Original URL: http://www.theregister.co.uk/2014/03/31/cern_team_uses_gpus_to_discover_if_antimatter_falls_up_not_down/

CERN team uses GPUs to discover if antimatter falls up, not down

Einstein's General Relativity theory may be in for 'a big surprise'

By Rik Myslewski

Posted in Science, 31st March 2014 21:32 GMT

GTC In the next year or two, researchers at CERN's Large Hadron Collider (LHC) should be able to answer one of the most fundamental questions bedeviling physicists: what is the effect of gravity on antimatter?

As we all learned back in high school, matter falls to Earth at an acceleration of 9.8 meters per second squared, and according to the weak equivalence principle (WEP), that acceleration is the same for all bodies independent of their size, mass, or composition – in a vacuum, of course.

But is the same true for antimatter? Or does it fall faster, slower – or, possibly, does it "fall" upwards, away from the gravitational force?

No one knows – but an international group of European researchers, the AEgIS Collaboration – Antimatter Experiment: gravity, Interferometry, Spectroscopy – aims to find out with the help of the parallel-processing powers of GPUs.

"The principle of equivalence between gravitational and inertial mass is a foundation of General Relativity," explained Akitaka Ariga of the University of Bern, Switzerland, at last week's GPU Technology Conference (GTC) in San José, California.

"General Relativity is a very fundamental role in our physics, invented by Einstein," Ariga said, "but when Einstein made this theory, he didn't know that antimatter existed in the world."

Until now, there has been no need to adjust the theory of General Relativity to account for the behavior of antimatter in a gravitational field because no one has been able to measure that behavior – and that's the information that Ariga and the AEgIS Collaboration aim to supply, with a goal of accuracy within one per cent.

Ariga explained that the methodology behind the measurement is "simple" – create antiprotons and anti-electrons (positrons), combine them to create anti-hydrogen atoms, then fire them in an anti-hydrogen beam at a photographic emulsion–based detector, where the anti-hydrogen atoms will be annihilated by their collision with the matter in the detector.

"We then measure how much it falls," he said, "and it is expected that it [will be on the] order of 10 microns it will fall. Or it may fall up. So if we find that it falls up, this is a big surprise and big discovery."

From the AEgIS Collaboration's point of view, that's the easy part. What is a challenge, Ariga says, will be wrangling the enormous amount of data produced by the experiment, which will produce 3D images of grains in the photographic emulsion excited by the tracks of particles resulting from the energy released by the antimatter-matter annihilation.

AEgIS experiment: detection of particles created by antimatter-matter annihilation

The AEgIS researchers postulate that antimatter will be deflected down by gravity – but it may 'fall' up

Photographic-emulsion detectors have a long and successful history, Ariga explained, having been successfully employed as far back as French physicist Antoine Henri Becquerel's discovery of natural radioactivity in 1896. The 3D nature of the AEgIS experiment, however, coupled with the high resolution of the photographic-emulsion detectors used, creates data sets on the order of 10 terabytes for a 50 micron–thick, 10-centimeter-by-10-centimeter 3D target.

"And usually we use more than one square meter of detectors," he said, "and it's quite a lot of data – usually it exceeds a petabyte."

'You call that big data? THIS is big data...'

Analysis of photographic-emulsion detectors has historically been conducted by humans, Ariga explained, because the human eye is far better at identifying the tracks produced by particles than have been analyses conducted by computers.

As the amount of data compiled by such detectors has increased, however, it has become necessary to use a series of particle-tracking algorithms to isolate tracks from background noise and identify their trajectories.

The first such work was done in the 1970s and 1980s, but the speed, efficiency, and quality of the automated detection was limited by the technology of the time. Early TTL-based systems could analyze scans of the emulsions at a speed of about 0.003 square centimeters per hour, Ariga explained, and current multi-CPU and FPGA-based systems have increased that throughput to between 20 to 50 square centimeters per hour.

The evolution of computer-assisted particle-tracking scanning systems

GPUs: good for Crysis, great for particle-scattering analysis

The amount of data produced by the antimatter-gravity experiments being conducted by the AEgIS Collaboration, however, will require far more processing power, which Ariga and his team achieve by employing GPUs.

"Next generation we are going to reach 100 or 10,000 square centimeters per hour with GPUs," he said, "depending on the parts."

The AEgIS scanning system currently produces 210MB per second of data, he said, and its next-generation system will produced data at a rate of 1.6GB per second. Turning all that data into information that will solve the antimatter gravitational puzzle will require far more power than CPUs alone can provide.

AEgIS Collaboration: tracking algorithm processing time, CPU v. GPU

If you want to analyze 3D particle tracks, you're going to want a GPU

Fortunately, the team's work with a GPU-accelerated system has shown significant speed increases in the image-filtering, grain-recognition, and track-sensing components of the algorithm used by the team to analyze the data:

In testing their system, the team discovered that the most efficient algorithms use the CPU to find the "seed" of a particle track – the combination of any two nearby photographic-emulsion grains that may constitute a track, and then the GPU to count the number of grains in a candidate "seed" to determine whether it meets the minimum criteria of five or more grains along the same axis to be regarded as a true particle track.

AEgIS Collaboration: tracking algorithm

The CPU identifies a particle-track 'seed' and the GPU decides whether it qualifies as a true track

The AEgIS track-processing machine, as currently configured, contains a single water-cooled 3.2GHz, six-core, 12-thread Intel Core i7-3930K processor coupled with 16GB of DDR3 2400 memory, and three Nvidia GeForce GTX Titan GPU cards, each with 2688 CUDA cores and 6GB of memory and each capable of 4.5 TFLOPS, all powered by a 1250-Watt power supply.

Overall, Ariga said, the improvement from moving from a single-threaded CPU to the multi-threaded CPU, multi-GPU system that they have developed is on the order of 60X. "High-energy physics needs GPUs for the very heavy computation – experimental physics, and not just simulation," he said in summary.

Expect the results of the AEgIS Collaboration's search for the behavior of antimatter in a gravitational field in about a year or two, Agira said – but we can't help but think that replacing those year-old GTX Titans with three just-released 8TFLOPS GTX Titan Zs, and that two-and-a-half year old Core i7-3930K with, say, a few sockets full of Xeon E7 v2s might bring that day a little closer. ®