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!

IBM Launches Commercial Quantum Network with Samsung, ORNL

December 14, 2017

In the race to commercialize quantum computing, IBM is one of several companies leading the pack. Today, IBM announced it had signed JPMorgan Chase, Daimler AG, Samsung and a number of other corporations to its IBM Q Net Read more…

By Tiffany Trader

TACC Researchers Test AI Traffic Monitoring Tool in Austin

December 13, 2017

Traffic jams and mishaps are often painful and sometimes dangerous facts of life. At this week’s IEEE International Conference on Big Data being held in Boston, researchers from TACC and colleagues will present a new Read more…

By HPCwire Staff

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in what has become an overwhelmingly two-socket landscape in the d Read more…

By John Russell

HPE Extreme Performance Solutions

Explore the Origins of Space with COSMOS and Memory-Driven Computing

From the formation of black holes to the origins of space, data is the key to unlocking the secrets of the early universe. Read more…

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as several tech giants jockey to establish a pole position in the race toward commercialization of quantum. This week, Microsoft took the next step in Read more…

By Tiffany Trader

IBM Launches Commercial Quantum Network with Samsung, ORNL

December 14, 2017

In the race to commercialize quantum computing, IBM is one of several companies leading the pack. Today, IBM announced it had signed JPMorgan Chase, Daimler AG, Read more…

By Tiffany Trader

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as several tech giants jockey to establish a pole position in the race toward commercializ Read more…

By Tiffany Trader

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be care Read more…

By Alex R. Larzelere

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Microsoft Spins Cycle Computing into Core Azure Product

December 5, 2017

Last August, cloud giant Microsoft acquired HPC cloud orchestration pioneer Cycle Computing. Since then the focus has been on integrating Cycle’s organization Read more…

By John Russell

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

HPE In-Memory Platform Comes to COSMOS

November 30, 2017

Hewlett Packard Enterprise is on a mission to accelerate space research. In August, it sent the first commercial-off-the-shelf HPC system into space for testing Read more…

By Tiffany Trader

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Leading Solution Providers

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

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