CUDA Spotlight: GPU-Accelerated High Energy Physics






Large Hadron Collider
Large Hadron Collider (courtesy CERN)

NVIDIA: Valerie, what are some of the important science questions in the study of the universe?
Valerie: Some of the key questions being explored are:

  • What is the dark matter that keeps galaxies together throughout the universe?
  • What is the origin of the large asymmetry between matter and anti-matter in the universe?
  • Why is gravity so weak?
  • Are the Standard Model particles all fundamental?
  • Are there more forces and can they be unified?
  • Last but not least, is the scalar discovered at the LHC on July 4, 2012 the Standard Model Higgs or a different Higgs Boson of the electroweak symmetry breaking and fermion masses? Surprises are still possible and the hunt for rare decays and processes is going on in parallel.

It’s exciting to be working in the area of high-energy physics because answers to these questions and many more might be hidden in the multi TeV (tera-electron volt) region accessible by the Large Hadron Collider.NVIDIA: In layman’s terms, what is the Large Hadron Collider (LHC)?
Valerie: The LHC is the world’s largest and most powerful particle accelerator. It first started up on September 10, 2008 and per the CERN website, remains the latest addition to CERN’s accelerator complex. It consists of a 27-kilometer ring of superconducting magnets with a number of accelerating structures to boost the energy of the particles along the way to a record energy of 8 TeV.

Inside the accelerator, two high-energy proton beams travel in opposite directions close to the speed of light before they are made to collide at four locations around the accelerator ring. These locations correspond to the positions of four particle detectors – ATLAS, CMS, ALICE and LHCb.

These detectors record the particles left by debris from the collisions and look for possible new particles or interactions. LHC is the best microscope able to probe new physics at 10-19 m and is also the best “time machine,” creating conditions similar to what happened a picosecond after the Big Bang at temperatures 8 orders of magnitude larger than the core of the Sun.

Hence, the LHC is a discovery machine that provides us the opportunity to explore the physics hidden in the multi TeV region.

NVIDIA: How can GPUs accelerate research in this field?






NVIDIA: When did you first realize that GPUs were interesting for high-level triggering?
Valerie: The Trigger and Data Acquisition (DAQ) systems in a modern collider experiment, such as the ATLAS and CMS experiments at the LHC, provide essential preliminary online analysis of the raw data for the purpose of filtering potentially interesting events into the data storage system.

It is essentially the “brain of the experiment.” Wrong decisions may cause certain topologies to evade detection forever. Realizing the significant role of the trigger, the current window of opportunity we have during the two years of the LHC shutdown, and the responsibility to enhance or even extend the physics reach at the LHC after the shutdown, led me to revisit the software-based trigger system.

By its very nature of being a computing system, the HLT relies on technologies that have evolved extremely rapidly. It is no longer possible to rely solely on increases in processor clock speed as a means for extracting additional computational power from existing architectures.

However, it is evident that innovations in parallel processing could accelerate the algorithms used in the trigger system and might yield significant impact.

One of the most interesting technologies, which continue to see exponential growth, is modern GPUs. By making a massive number of parallel execution units highly programmable for general purpose computing, and the supported API (CUDA C), it was obvious that putting this together with the flexibility of the CMS HLT software and hardware computing farm could enhance the HLT performance and potentially extend the reach of the physics at the LHC.




NVIDIA: Describe the system you are running on.
Valerie: The performance of the algorithms was evaluated using Intel multicore CPUs and NVIDIA Tesla C2075 and K20c GPUs. CPU code was written in C/C++ and OpenMP, while the GPU code uses CUDA C.

NVIDIA: Is your work in the research stage? What’s the next step?
Valerie: The work is still in the research stage, with preliminary results from testing the track reconstruction and the trigger algorithms using the Hough transform algorithm on simulation data and data taken at LHC by the CMS detector. Now that we have demonstrated proof of principle using input data from a simple detector simulation our goal is to methodically quantify and optimize the performance of the track reconstruction. The new proposed trigger algorithms aim to select long-lived particles or even Black Holes decaying in the silicon tracker system using input data recorded by the CMS detector. In principle it can also be implemented in the ATLAS detector.

Hough transform algorithm







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About Brad Nemire

Brad Nemire