Fermilab Computing Division

Kalman Filter Tracking on Parallel Architectures

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Full Title: Kalman Filter Tracking on Parallel Architectures
Date & Time: 13 Jan 2016 at 14:00
Event Location: WH1W
Event Topic(s): Computing Techniques Seminar
Event Moderator(s):
Event Info: Speaker: Giuseppe Cerati

Abstract: Limits on power dissipation have pushed CPUs to grow in parallel processing capabilities rather than clock rate, leading to the rise of "manycore" or GPU-like processors. In order to achieve the best performance, applications must be able to take full advantage of vector units across multiple cores, or some analogous arrangement on an accelerator card. Many core techniques will certainly be important if real-time processing is to keep up with detector data rates at CERN's Large Hadron Collider, for example, where planned upgrades will soon cause data to be produced at an unprecedented pace. For the High Luminosity LHC, the most computationally demanding task is expected to be track finding and fitting. It is projected to become by far the dominant problem in event reconstruction. Most of the common software for tackling this problem is based on Kalman filtering; these methods are known to produce robust physics results on real tracking detector systems, both in the trigger and offline. But Kalman filtering involves repetitious small-matrix operations that lack a natural SIMD formulation. The challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying SIMD (or SIMT) instruction set of modern processor architectures. On their own, compilers are often unable to produce optimal code of this type, even when given parallelization directives via OpenMP and other pragmas. However, the source code itself may be written in a way that assists the compiler in creating SIMD instructions and orchestrating the data movement appropriately. In our case, an abundance of small parallel tasks is available: we show how the data and associated tasks can be grouped in a way that is conducive to both multithreading and vectorization. We demonstrate very good vector performance and scalability in key portions of the code. We also identify issues that may currently inhibit the full application from scaling up to large numbers of threads.

Bio: Giuseppe Cerati is a member of the CMS Collaboration since 2006, when he started his PhD at the University of Milano-Bicocca. He is a post-doc at University of California San Diego since 2010. His physics contributions range from the Higgs boson discovery, to SUSY searches and to SM measurements. He was CMS Tracking convener in the period 2013-2014, he led the group in the preparation of the reconstruction algorithms for the LHC Run2. In 2014 he initiated the R&D project for track reconstruction on parallel architectures, whose status present in this seminar.

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Other documents for this event

CS-doc-# Title Author(s) Topic(s) Last Updated
5676-v1 Kalman Filter Tracking on Parallel Architectures Oliver Gutsche Computing Techniques Seminars
14 Jan 2016

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