NVIDIA Eyes Post-CUDA Era of GPU Computing

By Michael Feldman

December 7, 2011

Lost in the flotilla of vendor news at the Supercomputing Conference (SC11) in Seattle last month was the announcement of a new directives-based parallel programming standard for accelerators.  Called OpenACC, the open standard is intended to bring GPU computing into the realm of the average programmer, while making the resulting code portable across other accelerators and even multicore CPUs.

For obvious reasons, OpenACC is being heavily promoted and supported by NVIDIA, but it is The Portland Group (PGI) and Cray who are driving the early effort to commercialize the technology. PGI already has implemented a very similar a set of accelerator directives, which became part of the foundation for the OpenACC standard. Cray is developing its own OpenACC compiler and its XK6 customers, like Oak Ridge National Lab and the Swiss National Supercomputing Centre, are expected to be among the first supercomputer users of the technology

In a nutshell, OpenACC directives work much the same as OpenMP directives, but are specifically applicable to highly data parallel codes.  They can be inserted into standard C, C++ and Fortran programs to direct the compiler to parallelize certain code sections.  The compiler takes care of the logistics of moving data back and forth between the CPU and the GPU (or whatever) and mapping the computation onto the appropriate processor.

The idea is to enable developers to make relatively small modifications to existing (or new) code in order to expose  parallel regions for acceleration.  Since the directives are designed to apply to a generic parallel processor, the same code can run on a multicore CPU, GPU, or any other type of parallel hardware that is supported by the compiler.  This hardware independence is especially important to the HPC community, which is loathe to adopt vendor-specific, non-portable programming environments.

From NVIDIA’s perspective, the overriding goal is to bring GPU computing into the post-CUDA age.  CUDA C and Fortran are the most widely used programming languages for GPU programming today, but the underlying technology is proprietary to NVIDIA and offers a relatively low-level software model of GPU computing. As a result, the use of CUDA today tends to be restricted to computer science types, rather than the average programmer or researcher.

OpenCL, which is supported by NVIDIA, AMD and many others, also provides a parallel programming framework for GPUs and other accelerators, and unlike CUDA, is a bona fide open standard (under the direction of the Khronos Group — the same organization that brought us OpenGL).  But like CUDA, OpenCL is relatively low-level, requiring a fairly intimate knowledge of the inner workings of the target processor.  Therefore, like CUDA, use of OpenCL is mostly confined to computer scientists.

NVIDIA estimates there are over 100,000 CUDA programmers on the planet and a substantially smaller number of OpenCL developers, but they see a much larger potential audience if they can make GPU programming more open and developer-friendly.  Essentially they believe OpenACC will be able to make GPU technology accessible to the millions of scientists and researchers who don’t care to dabble in the low-level intricacies of processor architectures and chip-to-chip communications.

Steve Scott, CTO of NVIDIA’s Tesla business unit, sums up the goal of OpenACC thusly: “What we’d like to do at this point is to substantially increase the breadth of applicability and the number of people using GPUs.”

According to Scott, the high-level nature of OpenACC is not going to impact execution performance significantly. While in his previous CTO role at Cray, he encountered accelerator directives-based codes that were getting within 5 or 10 percent of the performance of hand-coded CUDA.  According to him, that was fairly typical.  Some applications, Scott says, were even doing better than their CUDA alternates, thanks to the ability of the compiler to optimize certain codes beyond what mere mortals could achieve. In any case, OpenACC is designed to be interoperable with CUDA, so hand-tuned kernels can work seamlessly with directives-based code if need be.

Besides PGI and Cray, CAPS enterprise, a French developer of multicore software tools, has also signed up to support the new directives.  All three vendors are expected to have compilers with OpenACC support ready in the first half of 2012.  Notably missing from the list of OpenACC supporters are Intel and AMD, although both have processors (multicore x86, AMD APUs and GPUs, and the Intel MIC) that would certainly be capable targets. That wouldn’t necessarily stop PGI, CAPS, or Cray from building OpenACC-enabled compilers for Intel and AMD hardware, however.

PGI and NVIDIA are in the process of running a free 30-day trial for developers interested in kicking the tires on PGI’s current accelerator directive compiler. The claim is that the technology will at least double application performance with less than 4 weeks of developer effort. Hundreds of researchers have already registered for the trial and this week NVIDIA has reported some initial results. At least one developer was able to get a 5X performance boost on his application after just a single day of tweaking the code.

But the real end game for OpenACC supporters is for the directives to be incorporated into the OpenMP standard.  Since OpenACC was derived from work done within the OpenMP Working Group on Accelerators, it stands to reason that this will indeed happen. Although there is no timeline for when the technology will be folded into OpenMP, it’s most likely to be occur in conjunction with the release of OpenMP 4.0, which is expected to be launched sometime in 2012.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire