The next big leap in scientific computing is the race to exascale, the capability for a computer to perform 1 million trillion floating-point operations per second.

The US Department of Energy, which will fund the development of such systems, has set targets of what it wants from exascale systems. It wants one available by 2018-2022 and to consume less than 20 megawatts of power.

For scientific computing having this much processing power available would mean that researchers could tackle the next big questions in science. It has been likened to the Hubble telescope, and the advantages it offered scientists in seeing far-off previously invisible stars.

But the problem is current technology is not at the level to accommodate the requirements of exascale computing. In order to reach exascale, at an efficient power and price point, new architecture will have to be developed that changes the way high performance computers compute and move data. Current generation hardware can not simply be scaled up until it reaches exascale level, the power required would simply be enormous and uneconomical.

While the US has put a great deal of resources into the necessary research required to hit exascale, in the end it may be beaten to exascale by another country.

In order to get a better understanding of what needs to happen before we reach exascale, and to get a perspective on some of the other rising powers in HPC computing, VR World spoke with Oak Ridge National Laboratory’s Jack Dongarra who delivered a keynote at the Supercomputing Frontiers 2015 conference in Singapore on the topic.

VR World: During your keynote you mentioned the ‘exascale challenge’. In your opinion, how do we get there from here? What has to happen?

Jack Dongarra: We can’t use today’s technology to build that exascale machine. It would cost too much money, and the power requirements would be way too much. It would take 30 Tianhe-2 clusters in order to get there. We have to have some way to reduce the power and keep the cost under control.

Today, all of our machines are over-provisioned for floating-point. They have an excess floating-point capability. The real issues are related to data movement. It’s related to bandwidth. For example, you have a chip. And this chip has increasing computing capability — you put more cores on it. Those cores need data, and the data has to come in from the sides. You’ve got area that’s increasing due to the computing capability but the perimeter is not increasing to compensate for it. The number of pins limits the data that can go in. That’s the crisis we have.

That has to change. One way it changes is by doing stacking. 3D stacking is a technology that we have at our disposal now. That will allow much more information flow in a way that makes a lot more sense in terms of increasing bandwidth. We have a mechanism for doing that, so we get increased bandwidth. That bandwidth is going to help reduce [power draw] as we don’t have to move data into the chip.

The other thing that’s going to happen is that photonics is going to take over. The data is going to move not over copper lines but over optical paths. The optical paths reduce the amount of power necessary. So that’s a way to enhance the data movement, and to reduce the power consumption of these processors. The chip gets much more affordable, and we can have a chance at turning that computing capability into realized performance — which is a key thing.

In the US, I think we’ll reach exascale in 2022. 2022 is the point where the money will be in place and it’s a question of money. We could build a machine today, but it it would be too expensive. The current thinking is it will be realizable around 2020, and the US is going to be able to deploy the machine in 2022. The money won’t be in place until then, but the technology will be ready ahead of time.

VRW: What’s your take on vendors’ 3D stacking efforts so far?

JD: It’s great. It has to happen. It’s gotta be that way. It’s a natural way to move. It’s going to be the key thing in terms of performance enhancement in the next few years, and being able to effectively employ that as a device. Things look very positive.

VRW: Over the last few years we’ve witnessed China becoming a rising CPU player, with its domestic Alpha and MIPS-based CPUs. Do you have a feeling that conventional CPU vendors have over complicated things for themselves?

JD: China has an indigenous processor which may or may not come out and be deployed in a high performance machine. There are some rumors that the next big machine would be based on the ShenWei CPU. I can understand the motivation for China wanting a processor, they don’t want to dependent on Western technology for these things. There are some issues here. It’s not going to be on x86 architecture, so software will have to be re-written for this machine. Software is a big deal on these systems, but that can be overcome.

When China does deploy this wide scale, Intel will stand up and take notice. It will be a big thing, now China will be in a position to use their product and not Intel’s product. That becomes a big issue.

VRW: Do you see any emerging powers in the HPC space that are outside the traditional industrial powers of US, Japan, Europe and China?

JD: Things have been dominated by the US, followed by the European Union and Japan. China is a more recent investor in high performance computing. Then there are other countries that claim to be wanting to be involved. Korea is a country that claims to wanting to be involved. They are making noise about buying a big machine. They aren’t going to build a machine — they don’t have the processors — they are going to buy a machine from the US.

India has made claims they want to do something. Again, they aren’t going to make their machine. They are going to purchase one.

VRW: Thanks for your time.

The post Jack Dongarra on the Great Exascale Challenge and Rising HPC Powers appeared first on VR World.

Read VR World’s full interview with Prof. Jack Dongarra here. 

Countries around the world, particularly emerging markets, all would love to have a top 100 supercomputer. Being able to have a supercomputer that ranks in the top 100, or even the top 10, would be a national showpiece – a sign of technological might – and would please many of the country’s politicians.

The United States is the world’s dominate high performance computing power, as it has more supercomputers in the top 500 list than any other single country, but China would like to challenge this hegemony. After all, China has the world’s fastest supercomputer, Tianhe-2 , at the National Supercomputer Center at Sun Yat-sen University in Guangzhou.

But in an exclusive interview with VR World, Dr. Jack Dongarra of Oak Ridge National Laboratory and the University of Tennessee, said that China’s HPC stature may be something of a facade. Tianhe-2, while definitely the world’s fastest supercomputer, is somewhat idle and is not being used to its full capacity.

“The real question is: what are they going to use the machine for. I question, at some level, what the Chinese are doing with these big machines,” Dongarra said. “They are are not using the accelerator part of the machine.” [48,000 Intel Xeon Phi 31S1P Accelerator cards].

“I go visit the computing facilities [in China] – and I’m not saying that they are being used for things that are secret – I’m saying that I don’t know what they are being used for,” he continued.

Dongarra explained that part of the reason why Tianhe-2 is more idle than other top supercomputers is because of the funding model China’s government provides. The government paid for the costs to develop and construct the machine, but not for its operational costs which is not the norm in the scientific computing community.

The additional difficulty might be the machine setup China decided to go with. Intel’s (NASDAQ: INTC) Xeon Phi hasn’t proven itself in ease of use when compared to pure CPU code or accelerated code through GPGPU accelerators such as the Nvidia (NASDAQ: NVDA) Tesla or AMD (NASDAQ: AMD) FirePro S Series.

“They have to come up with some mechanism to pay for it,” Dongarra said. “In scientific computing we don’t pay for computing time. It’s not in the culture of how we do business. A situation where people have to pay for computing time limits the computing time being used.”

The post Jack Dongarra: China Isn’t the Emerging HPC Power You Think It Is appeared first on VR World.