A Call to Arms for Parallel Programming Standards

By Nicole Hemsoth

November 16, 2010

Although the parallel programming landscape is relatively young, it’s already easy to get lost in. Beside legacy frameworks like MPI and OpenMP, we now have NVIDIA’s CUDA, OpenCL, Cilk, Intel Threading Building Blocks, Microsoft’s parallel programming extensions for .NET, and a whole gamut of PGAS languages.

And according to Intel’s Tim Mattson, that’s not necessarily a good thing. Mattson, who is a principal engineer (and parallel programming evangelist) at the company’s Visual Applications Research Lab, says all these software frameworks are leading to what he calls “choice overload” and this concerns him greatly.

From his point of view, the road to parallel programming need to be paved with open industry standards. And today then means MPI, OpenMP, and OpenCL. Given that some of Intel’s parallel software offerings such as the Cilk Plus and Array Building Blocks are proprietary, that viewpoint sometimes puts him at odds with his own company. But Mattson’s role as an Intel researcher forces him to look beyond the one or two-year timeframe of product cycles. He’s in it for the long-term, and that means Mattson is looking at what is best for the ecosystem ten years out. “First and foremost, we have to make sure that the right standards exist and they run best on Intel products,” he says.

At SC10 this year, Mattson will be in full software evangelist mode, speaking at seven different tutorials, BoFs and panels on various parallel programming topics. Three of these are geared to fire up the troops for OpenCL, an open standard parallel programming framework for heterogeneous multicore architectures. HPCwire spoke with Mattson shortly before the conference about the importance of open standards, his unapologetic enthusiasm for OpenCL, and his open animosity for the CUDA programming language.

HPCwire: What is the significance of OpenCL and why are you devoting so much time talking about it at SC10?

Tim Mattson: I think OpenCL is perhaps the most important development in the last five, if not the last ten years. The reason I make such an over-the-top statement is that I believe the core to solving the parallel programming challenge is standards. Only an idiot software developer would write code using a propriety API. Since I don’t like to work with idiots (laughs), I want to support good software developers out there by making sure they have the full suite of standards that they need.

So we have message passing covered: MPI. It’s great. We have shared memory covered: OpenMP. It’s great. The glaring hole — because frankly I don’t think any of us saw it coming in the early 2000s — is heterogenous platforms. So we have to fill that hole, and that’s what OpenCL does. So I think it’s incredibly important because now with MPI, OpenMP and OpenCL we’ve got the space covered with these low-level basic programming standards that are required to move things forward.

HPCwire: Well as far as openness goes, NVIDIA’s CUDA programming API is available for any vendor to implement for their particular parallel hardware architecture. For example, AMD could support CUDA for their x86 and GPU platforms. So couldn’t CUDA be adopted as a standard as well?

Mattson: Well, just think about it. I can’t speak for AMD, but why would Intel put resources into an API or language that we have absolutely no say in over how it’s going to evolve? To call CUDA a standard is just insulting. It’s not a standard until the various players can all have a voice in it. It’s ridiculous. If NVIDIA was serious about it, they would create an industry working group that owns the development of CUDA’s API and languages and that has a full voice in what happens with it. Oh, by the way, that’s what OpenCL is.

HPCwire: But there is at least one example of a standard language that emerged from a vendor initiative. Java was controlled by Sun Microsystems for many years and was adopted as a commercial standard because it became so popular across the industry. Don’t you think CUDA could follow that model?

Mattson: Well I know that’s what NVIDIA would like to see happen. And yes, Java is the one instance that would call into question how absolute my statement is. Java though was coming into a very different market and was tightly associated with the Web browser — a platform that cut across the industry. And Sun showed very early on that they were willing to support it as a cross-platform language. They had Java available on x86 and Sparc and showed a willingness to work across the vendors. NVIDIA — rationally, by the way — isn’t doing this with CUDA.

When you look closely at OpenCL, it covers everything CUDA can do and more. OpenCL has all the key vendors and covers a much wider space than CUDA. We’ve got the embedded people, the cell phone vendors, and game vendors all involved. So OpenCL is the right way to go; CUDA is the wrong way to go.

HPCwire: In the high performance computing community, though, there has been criticism that OpenCL doesn’t deliver the kind of performance required for HPC codes. Do you think that’s a fair assessment?

Mattson: That’s a statement that’s both true and false. There’s nothing pathological in the definition of OpenCL that prevents it from being every bit as efficient as CUDA. The thing about OpenCL is that it’s young; it just hasn’t been out very long. So it really comes down to the vendors as far as the quality of their implementations.

I think it’s important for the programmers out there — and let’s face it, they are the end user community for these technologies — to steer things in the right direction by insisting on standards. Look at how MPI and OpenMP came into existence. In both those cases, the user community insisted that these standards be the foundation of the software ecosystem, and the vendors stood behind them. We need people to do the same thing here and not get caught up with point solutions.

If NVIDIA engineers spent as much time optimizing OpenCL, it would run as fast as CUDA. So the performance arguments don’t hold a lot of sway with me, except when someone can say this feature of the language as defined is fundamentally going to be inefficient regardless of the quality of implementation. When people find those, we in the OpenCL group take it very seriously.

We’re roughly on a two-year cadence of coming out with new releases of the OpenCL spec, and we’re very focused on finding the weakness in OpenCL and aggressively evolving the language and to stay right in line with the latest hardware trends.

HPCwire: There are plenty of other languages that address multicore parallelism, some of which have been introduced by Intel. How does OpenCL fit in?

Mattson: Let me be really clear. There are three distinct standards that address multicore. MPI, for example, works great on multicore. OpenMP, if you have a shared address space, works really well too. And OpenCL covers heterogenous architectures. It’s really the trio that I’m pushing and Intel is 100 percent behind them.

On the other hand, yes, there is a trend that I find deeply disturbing of vendors wanting to distinguish themselves by creating new languages and proprietary APIs. It’s disturbing because time spent on a new language or proprietary API is time not spent on improving and establishing these standards. So this is where I’m kind of at odds with some of my colleagues at Intel. That’s just the way it goes.

Let’s face it. Vendors, left to their devices, want people to adopt a proprietary API that lock them into their platform. That’s not bad. NVIDIA is completely rational in wanting to lock people into their platform with CUDA. If I was working at NVIDIA, I’d probably be trying to do the same think. I think it’s up to the user community to refuse to let users get away with that game. They can do that by insisting on standards or open source solutions.

The three standards I mentioned are where I think most of the resource should go. But Intel did release Task Building Blocks –TBB — as open source. That was a very responsible thing to do. I was very excited, as was the TBB team, when that happened.

HPCwire: Another language is Ct, which started as a research project at Intel and has now been commercialized. How does that fit in to this parallel language ecosystem?

Mattson: Ct, which by the way, is now called Array Building Blocks, is a higher level abstraction of parallelism. While I’m a huge supporter of what they are doing with Array Building Blocks, [as for] how useful it will be in the marketplace, I’m not sure because it is proprietary. But I think some of the optimizations it does under the covers is very important. There are a lot of really important things about that project.

But I think we should distinguish creating good technologies versus confusing the market by having too many options out there. In 2004, if you wanted to do parallel programming you had Windows threads, pthreads on Linux, OpenMP, and MPI. Five options was fine. Now there are a dozen or more parallel programming languages out there. So I think we’re losing ground. I think choice overload is real. And that concerns me deeply.

HPCwire: Do you think parallelizing established languages like Java and Python is a positive development?

Mattson: Let me tell you where I think things are going and where we’ll be in 10 years. The question is do we get there cleanly or do we get there with messy detours along the way. Ultimately I think we have to raise the level of abstraction, which is what you see with these efforts around building parallelism into Python. We need to focus on the higher level frameworks that people are increasingly using to write software.

This is really what I spend the bulk of my time doing in my personal research, and with a group at UC Berkeley — to define patterned languages from which we can derive the frameworks, which then map down to the lower-level languages. I really want to make it so that only a small number of performance-oriented, efficiency layer programmers worry about these low-level languages — OpenCL, MPI, OpenMP, or TBB. But beyond that, people need to have some higher level framework they can work with. A parallel Python project like Copperhead is such an example. I’m very excited about it because I think that’s clearly the direction things are moving.

I learned this most clearly looking at the gaming industry, because that industry has been the leader in adopting multicore, and I mean adopting multicore as a successful business venture. Researchers have been playing with it for a long time, but in terms of creating multicore software, selling it, and building a profitable businesses around it, the gaming industry has led the charge.

They have these separations of concerns very sharply defined, and it works extremely well for them. Most of their programmers work in a higher level scripting language or with collections of libraries written in C++. And then they have a small number of “technology programmers” which are on the order of 10 percent of their software developers. They’re the guys who do the low-level stuff. And I think that kind of separation of concerns is what’s absolutely critical.

HPCwire: So you think higher level frameworks will be key to enabling these low-level parallel programming APIs you’re talking about?

Mattson: When we were sitting around creating OpenCL, we explicitly talked about that as our goal. In fact, you’ll find some places in the spec where we describe OpenCL as a hardware abstraction layer. We’re perfectly aware that OpenCL is obnoxiously low-level. It exposes so many details of the underlying platform. We achieve extreme portability by exposing everything and abstracting as little as we can. The reason we think that’s the right thing to do is because we view OpenCL ultimately as being a target for higher level frameworks. It’s young, so those higher level frameworks don’t exist yet, but I think they will and I think that will be the long range trend, not just for OpenCL, but for all these parallel languages.

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!

Nvidia Rolls Out Certified Server Program Targeting AI Applications

January 26, 2021

Nvidia today launched a certified systems program in which participating vendors can offer Nvidia-certified servers with up to eight A100 GPUs. Separate support contracts directly from Nvidia for the certified systems ar Read more…

By John Russell

XSEDE Supercomputers Square Off Against Ebola

January 26, 2021

COVID-19 may have dominated headlines and occupied much of the world’s scientific computing capacity over the last year, but many researchers continued their work to keep other deadly viruses at bay. One of those, Ebol Read more…

By Oliver Peckham

What’s New in HPC Research: Galaxies, Fugaku, Electron Microscopes & More

January 25, 2021

In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

Red Hat’s Disruption of CentOS Unleashes Storm of Dissent

January 22, 2021

Five weeks after angering much of the CentOS Linux developer community by unveiling controversial changes to the no-cost CentOS operating system, Red Hat has unveiled alternatives for affected users that give them severa Read more…

By Todd R. Weiss

China Unveils First 7nm Chip: Big Island

January 22, 2021

Shanghai Tianshu Zhaoxin Semiconductor Co. is claiming China’s first 7-nanometer chip, described as a leading-edge, general-purpose cloud computing chip based on a proprietary GPU architecture. Dubbed “Big Island Read more…

By George Leopold

AWS Solution Channel

Fire Dynamics Simulation CFD workflow on AWS

Modeling fires is key for many industries, from the design of new buildings, defining evacuation procedures for trains, planes and ships, and even the spread of wildfires. Read more…

HiPEAC Keynote: In-Memory Computing Steps Closer to Practical Reality

January 21, 2021

Pursuit of in-memory computing has long been an active area with recent progress showing promise. Just how in-memory computing works, how close it is to practical application, and what are some of the key opportunities a Read more…

By John Russell

Nvidia Rolls Out Certified Server Program Targeting AI Applications

January 26, 2021

Nvidia today launched a certified systems program in which participating vendors can offer Nvidia-certified servers with up to eight A100 GPUs. Separate support Read more…

By John Russell

Red Hat’s Disruption of CentOS Unleashes Storm of Dissent

January 22, 2021

Five weeks after angering much of the CentOS Linux developer community by unveiling controversial changes to the no-cost CentOS operating system, Red Hat has un Read more…

By Todd R. Weiss

HiPEAC Keynote: In-Memory Computing Steps Closer to Practical Reality

January 21, 2021

Pursuit of in-memory computing has long been an active area with recent progress showing promise. Just how in-memory computing works, how close it is to practic Read more…

By John Russell

HiPEAC’s Vision for a New Cyber Era, a ‘Continuum of Computing’

January 21, 2021

Earlier this week (Jan. 19), HiPEAC — the European Network on High Performance and Embedded Architecture and Compilation — published the 8th edition of the HiPEAC Vision, detailing an increasingly interconnected computing landscape where complex tasks are carried out across multiple... Read more…

By Tiffany Trader

Saudi Aramco Unveils Dammam 7, Its New Top Ten Supercomputer

January 21, 2021

By revenue, oil and gas giant Saudi Aramco is one of the largest companies in the world, and it has historically employed commensurate amounts of supercomputing Read more…

By Oliver Peckham

President-elect Biden Taps Eric Lander and Deep Team on Science Policy

January 19, 2021

Last Friday U.S. President-elect Joe Biden named The Broad Institute founding director and president Eric Lander as his science advisor and as director of the Office of Science and Technology Policy. Lander, 63, is a mathematician by training and distinguished life sciences... Read more…

By John Russell

Pat Gelsinger Returns to Intel as CEO

January 14, 2021

The Intel board of directors has appointed a new CEO. Intel alum Pat Gelsinger is leaving his post as CEO of VMware to rejoin the company that he parted ways with 11 years ago. Gelsinger will succeed Bob Swan, who will remain CEO until Feb. 15. Gelsinger previously spent 30 years... Read more…

By Tiffany Trader

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

By John Russell

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

By John Russell

Esperanto Unveils ML Chip with Nearly 1,100 RISC-V Cores

December 8, 2020

At the RISC-V Summit today, Art Swift, CEO of Esperanto Technologies, announced a new, RISC-V based chip aimed at machine learning and containing nearly 1,100 low-power cores based on the open-source RISC-V architecture. Esperanto Technologies, headquartered in... Read more…

By Oliver Peckham

Azure Scaled to Record 86,400 Cores for Molecular Dynamics

November 20, 2020

A new record for HPC scaling on the public cloud has been achieved on Microsoft Azure. Led by Dr. Jer-Ming Chia, the cloud provider partnered with the Beckman I Read more…

By Oliver Peckham

NICS Unleashes ‘Kraken’ Supercomputer

April 4, 2008

A Cray XT4 supercomputer, dubbed Kraken, is scheduled to come online in mid-summer at the National Institute for Computational Sciences (NICS). The soon-to-be petascale system, and the resulting NICS organization, are the result of an NSF Track II award of $65 million to the University of Tennessee and its partners to provide next-generation supercomputing for the nation's science community. Read more…

Is the Nvidia A100 GPU Performance Worth a Hardware Upgrade?

October 16, 2020

Over the last decade, accelerators have seen an increasing rate of adoption in high-performance computing (HPC) platforms, and in the June 2020 Top500 list, eig Read more…

By Hartwig Anzt, Ahmad Abdelfattah and Jack Dongarra

Aurora’s Troubles Move Frontier into Pole Exascale Position

October 1, 2020

Intel’s 7nm node delay has raised questions about the status of the Aurora supercomputer that was scheduled to be stood up at Argonne National Laboratory next year. Aurora was in the running to be the United States’ first exascale supercomputer although it was on a contemporaneous timeline with... Read more…

By Tiffany Trader

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Programming the Soon-to-Be World’s Fastest Supercomputer, Frontier

January 5, 2021

What’s it like designing an app for the world’s fastest supercomputer, set to come online in the United States in 2021? The University of Delaware’s Sunita Chandrasekaran is leading an elite international team in just that task. Chandrasekaran, assistant professor of computer and information sciences, recently was named... Read more…

By Tracey Bryant

Leading Solution Providers

Contributors

Top500: Fugaku Keeps Crown, Nvidia’s Selene Climbs to #5

November 16, 2020

With the publication of the 56th Top500 list today from SC20's virtual proceedings, Japan's Fugaku supercomputer – now fully deployed – notches another win, Read more…

By Tiffany Trader

Texas A&M Announces Flagship ‘Grace’ Supercomputer

November 9, 2020

Texas A&M University has announced its next flagship system: Grace. The new supercomputer, named for legendary programming pioneer Grace Hopper, is replacing the Ada system (itself named for mathematician Ada Lovelace) as the primary workhorse for Texas A&M’s High Performance Research Computing (HPRC). Read more…

By Oliver Peckham

At Oak Ridge, ‘End of Life’ Sometimes Isn’t

October 31, 2020

Sometimes, the old dog actually does go live on a farm. HPC systems are often cursed with short lifespans, as they are continually supplanted by the latest and Read more…

By Oliver Peckham

Gordon Bell Special Prize Goes to Massive SARS-CoV-2 Simulations

November 19, 2020

2020 has proven a harrowing year – but it has produced remarkable heroes. To that end, this year, the Association for Computing Machinery (ACM) introduced the Read more…

By Oliver Peckham

Nvidia and EuroHPC Team for Four Supercomputers, Including Massive ‘Leonardo’ System

October 15, 2020

The EuroHPC Joint Undertaking (JU) serves as Europe’s concerted supercomputing play, currently comprising 32 member states and billions of euros in funding. I Read more…

By Oliver Peckham

Intel Xe-HP GPU Deployed for Aurora Exascale Development

November 17, 2020

At SC20, Intel announced that it is making its Xe-HP high performance discrete GPUs available to early access developers. Notably, the new chips have been deplo Read more…

By Tiffany Trader

Nvidia-Arm Deal a Boon for RISC-V?

October 26, 2020

The $40 billion blockbuster acquisition deal that will bring chipmaker Arm into the Nvidia corporate family could provide a boost for the competing RISC-V architecture. As regulators in the U.S., China and the European Union begin scrutinizing the impact of the blockbuster deal on semiconductor industry competition and innovation, the deal has at the very least... Read more…

By George Leopold

HPE, AMD and EuroHPC Partner for Pre-Exascale LUMI Supercomputer

October 21, 2020

Not even a week after Nvidia announced that it would be providing hardware for the first four of the eight planned EuroHPC systems, HPE and AMD are announcing a Read more…

By Oliver Peckham

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This