious art of artificial intelligence and machine learning is neither straightforward, or easy. AI systems have been termed “black boxes” for this reason for decades now. We desperately continue to present ever larger, more unwieldy datasets to increasingly sophisticated “mystery algorithms” in our attempts to rapidly infer and garner new knowledge.
How can we try to make all of this just a little easier?
Hyperscalers with multi-million dollar analytics teams have access to vast, effectively unlimited compute and storage of all shapes and sizes. Huge teams of analysts, systems managers, resilience and reliability experts are standing up …
There is No Such Thing as Easy AI — But We’re Getting Closer was written by James Cuff at The Next Platform.
You can’t swing a good-sized cat without hitting an enterprise running Oracle software in some shape or form. If it’s not Oracle’s ubiquitous database, then it’s one of its middleware platforms or its enterprise applications in the Fusion suite or its predecessors in the Oracle, Siebel, PeopleSoft, and JD Edwards suites.
Currently Oracle boasts 430,000 customers running its software – that’s quite an installed base. And it’s all teed up to become quite a battleground. Why?
Six months or so ago, news broke that Oracle was laying off a large number of hardware folks. Something like 2,500 Sparc and Solaris …
Turbulence – And Opportunity – Ahead In The Oracle Sparc Base was written by Timothy Prickett Morgan at The Next Platform.
There continues to be an ongoing push among tech vendors to bring artificial intelligence (AI) and its various components – including deep learning and machine learning – to the enterprise. The technologies are being rapidly adopted by hyperscalers and in the HPC space, and enterprises stand to reap significant benefits by also embracing them.
As we’ve noted many times here at The Next Platform, at the most basic level, machine learning and deep learning can enable enterprises to quickly sort through and analyze the massive amounts of data that they’re collecting to find patterns that can lead to better …
HPE Aims Apollo at Enterprise AI was written by Jeffrey Burt at The Next Platform.
We all know about the Top 500 supercomputing benchmark, which measures raw floating point performance. But over the several years there has been talk that this no longer represents real-world application performance.
This has opened the door for a new benchmark to come to the fore, in this case the high performance conjugate gradients benchmark, or HPCG, benchmark.
Here to talk about this on today’s episode of “The Interview” with The Next Platform is one of the creators of HPCG, Sandia National Lab’s Dr. Michael Heroux. Interestingly, Heroux co-developed HPCG with one of the founders of the Top …
What’s Ahead for Supercomputing’s Balanced Benchmark was written by Nicole Hemsoth at The Next Platform.