OpenCL Gains Ground On CUDA

By Michael Feldman

February 28, 2012

As the two major programming frameworks for GPU computing, OpenCL and CUDA have been competing for mindshare in the developer community for the past few years. Until recently, CUDA has attracted most of the attention from developers, especially in the high performance computing realm. But OpenCL software has now matured to the point where HPC practitioners are taking a second look.

Both OpenCL and CUDA provide a general-purpose model for data parallelism as well as low-level access to hardware, but only OpenCL provides an open, industry-standard framework. As such, it has garnered support from nearly all processor manufacturers including AMD, Intel, and NVIDIA, as well as others that serve the mobile and embedded computing markets. As a result, applications developed in OpenCL are now portable across a variety of GPUs and CPUs.

Although OpenCL 1.0 was introduced in December 2008, just a year and a half after the NVIDIA launched its first version of CUDA, OpenCL still trails CUDA in popularity by a wide margin, especially with regard to HPC. That is mostly due to a concerted effort by NVIDIA to establish CUDA as the dominant programming framework for GPU application development in this realm..

AMD has been the most vocal booster of OpenCL technology for technical computing, but it’s lack of a competitive product set for high-end GPU computing has muted that message. Thus far, OpenCL usage has been mostly relegated to client-side computing, especially for mobile platforms, which have increasingly incorporated GPU silicon into their designs. Apple, who initially developed OpenCL before handing it off to the open-standard Khronos Group, was instrumental in getting the technology off the ground.

The knock on OpenCL for HPC users has been lack of maturity, which has resulted in low performance, compared to CUDA. There is also the perception that support from the principle HPC chipmakers (Intel, AMD and NVIDIA) would be less than enthusiastic, at least for their high-end processors. In many ways, that’s still true, given that NVIDIA is devoting most of its attention to its home-grown CUDA software, while Intel seems to have settled on its own parallel programming frameworks, mainly Cilk Plus and Threading Building Blocks.

AMD though, continues to champion OpenCL, and some of their more recent compiler and library releases have improved performance considerably. In fact, Kyle Spafford, from the Future Technology Group at Oak Ridge National Lab (ORNL), has been benchmarking the two technologies for some time and is now convinced that OpenCL performance is now on par with that of CUDA. He recently presented his findings at Georgia Tech’s Keeneland Workshop.

Spafford’s ran ORNL’s Scalable Heterogeneous Computing Benchmark Suite (SHOC) that has been optimized for both CUDA and OpenCL, and found that OpenCL can match CUDA performance on most of the basic math kernels. He also found that OpenCL’s performance on some kernels, like SGEMM, has increased 10-fold since 2009. The one code that CUDA still has a significant performance advantage is that of the Fast Fourier Transform (FFT). Spafford attributes CUDA’s better FFT performance on its use of a fast intrinsic, with OpenCL implementation (NVIDIA’s in this case*) employing a slower, more accurate version. If the implementations are matched, the performance difference goes away, says Spafford.

Others have found similar behavior on stand-alone science applications. A research group at Dartmouth running a numerical model of gravitation waves with OpenCL and CUDA found similar performance between OpenCL and CUDA, in this case on Tesla GPUs and IBM’s Cell BE processors. In the resulting paper, the researchers conclude that “an OpenCL-based implementation delivers comparable performance to that based on a native SDK on both types of accelerator hardware.”

GPU software maker AccelerEyes has seen CUDA and OpenCL performance equalize. The company, which recently released OpenCL-powered beta versions of their two flagship software products, ArrayFire and Jacket, has found that for most kernel codes, the two technologies now exhibit similar performance. Like ORNL, they found FFT speed is still better on CUDA due to NVIDIA’s faster implementation, but AMD’s OpenCL compiler and libraries have improved considerably, both in scope and performance.

According to AccelerEyes CEO John Melonakos, over half of their customers develop their GPU-accelerated code on their PCs before deploying to a workstation or cluster, so the ability to support non-NVIDIA hardware can be quite useful. For example, customers using MacBooks as development platforms couldn’t run CUDA there because Apple has no NVIDIA GPU option on its latest laptops. And since the AMD OpenCL libraries that AccelerEyes used in their beta offerings work just fine on Intel CPUs, AMD CPUs, and NVIDIA GPUs, there are no hardware incompatibility issues.

Then there are users who are just unwilling to adopt vendor-specific software stacks such as CUDA. “There are a class of people who absolutely want to do GPU computing but are resistant to anything that is vendor-locked,” Melonakos told HPCwire. He says this is group that has jumped onto their OpenCL-based offerings first.

To counter that kind of perception, NVIDIA has recently opened up the CUDA compiler source code for third-party developers. Significantly though, NVIDIA is not putting its all-important CUDA math libraries, like CUBLAS and CUFFT, into the open source pot. According to Melonakos, the large and mature library set is CUDA’s real strength in the technical computing arena. Open source or not, NVIDIA still retains control of the CUDA software technology, which is why it is still perceived as a vendor-specific solution.

Even NVIDIA and Intel are hedging their bets with OpenCL though, with both vendors offering software hooks for their respective hardware. At this point, these companies are providing this support as a nod to their mobile computing developers (although Intel is reportedly working on a MIC processor port too). But since there is an increasing amount of cross-pollination between mobile and HPC these days, it’s not clear how developers will end up using these technologies.

In fact, if the mobile space latches onto OpenCL in a big way and it becomes the standard low-level solution for heterogenous computing, that could help speed its adoption at the high-end. Once OpenCL reaches a critical mass of acceptance in a volume market such as that, there will be a rapid increase in demand for robust compilers and libraries. As Melonakos put it: “I dont think OpenCL is going away.”

[Editor’s note: The original article erroneously stated that the SHOC benchmark work used AMD’s implementation of OpenCL, rather than NVIDIA’s. We regret the error.]

Subscribe to HPCwire's Weekly Update!

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

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This