tipuesearch_content.js

var tipuesearch = {"pages":[{"title":"Publications and Presentations","text":"Publications Speeding up particle track reconstruction using a parallel Kalman filter algorithm - Journal of Instrumentation 09 (2020) P09030 arXiv , DOI Parallelizing the unpacking and clustering of detector data for reconstruction of charged particle tracks on multi-core CPUs and many-core GPUs - Proceedings of Connecting the Dots 2020 (virtual conference) Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm - Proceedings of the 24th Annual International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019) (Adelaide, Australia) arXiv Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm - Proceedings of Connecting the Dots and Workshop on Intelligent Trackers (CTD/WIT 2019) (Valencia, Spain) arXiv Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector - Proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019) (Saas Fee, Switzerland) arXiv , DOI , pdf Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events - Proceedings of the 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv , DOI , pdf Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures - Proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle, WA, USA) arXiv , DOI , pdf Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs - Proceedings of Connecting The Dots / Intelligent Trackers 2017 (Orsay, France) arXiv , DOI , pdf Kalman filter tracking on parallel architectures - Journal of Physics: Conference Series 898 (2017) 042051, Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco, CA, USA) arXiv , DOI , pdf Kalman Filter Tracking on Parallel Architectures - Proceedings of Connecting The Dots 2016 (CTD2016) workshop (Vienna, Austria) arXiv , DOI , pdf Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders - Proceedings of the 2015 IEEE NSS/MIC Conference (San Diego, CA, USA) arXiv , DOI , pdf Kalman Filter Tracking on Parallel Architectures - Journal of Physics: Conference Series 664 (2015) 072008, Proceedings of the 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) (Okinawa, Japan) arXiv , DOI , pdf Traditional Tracking with Kalman Filter on Parallel Architectures - Journal of Physics: Conference Series 608 (2015) 012057, Proceedings of 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT 2014) (Prague, Czech Republic) arXiv , DOI , pdf Presentations 5 May 2020 - mkFit update: strip tracker unpacking and clustering on GPU - Dan Riley, CMS HLT Upgrade TSG Weekly Meeting 28 Apr 2020 - mkFit update: strip tracker unpacking and clustering on GPU - Dan Riley, CMS Tracker DPG - Tracking POG general Weekly Meeting 22 Apr 2020 - Parallelizing the unpacking and clustering of detector data for reconstruction of charged particle tracks on multi-core CPUs and many-core GPUs - Bei Wang, Connecting the Dots 2020 12 Feb 2020 - mkfit and HL-LHC tracking in CMS - Mario Masciovecchio, Joint HSF event reconstruction/trigger working group and IRIS-HEP Topical Meeting 11 Feb 2020 - Accelerated Full Tracking at (CMS) HLT - Mario Masciovecchio, CMS scouting kick-off workshop 5 Feb 2020 - mkFit update: strip tracker unpacking and clustering on CPU and GPU - Dan Riley, CMS Tracker DPG/Strip Calibration and Local Reconstruction Weekly Meeting 7 Nov 2019 - Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm - G. Cerati, 24th Annual International Conference on Computing in High Energy and Nuclear Physics 9 Sep 2019 - mkFit Status Update - A. Reinsvold Hall, Tracking POG Meeting 17 Jul 2019 - HEP Event Reconstruction with Cutting Edge Computing Architectures - G. Cerati, SciDAC-4 PI meeting 13 Jun 2019 - Advanced Methods for Data Processing and Reconstruction - A. Reinsvold Hall, Informal DOE Briefing: USCMS/FNAL Computing Innovation towards HL-LHC 6 Jun 2019 - Speeding up CMS track reconstruction - A. Reinsvold Hall, 2019 USCMS Annual Collaboration Meeting 16 Apr 2019 - Speeding up particle track reconstruction using a vectorized and parallelized Kalman Filter algorithm - A. Reinsvold Hall, CMS Tracker Activities at FNAL Working Group 11 Apr 2019 - Parallelized Kalman Filter Tracking - M. Masciovecchio, Tracking POG Meeting, CMS Week 10 Apr 2019 - mkFit Project: Speeding up particle track reconstruction using a vectorized and parallelized Kalman Filter algorithm - A. Reinsvold Hall, IRIS-HEP Topical Meeting 10 Apr 2019 - mkFit Project: Speeding up particle track reconstruction using a vectorized and parallelized Kalman Filter algorithm - A. Reinsvold Hall, PC, Trigger, PPD, O&C Plenary, CMS Week 04 Apr 2019 - mkFit Project: Speeding up particle track reconstruction using a vectorized and parallelized Kalman Filter algorithm - A. Reinsvold Hall, CTD/WIT 2019 12 Mar 2019 - Parallelized Kalman-Filter-based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS detector - M. Masciovecchio, ACAT 2019 20 Feb 2019 - Parallelized Kalman Filter Tracking - M. Masciovecchio, CMS Tracking Mini-Workshop 05 Dec 2018 - Update on the mkFit Project - A. Reinsvold Hall, Tracking POG Meeting, CMS Week 08 Nov 2018 - Parallelized KF tracking with Matriplex & mkFit - M. Masciovecchio, CMS ECoM2X Group Meeting 10 Jul 2018 - Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events - M. Tadel, CHEP 2018 26 Jun 2018 - Update on vectorized KF track reconstruction - S. Krutelyov, CMS Trigger Studies Group Meeting, CMS Week 28 Mar 2018 - Parallelized tracking algorithms - M. Kortelainen, Joint WLCG & HSF Workshop 2018 21 Mar 2018 - Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks with Accurate Detector Geometry - M. Tadel, Connecting the Dots 2018 7 Mar 2018 - Parallelization of track reconstruction in view of Run 3 and beyond - S. Krutelyov, CMS Trigger Studies Group Meeting 6 Nov 2017 - Parallelized KF Tracking - S. Krutelyov, CMS Tracking POG Meeting 16 Oct 2017 - Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures - M. Tadel, CMS Software/Computing R&D Meeting 31 Aug 2017 - Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs - M. Lefebvre, NVIDIA Technical Working Meeting 21 Aug 2017 - Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures - D. Riley, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) 4 Apr 2017 - Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs, Part I - K. McDermott, CMS Offline/Computing R&D (Open Session at Apr2017 CMS Week) 8 Mar 2017 - Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs - M. Lefebvre, Connecting The Dots / Intelligent Trackers (CTDWIT 2017) 12 Oct 2016 - Kalman Filter Tracking on Parallel Architectures - D. Riley, 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) 21 Sep 2016 - Reconstructing Particle Trajectories in High Energy Physics with Xeon and Xeon Phi - D. Abdurachmanov, Intel Xeon Phi User Group Meeting 2016 19 May 2016 - Kalman Filter Tracking on Parallel Architectures - K. McDermott, US CMS Collaboration Meeting 9 May 2016 - Particle Track Reconstruction for the Large Hadron Collider: Progress in Many-Core Parallel Computing - S. Lantz, Scientific Computing and Numerics (SCAN) Seminar (Cornell University) 27 Apr 2016 - The GPGPU & Many Vector Core Folly ... is there hope for HEP? - M. Tadel, CERN Software Technology Forum 16 Mar 2016 - Parallelized Tracking - G. Cerati, Future Trends in Nuclear Physics Computing workshop 22 Feb 2016 - Kalman Filter Tracking on Parallel Architectures - K. McDermott, Connecting The Dots 2016 (CTD2016) workshop (Vienna) 13 Jan 2016 - Kalman Filter Tracking on Parallel Architectures - G. Cerati, FNAL Computing Techniques Seminar 17 Nov 2015 - High Performance Computing in the Manycore Era: Challenges for Applications - S. Lantz, Center for Applied and Computational Mathematics (CACM) Seminar (Rochester Institute of Technology) 13 Apr 2015 - Vectorization and Parallelization of Track Reconstruction Codes for the Large Hadron Collider - P. Wittich and S. Lantz, Scientific Computing and Numerics (SCAN) Seminar (Cornell University) 13 Apr 2015 - Kalman Filter Tracking on Parallel Architectures - G. Cerati, 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015)","tags":"pages","url":"https://trackreco.github.io/pages/documents.html.html","loc":"https://trackreco.github.io/pages/documents.html.html"},{"title":"","text":"The Large Hadron Collider (LHC) at CERN is the highest energy collider ever constructed. It consists of two counter-circulating proton beams made to interact in four locations around a 27 kilometer ring straddling the border between Switzerland and France. It is by some measures the largest man-made scientific device on the planet. The goal of the LHC is to probe the basic building blocks of matter and their interactions. For example, in 2012, the Higgs boson was discovered by the CMS and ATLAS collaborations. The LHC collides proton beams at the center of our detectors. By measuring the energy and momentum of the escaping particles, we infer the existence of massive particles that were created in the collisions and measure the massive particles' properties based on their decay products. The determination of the trajectories of charged particles (\"tracks\") from a set of positions of energy deposits from the various layers in our detector (\"hits\") plays a key role in identifying particles and measuring their charge and momentum. This pattern recognition problem—known as \"track reconstruction\" or simply \"tracking\"—is as a whole the most computationally complex and time-consuming step in the measurement process, as well as the most sensitive to increased activity in the detector, and traditionally, the least amenable to parallelized processing. This project aims to develop fully vectorized and parallelized tracking algorithms based on the Kalman Filter for use in a collider experiment. The software will be usable with parallel architectures such as Intel Xeon processors and NVIDIA GPUs, yet maintain and extend the physics performance required for the challenges associated with the High Luminosity LHC (HL-LHC) planned for the 2020s. The project also initiated training for young researchers through the first dedicated school on tools, techniques and methods for Computational and Data Science for High Energy Physics (CoDaS-HEP) . The CoDaS-HEP school provides a broad introduction to these critical skills as well as an overview of applications in High Energy Physics. Specific topics covered at the school include: Parallel Programming, Big Data Tools and Techniques, and Machine Learning Technology and Methods, as well as a variety of practical skills. The inaugural CoDaS-HEP school took place on 10-13 July, 2017 at Princeton University. Subsequent schools took place on 23-27 July, 2018 and 22-26 July, 2019 at Princeton University. Starting in 2018, this project became integrated into IRIS-HEP , the Institute for Research and Innovation in Software for High Energy Physics. The latest status of what is now called the mkFit project can be found at the mkFit project page on the IRIS-HEP website .","tags":"pages","url":"https://trackreco.github.io/pages/.html","loc":"https://trackreco.github.io/pages/.html"},{"title":"","text":"Project Team Peter Elmer (PI) - Princeton University, Department of Physics Peter Wittich (PI) - Cornell University, Department of Physics Avi Yagil (PI) - University of California, San Diego, Department of Physics Dan Riley - Cornell University, Department of Physics Tres Reid - Cornell University, Department of Physics Steve Lantz - Cornell University, Center for Advanced Computing Slava Krutelyov - University of California San Diego, Department of Physics Mario Masciovecchio - University of California San Diego, Department of Physics Matevz Tadel - University of California San Diego, Department of Physics Leonardo Giannini - University of California San Diego, Department of Physics Bei Wang - Princeton University, Research Computing Collaborators Giuseppe Cerati - Fermi National Accelerator Laboratory, Scientific Computing Division Allie Reinsvold Hall - Fermi National Accelerator Laboratory, Scientific Computing Division Boyana Norris - University of Oregon, Department of Computer and Information Science Brian Gravelle - University of Oregon, Department of Computer and Information Science Matti Kortelainen - Fermi National Accelerator Laboratory, Scientific Computing Division Former Contributors Kevin McDermott - Cornell University, Department of Physics (Ph.D., 2019) Frank Wuerthwein (past PI) - University of California San Diego, Department of Physics Matthieu Lefebvre - Princeton University, Research Computing Ian MacNeill - University of California, San Diego, Department of Physics David Abdurachmanov - CERN Acknowledgement This project is currently supported by National Science Foundation grants OAC-1836650, PHY-1520942 and PHY-1624356. Earlier support was provided by grants PHY-1520969, PHY-1521042 and PHY-1120138. Any opinions, findings, conclusions or recommendations expressed in this material are those of the developers and do not necessarily reflect the views of the National Science Foundation.","tags":"pages","url":"https://trackreco.github.io/pages/team.html.html","loc":"https://trackreco.github.io/pages/team.html.html"}]};