Speaker
David Abdurachmanov
(Vilnius University (LT))
Description
Power density constraints are limiting the performance improvements
of modern CPUs. To address this we have seen the introduction of
lower-power, multi-core processors, but the future will be even
more exciting. In order to stay within the power density limits but
still obtain Moore's Law performance/price gains, it will be necessary
to parallelize algorithms to exploit larger numbers of lightweight
cores and specialized functions like large vector units. Example
technologies today include Intel's Xeon Phi and GPGPUs.
Track finding and fitting is one of the most computationally
challenging problems for event reconstruction in particle physics.
At the High Luminosity LHC, for example, this will be by far the
dominant problem. The need for greater parallelism has driven
investigations of very different track finding techniques including
Cellular Automata or returning to Hough Transform techniques
originating in the days of bubble chambers. The most common track
finding techniques in use today are however those based on the
Kalman Filter. Significant experience has been accumulated with
these techniques on real tracking detector systems, both in the
trigger and offline. They are known to provide high physics
performance, are robust and are exactly those being used today for
the design of the tracking system for HL-LHC. We report the results
of our investigations into the potential and limitations of these
algorithms on the new parallel hardware.
Primary authors
Avi Yagil
(Univ. of California San Diego (US))
Dr
Daniel Sherman Riley
(Cornell University (US))
Frank Wuerthwein
(Univ. of California San Diego (US))
Giuseppe Cerati
(Univ. of California San Diego (US))
Ian Macneill
(Univ. of California San Diego (US))
Kevin Mcdermott
(Cornell University (US))
Matevz Tadel
(Univ. of California San Diego (US))
Dr
Peter Elmer
(Princeton University (US))
Peter Wittich
(Cornell University (US))
Dr
Steven Lantz
(Cornell University)
