Showing 1–25 of 25 results for author: Tadel, M

Line Segment Tracking: Improving the Phase 2 CMS High Level Trigger Tracking with a Novel, Hardware-Agnostic Pattern Recognition Algorithm

Authors: Emmanouil Vourliotis, Philip Chang, Peter Elmer, Yanxi Gu, Jonathan Guiang, Vyacheslav Krutelyov, Balaji Venkat Sathia Narayanan, Gavin Niendorf, Michael Reid, Mayra Silva, Andres Rios Tascon, Matevž Tadel, Peter Wittich, Avraham Yagil

Abstract: Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future, projections for the High Luminosity LHC (HL-LHC) indicate a superlinear growth for required computing resources for single-threaded CPU algorithms that surpass the computing resources that are expected to be available.… ▽ More Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future, projections for the High Luminosity LHC (HL-LHC) indicate a superlinear growth for required computing resources for single-threaded CPU algorithms that surpass the computing resources that are expected to be available. The combination of these facts creates the need for efficient and computationally performant pattern recognition algorithms that will be able to run in parallel and possibly on other hardware, such as GPUs, given that these become more and more available in LHC experiments and high-performance computing centres. Line Segment Tracking (LST) is a novel such algorithm which has been developed to be fully parallelizable and hardware agnostic. The latter is achieved through the usage of the Alpaka library. The LST algorithm has been tested with the CMS central software as an external package and has been used in the context of the CMS HL-LHC High Level Trigger (HLT). When employing LST for pattern recognition in the HLT tracking, the physics and timing performances are shown to improve with respect to the ones utilizing the current pattern recognition algorithms. The latest results on the usage of the LST algorithm within the CMS HL-LHC HLT are presented, along with prospects for further improvements of the algorithm and its CMS central software integration. △ Less

Submitted 25 July, 2024; originally announced July 2024.

Report number: CMS-CR-2024-141

Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC

Authors: Jonathan Guiang, Slava Krutelyov, Manos Vourliotis, Yanxi Gu, Avi Yagil, Balaji Venkat Sathia Narayanan, Matevz Tadel, Philip Chang, Mayra Silva, Gavin Niendorf, Peter Wittich, Tres Reid, Peter Elmer

Abstract: In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC, towards the High Luminosity LHC (HL-LHC… ▽ More In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC, towards the High Luminosity LHC (HL-LHC) upgrade. Moreover, we have already shown excellent efficiency and performance results as we iteratively improve LST, leveraging a full simulation of the CMS detector. At the same time, promising deep-learning-based tracking algorithms, such as Graph Neural Networks (GNNs), are being pioneered on the simplified TrackML dataset. These results suggest that parts of LST could be improved or replaced by ML. Thus, a thorough, step-by-step investigation of exactly how and where ML can be utilized, while still meeting realistic HL-LHC performance and efficiency constraints, is implemented as follows. First, a lightweight neural network is used to replace and improve upon explicitly defined track quality selections. This neural network is shown to be highly efficient and robust to displaced tracks while having little-to-no impact on the runtime of LST. These results clearly establish that ML can be used to improve LST without penalty. Next, exploratory studies of GNN track-building algorithms are described. In particular, low-level track objects from LST are considered as nodes in a graph, where edges represent higher-level objects or even entire track candidates. Then, an edge-classifier GNN is trained, and the efficiency of the resultant edge scores is compared with that of the existing LST track quality selections. These GNN studies provide insights into the practicality and performance of using more ambitious and complex ML algorithms for HL-LHC tracking at the CMS Experiment. △ Less

Submitted 19 March, 2024; originally announced March 2024.

Comments: Connecting the Dots Workshop (CTD 2023)

RenderCore -- a new WebGPU-based rendering engine for ROOT-EVE

Authors: Ciril Bohak, Dmytro Kovalskyi, Sergey Linev, Alja Mrak Tadel, Sebastien Strban, Matevz Tadel, Avi Yagil

Abstract: ROOT-Eve (REve), the new generation of the ROOT event-display module, uses a web server-client model to guarantee exact data translation from the experiments' data analysis frameworks to users' browsers. Data is then displayed in various views, including high-precision 2D and 3D graphics views, currently driven by THREE.js rendering engine based on WebGL technology. RenderCore, a computer graphics… ▽ More ROOT-Eve (REve), the new generation of the ROOT event-display module, uses a web server-client model to guarantee exact data translation from the experiments' data analysis frameworks to users' browsers. Data is then displayed in various views, including high-precision 2D and 3D graphics views, currently driven by THREE.js rendering engine based on WebGL technology. RenderCore, a computer graphics research-oriented rendering engine, has been integrated into REve to optimize rendering performance and enable the use of state-of-the-art techniques for object highlighting and object selection. It also allowed for the implementation of optimized instanced rendering through the usage of custom shaders and rendering pipeline modifications. To further the impact of this investment and ensure the long-term viability of REve, RenderCore is being refactored on top of WebGPU, the next-generation GPU interface for browsers that supports compute shaders, storage textures and introduces significant improvements in GPU utilization. This has led to optimization of interchange data formats, decreased server-client traffic, and improved offloading of data visualization algorithms to the GPU. FireworksWeb, a physics analysis-oriented event display of the CMS experiment, is used to demonstrate the results, focusing on high-granularity calorimeters and targeting high data-volume events of heavy-ion collisions and High-Luminosity LHC. The next steps and directions are also discussed. △ Less

Submitted 18 December, 2023; originally announced December 2023.

Generalizing mkFit and its Application to HL-LHC

Authors: Giuseppe Cerati, Peter Elmer, Patrick Gartung, Leonardo Giannini, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Mario Masciovecchio, Tres Reid, Allison Reinsvold Hall, Daniel Riley, Matevz Tadel, Emmanouil Vourliotis, Peter Wittich, Avi Yagil

Abstract: mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hi… ▽ More mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain. △ Less

Submitted 18 December, 2023; originally announced December 2023.

Speeding up the CMS track reconstruction with a parallelized and vectorized Kalman-filter-based algorithm during the LHC Run 3

Authors: Sophie Berkman, Giuseppe Cerati, Peter Elmer, Patrick Gartung, Leonardo Giannini, Brian Gravelle, Allison R. Hall, Matti Kortelainen, Vyacheslav Krutelyov, Steve R. Lantz, Mario Masciovecchio, Kevin McDermott, Boyana Norris, Michael Reid, Daniel S. Riley, Matevž Tadel, Emmanouil Vourliotis, Bei Wang, Peter Wittich, Avraham Yagil

Abstract: One of the most challenging computational problems in the Run 3 of the Large Hadron Collider (LHC) and more so in the High-Luminosity LHC (HL-LHC) is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods used so far at the LHC and in particular at the CMS experiment are based on the Kalman filter technique. Such methods have shown to be robust and to p… ▽ More One of the most challenging computational problems in the Run 3 of the Large Hadron Collider (LHC) and more so in the High-Luminosity LHC (HL-LHC) is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods used so far at the LHC and in particular at the CMS experiment are based on the Kalman filter technique. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD architectures. This adapted Kalman-filter-based software, called "mkFit", was shown to provide a significant speedup compared to the traditional algorithm, thanks to its parallelized and vectorized implementation. The mkFit software was recently integrated into the offline CMS software framework, in view of its exploitation during the Run 3 of the LHC. At the start of the LHC Run 3, mkFit will be used for track finding in a subset of the CMS offline track reconstruction iterations, allowing for significant improvements over the existing framework in terms of computational performance, while retaining comparable physics performance. The performance of the CMS track reconstruction using mkFit at the start of the LHC Run 3 is presented, together with prospects of further improvement in the upcoming years of data taking. △ Less

Submitted 12 April, 2023; originally announced April 2023.

Comments: Contribution to the ACAT 2022

Segment Linking: A Highly Parallelizable Track Reconstruction Algorithm for HL-LHC

Authors: Philip Chang, Peter Elmer, Yanxi Gu, Vyacheslav Krutelyov, Gavin Niendorf, Michael Reid, Balaji Venkat Sathia Narayanan, Matevž Tadel, Emmanouil Vourliotis, Bei Wang, Peter Wittich, Avraham Yagil

Abstract: The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by th… ▽ More The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms. △ Less

Submitted 27 September, 2022; originally announced September 2022.

Comments: Contribution to the HEP 2022 - 39th Conference on Recent Developments in High Energy Physics and Cosmology, 15-18 June 2022, Thessaloniki, Greece

Journal ref: 2022 J. Phys.: Conf. Ser. 2375 012005

Line Segment Tracking in the HL-LHC

Authors: Gavin Niendorf, Tres Reid, Peter Wittich, Peter Elmer, Bei Wang, Philip Chang, Yanxi Gu, Vyacheslav Krutelyov, Balaji Venkat Sathia Narayanan, Matevz Tadel, Emmanouil Vourliotis, Avi Yagil

Abstract: The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, Line Segment Tracking takes a fundamentally di… ▽ More The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, Line Segment Tracking takes a fundamentally different approach by doing a bottom-up reconstruction of tracks. Small track stubs from adjoining detector regions are constructed, and then these track stubs that are consistent with typical track trajectories are successively linked. Since the production of these track stubs is localized, they can be made in parallel, which lends way into using architectures like GPUs and multi-CPUs to take advantage of the parallelism. The algorithm is implemented in the context of the CMS Phase-2 Tracker and runs on NVIDIA Tesla V100 GPUs. Good physics and timing performance has been obtained, and stepping stones for the future are elaborated. △ Less

Submitted 28 September, 2022; v1 submitted 17 July, 2022; originally announced July 2022.

Comments: Connecting the Dots Workshop (CTD 2022) v2 : Implemented referee comments from internal review

HL-LHC Analysis With ROOT

Authors: Axel Naumann, Philippe Canal, Enric Tejedor, Enrico Guiraud, Lorenzo Moneta, Bertrand Bellenot, Olivier Couet, Alja Mrak Tadel, Matevz Tadel, Sergey Linev, Javier Lopez Gomez, Jonas Rembser, Vincenzo Eduardo Padulano, Jakob Blomer, Jonas Hahnfeld, Bernhard Manfred Gruber, Vassil Vassilev

Abstract: ROOT is high energy physics' software for storing and mining data in a statistically sound way, to publish results with scientific graphics. It is evolving since 25 years, now providing the storage format for more than one exabyte of data; virtually all high energy physics experiments use ROOT. With another significant increase in the amount of data to be handled scheduled to arrive in 2027, ROOT… ▽ More ROOT is high energy physics' software for storing and mining data in a statistically sound way, to publish results with scientific graphics. It is evolving since 25 years, now providing the storage format for more than one exabyte of data; virtually all high energy physics experiments use ROOT. With another significant increase in the amount of data to be handled scheduled to arrive in 2027, ROOT is preparing for a massive upgrade of its core ingredients. As part of a review of crucial software for high energy physics, the ROOT team has documented its R&D plans for the coming years. △ Less

Submitted 12 May, 2022; originally announced May 2022.

Report number: FERMILAB-TM-2774-SCD

ROOT for the HL-LHC: data format

Authors: Axel Naumann, Philippe Canal, Enric Tejedor, Enrico Guiraud, Lorenzo Moneta, Bertrand Bellenot, Olivier Couet, Alja Mrak Tadel, Matevz Tadel, Sergey Linev, Javier Lopez Gomez, Jonas Rembser, Vincenzo Eduardo Padulano, Jakob Blomer, Jonas Hahnfeld, Bernhard Manfred Gruber, Vassil Vassilev

Abstract: This document discusses the state, roadmap, and risks of the foundational components of ROOT with respect to the experiments at the HL-LHC (Run 4 and beyond). As foundational components, the document considers in particular the ROOT input/output (I/O) subsystem. The current HEP I/O is based on the TFile container file format and the TTree binary event data format. The work going into the new RNTup… ▽ More This document discusses the state, roadmap, and risks of the foundational components of ROOT with respect to the experiments at the HL-LHC (Run 4 and beyond). As foundational components, the document considers in particular the ROOT input/output (I/O) subsystem. The current HEP I/O is based on the TFile container file format and the TTree binary event data format. The work going into the new RNTuple event data format aims at superseding TTree, to make RNTuple the production ROOT event data I/O that meets the requirements of Run 4 and beyond. △ Less

Submitted 9 April, 2022; originally announced April 2022.

Report number: FERMILAB-FN-1165-SCD

Speeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm

Authors: Steven Lantz, Kevin McDermott, Michael Reid, Daniel Riley, Peter Wittich, Sophie Berkman, Giuseppe Cerati, Matti Kortelainen, Allison Reinsvold Hall, Peter Elmer, Bei Wang, Leonardo Giannini, Vyacheslav Krutelyov, Mario Masciovecchio, Matevž Tadel, Frank Würthwein, Avraham Yagil, Brian Gravelle, Boyana Norris

Abstract: One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster comp… ▽ More One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as mkFit. A key piece of the algorithm is the Matriplex library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the mkFit algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading. mkFit achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework. △ Less

Submitted 10 July, 2020; v1 submitted 29 May, 2020; originally announced June 2020.

Software Challenges For HL-LHC Data Analysis

Authors: ROOT Team, Kim Albertsson Brann, Guilherme Amadio, Sitong An, Bertrand Bellenot, Jakob Blomer, Philippe Canal, Olivier Couet, Massimiliano Galli, Enrico Guiraud, Stephan Hageboeck, Sergey Linev, Pere Mato Vila, Lorenzo Moneta, Axel Naumann, Alja Mrak Tadel, Vincenzo Eduardo Padulano, Fons Rademakers, Oksana Shadura, Matevz Tadel, Enric Tejedor Saavedra, Xavier Valls Pla, Vassil Vassilev, Stefan Wunsch

Abstract: The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the central software players in high energy physics since decades. From its experience and expectations, the ROOT team has distilled a comprehensive set of areas that should see research and development in the context of data ana… ▽ More The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the central software players in high energy physics since decades. From its experience and expectations, the ROOT team has distilled a comprehensive set of areas that should see research and development in the context of data analysis software, for making best use of HL-LHC's physics potential. This work shows what these areas could be, why the ROOT team believes investing in them is needed, which gains are expected, and where related work is ongoing. It can serve as an indication for future research proposals and cooperations. △ Less

Submitted 4 May, 2020; v1 submitted 16 April, 2020; originally announced April 2020.

Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm

Authors: Giuseppe Cerati, Peter Elmer, Brian Gravelle, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Mario Masciovecchio, Kevin McDermott, Boyana Norris, Allison Reinsvold Hall, Michael Reid, Daniel Riley, Matevž Tadel, Peter Wittich, Bei Wang, Frank Würthwein, Avraham Yagil

Abstract: One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational th… ▽ More One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our mkFit software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software. Recently we have improved the overall efficiency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and offline reconstruction for the HL-LHC CMS tracker. △ Less

Submitted 9 July, 2020; v1 submitted 14 February, 2020; originally announced February 2020.

Report number: FERMILAB-CONF-20-075-SCD

Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm

Authors: Giuseppe Cerati, Peter Elmer, Brian Gravelle, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Mario Masciovecchio, Kevin McDermott, Boyana Norris, Michael Reid, Allison Reinsvold Hall, Daniel Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Abstract: Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup expected from the High-Luminosity LHC, the computational challenge of track finding and fitting requires novel solutions. The current track reconstruction algorithms used at the LHC are based on Kalman filter methods tha… ▽ More Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup expected from the High-Luminosity LHC, the computational challenge of track finding and fitting requires novel solutions. The current track reconstruction algorithms used at the LHC are based on Kalman filter methods that achieve good physics performance. By adapting the Kalman filter techniques for use on many-core SIMD architectures such as the Intel Xeon and Intel Xeon Phi and (to a limited degree) NVIDIA GPUs, we are able to obtain significant speedups and comparable physics performance. New optimizations, including a dedicated post-processing step to remove duplicate tracks, have improved the algorithm's performance even further. Here we report on the current structure and performance of the code and future plans for the algorithm. △ Less

Submitted 6 November, 2019; v1 submitted 27 June, 2019; originally announced June 2019.

Comments: Submitted to proceedings of the 2019 Connecting the Dots and Workshop on Intelligent Trackers (CTD/WIT 2019); 6 pages, 4 figures

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector

Authors: Giuseppe Cerati, Peter Elmer, Brian Gravelle, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Mario Masciovecchio, Kevin McDermott, Boyana Norris, Allison Reinsvold Hall, Daniel Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Abstract: In the High-Luminosity Large Hadron Collider (HL-LHC), one of the most challenging computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods currently in use at the LHC are based on the Kalman filter. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve… ▽ More In the High-Luminosity Large Hadron Collider (HL-LHC), one of the most challenging computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods currently in use at the LHC are based on the Kalman filter. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD and SIMT architectures. Our adapted Kalman-filter-based software has obtained significant parallel speedups using such processors, e.g., Intel Xeon Phi, Intel Xeon SP (Scalable Processors) and (to a limited degree) NVIDIA GPUs. Recently, an effort has started towards the integration of our software into the CMS software framework, in view of its exploitation for the Run III of the LHC. Prior reports have shown that our software allows in fact for some significant improvements over the existing framework in terms of computational performance with comparable physics performance, even when applied to realistic detector configurations and event complexity. Here, we demonstrate that in such conditions physics performance can be further improved with respect to our prior reports, while retaining the improvements in computational performance, by making use of the knowledge of the detector and its geometry. △ Less

Submitted 5 June, 2019; originally announced June 2019.

Comments: Submitted to proceedings of 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019); 6 pages, 5 figures

HEP Software Foundation Community White Paper Working Group --- Visualization

Authors: Matthew Bellis, Riccardo Maria Bianchi, Sebastien Binet, Ciril Bohak, Benjamin Couturier, Hadrien Grasland, Oliver Gutsche, Sergey Linev, Alex Martyniuk, Thomas McCauley, Edward Moyse, Alja Mrak Tadel, Mark Neubauer, Jeremi Niedziela, Leo Piilonen, Jim Pivarski, Martin Ritter, Tai Sakuma, Matevz Tadel, Barthélémy von Haller, Ilija Vukotic, Ben Waugh

Abstract: In modern High Energy Physics (HEP) experiments visualization of experimental data has a key role in many activities and tasks across the whole data chain: from detector development to monitoring, from event generation to reconstruction of physics objects, from detector simulation to data analysis, and all the way to outreach and education. In this paper, the definition, status, and evolution of d… ▽ More In modern High Energy Physics (HEP) experiments visualization of experimental data has a key role in many activities and tasks across the whole data chain: from detector development to monitoring, from event generation to reconstruction of physics objects, from detector simulation to data analysis, and all the way to outreach and education. In this paper, the definition, status, and evolution of data visualization for HEP experiments will be presented. Suggestions for the upgrade of data visualization tools and techniques in current experiments will be outlined, along with guidelines for future experiments. This paper expands on the summary content published in the HSF \emph{Roadmap} Community White Paper~\cite{HSF-CWP-2017-01} △ Less

Submitted 26 November, 2018; originally announced November 2018.

Report number: HSF-CWP-2017-15

Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events

Authors: Giuseppe Cerati, Peter Elmer, Brian Gravelle, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Matthieu Lefebvre, Mario Masciovecchio, Kevin McDermott, Boyana Norris, Allison Reinsvold Hall, Daniel Riley, Matevz Tadel, Peter Wittich, Frank Wuerthwein, Avi Yagil

Abstract: The High-Luminosity Large Hadron Collider at CERN will be characterized by greater pileup of events and higher occupancy, making the track reconstruction even more computationally demanding. Existing algorithms at the LHC are based on Kalman filter techniques with proven excellent physics performance under a variety of conditions. Starting in 2014, we have been developing Kalman-filter-based metho… ▽ More The High-Luminosity Large Hadron Collider at CERN will be characterized by greater pileup of events and higher occupancy, making the track reconstruction even more computationally demanding. Existing algorithms at the LHC are based on Kalman filter techniques with proven excellent physics performance under a variety of conditions. Starting in 2014, we have been developing Kalman-filter-based methods for track finding and fitting adapted for many-core SIMD processors that are becoming dominant in high-performance systems. This paper summarizes the latest extensions to our software that allow it to run on the realistic CMS-2017 tracker geometry using CMSSW-generated events, including pileup. The reconstructed tracks can be validated against either the CMSSW simulation that generated the hits, or the CMSSW reconstruction of the tracks. In general, the code's computational performance has continued to improve while the above capabilities were being added. We demonstrate that the present Kalman filter implementation is able to reconstruct events with comparable physics performance to CMSSW, while providing generally better computational performance. Further plans for advancing the software are discussed. △ Less

Submitted 9 July, 2019; v1 submitted 9 November, 2018; originally announced November 2018.

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures

Authors: Giuseppe Cerati, Peter Elmer, Slava Krutelyov, Steven Lantz, Matthieu Lefebvre, Mario Masciovecchio, Kevin McDermott, Daniel Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Abstract: Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expr… ▽ More Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity. △ Less

Submitted 27 March, 2018; v1 submitted 16 November, 2017; originally announced November 2017.

Comments: Accepted to the Proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research; 6 pages, 5 figures. arXiv admin note: text overlap with arXiv:1702.06359

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

Authors: Giuseppe Cerati, Peter Elmer, Slava Krutelyov, Steven Lantz, Matthieu Lefebvre, Mario Masciovecchio, Kevin McDermott, Daniel Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Abstract: For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. How… ▽ More For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem in the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offline. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port Kalman filter to NVIDIA GPUs. △ Less

Submitted 19 June, 2017; v1 submitted 8 May, 2017; originally announced May 2017.

Comments: Submitted to proceedings of Connecting The Dots 2017 (CTD2017), Orsay. arXiv admin note: substantial text overlap with arXiv:1605.05508

Kalman filter tracking on parallel architectures

Authors: Giuseppe Cerati, Peter Elmer, Slava Krutelyov, Steven Lantz, Matthieu Lefebvre, Kevin McDermott, Daniel Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

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. Such parallel performance is becoming a criti… ▽ More 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. Such parallel performance is becoming a critical requirement for methods to reconstruct the tracks of charged particles at the Large Hadron Collider and, in the future, at the High Luminosity LHC. This is because the steady increase in luminosity is causing an exponential growth in the overall event reconstruction time, and tracking is by far the most demanding task for both online and offline processing. Many past and present collider experiments adopted Kalman filter-based algorithms for tracking because of their robustness and their excellent physics performance, especially for solid state detectors where material interactions play a significant role. We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs. △ Less

Submitted 21 November, 2017; v1 submitted 21 February, 2017; originally announced February 2017.

Comments: Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016; 8 pages, 9 figures

Journal ref: G Cerati et al 2017 J. Phys.: Conf. Ser. 898 042051

Kalman Filter Tracking on Parallel Architectures

Authors: Giuseppe Cerati, Peter Elmer, Slava Krutelyov, Steven Lantz, Matthieu Lefebvre, Kevin McDermott, Daniel Riley, Matevz Tadel, Peter Wittich, Frank Wuerthwein, Avi Yagil

Abstract: 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 such as GPGPU, ARM and Intel MIC. 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 specia… ▽ More 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 such as GPGPU, ARM and Intel MIC. 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. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-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 such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are 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 in use today at the LHC. We report on porting these algorithms to new parallel architectures. Our previous investigations showed that, using optimized data structures, track fitting with a Kalman Filter can achieve large speedups both with Intel Xeon and Xeon Phi. Additionally, we have previously shown first attempts at track building with some speedup. We report here our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment. △ Less

Submitted 18 May, 2016; originally announced May 2016.

Comments: Submitted to proceedings of Connecting The Dots 2016 (CTD2016), Vienna. arXiv admin note: text overlap with arXiv:1601.08245

Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders

Authors: Giuseppe Cerati, Peter Elmer, Steven Lantz, Kevin McDermott, Dan Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Abstract: 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 such as GPGPU, ARM and Intel MIC. 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 specia… ▽ More 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 such as GPGPU, ARM and Intel MIC. 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. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-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 such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are 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 in use today at the LHC. We report on porting these algorithms to new parallel architectures. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedups both with Intel Xeon and Xeon Phi. We report here our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic experimental environment. △ Less

Submitted 29 January, 2016; originally announced January 2016.

Comments: Proceedings of the 2015 IEEE NSS/MIC Conference, San Diego, CA

ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

Authors: Ilka Antcheva, Maarten Ballintijn, Bertrand Bellenot, Marek Biskup, Rene Brun, Nenad Buncic, Philippe Canal, Diego Casadei, Olivier Couet, Valery Fine, Leandro Franco, Gerardo Ganis, Andrei Gheata, David Gonzalez Maline, Masaharu Goto, Jan Iwaszkiewicz, Anna Kreshuk, Diego Marcos Segura, Richard Maunder, Lorenzo Moneta, Axel Naumann, Eddy Offermann, Valeriy Onuchin, Suzanne Panacek, Fons Rademakers , et al. (2 additional authors not shown)

Abstract: ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical… ▽ More ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way. △ Less

Submitted 31 August, 2015; originally announced August 2015.

Journal ref: Computer Physics Communications Volume 180, Issue 12, December 2009, Pages 2499-2512

Any Data, Any Time, Anywhere: Global Data Access for Science

Authors: Kenneth Bloom, Tommaso Boccali, Brian Bockelman, Daniel Bradley, Sridhara Dasu, Jeff Dost, Federica Fanzago, Igor Sfiligoi, Alja Mrak Tadel, Matevz Tadel, Carl Vuosalo, Frank Würthwein, Avi Yagil, Marian Zvada

Abstract: Data access is key to science driven by distributed high-throughput computing (DHTC), an essential technology for many major research projects such as High Energy Physics (HEP) experiments. However, achieving efficient data access becomes quite difficult when many independent storage sites are involved because users are burdened with learning the intricacies of accessing each system and keeping ca… ▽ More Data access is key to science driven by distributed high-throughput computing (DHTC), an essential technology for many major research projects such as High Energy Physics (HEP) experiments. However, achieving efficient data access becomes quite difficult when many independent storage sites are involved because users are burdened with learning the intricacies of accessing each system and keeping careful track of data location. We present an alternate approach: the Any Data, Any Time, Anywhere infrastructure. Combining several existing software products, AAA presents a global, unified view of storage systems - a "data federation," a global filesystem for software delivery, and a workflow management system. We present how one HEP experiment, the Compact Muon Solenoid (CMS), is utilizing the AAA infrastructure and some simple performance metrics. △ Less

Submitted 6 August, 2015; originally announced August 2015.

Comments: 9 pages, 6 figures, submitted to 2nd IEEE/ACM International Symposium on Big Data Computing (BDC) 2015

Kalman Filter Tracking on Parallel Architectures

Authors: Giuseppe Cerati, Peter Elmer, Steven Lantz, Kevin McDermott, Dan Riley, Matevž Tadel, Peter Wittich, Frank Würthwein, Avi Yagil

Abstract: 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 lightweig… ▽ More 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. 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. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedup both with Intel Xeon and Xeon Phi. We report here our further progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic simulation setup. △ Less

Submitted 18 May, 2015; originally announced May 2015.

Traditional Tracking with Kalman Filter on Parallel Architectures

Authors: Giuseppe Cerati, Peter Elmer, Steven Lantz, Ian MacNeill, Kevin McDermott, Dan Riley, Matevz Tadel, Peter Wittich, Frank Wuerthwein, Avi Yagil

Abstract: 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 lightwei… ▽ More 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 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. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware. △ Less

Submitted 29 September, 2014; originally announced September 2014.

Comments: Submitted to proceedings of 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT 2014), Prague