The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Peter Wittich - One of the best experts on this subject based on the ideXlab platform.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures
Journal of Physics: Conference Series, 2018Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, Kevin Mcdermott, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
arXiv: Computational Physics, 2017Co-Authors: G B Cerati, Peter Elmer, Steven Lantz, M Masciovecchio, K Mcdermott, Slava Krutelyov, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
European Physical Journal Web of Conferences, 2017Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, K Mcdermott, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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 Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. 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 at 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 offine. 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 the Kalman filter to NVIDIA GPUs.
G B Cerati - One of the best experts on this subject based on the ideXlab platform.
-
parallelizing the unpacking and clustering of detector data for reconstruction of charged Particle Tracks on multi core cpus and many core gpus
arXiv: High Energy Physics - Experiment, 2021Co-Authors: G B Cerati, Peter Elmer, Brian Gravelle, Matti J Kortelainen, V Krutelyov, Steven Lantz, M Masciovecchio, K Mcdermott, Boyana Norris, Allison Reinsvold HallAbstract:We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged Particle Tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures with the cms detector
arXiv: Instrumentation and Detectors, 2020Co-Authors: G B Cerati, Peter Elmer, Brian Gravelle, Matti J Kortelainen, M Masciovecchio, K Mcdermott, Boyana Norris, Steven R Lantz, Reinsvold A Hall, D RileyAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures
Journal of Physics: Conference Series, 2018Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, Kevin Mcdermott, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
arXiv: Computational Physics, 2017Co-Authors: G B Cerati, Peter Elmer, Steven Lantz, M Masciovecchio, K Mcdermott, Slava Krutelyov, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
European Physical Journal Web of Conferences, 2017Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, K Mcdermott, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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 Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. 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 at 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 offine. 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 the Kalman filter to NVIDIA GPUs.
Jaroslava Schovancova - One of the best experts on this subject based on the ideXlab platform.
-
performance of missing transverse momentum reconstruction with the atlas detector using proton proton collisions at s 13 tev
European Physical Journal C, 2018Co-Authors: M Aaboud, Alexander Kupco, P Davison, Samuel Webb, Joey Huston, K Jakobs, M Spousta, M Cobal, T Agatonovicjovin, Jaroslava SchovancovaAbstract:The performance of the missing transverse momentum ( ETmiss ) reconstruction with the ATLAS detector is evaluated using data collected in proton-proton collisions at the LHC at a centre-of-mass energy of 13 TeV in 2015. To reconstruct ETmiss , fully calibrated electrons, muons, photons, hadronically decaying τ-leptons , and jets reconstructed from calorimeter energy deposits and charged-Particle Tracks are used. These are combined with the soft hadronic activity measured by reconstructed charged-Particle Tracks not associated with the hard objects. Possible double counting of contributions from reconstructed charged-Particle Tracks from the inner detector, energy deposits in the calorimeter, and reconstructed muons from the muon spectrometer is avoided by applying a signal ambiguity resolution procedure which rejects already used signals when combining the various ETmiss contributions. The individual terms as well as the overall reconstructed ETmiss are evaluated with various performance metrics for scale (linearity), resolution, and sensitivity to the data-taking conditions. The method developed to determine the systematic uncertainties of the ETmiss scale and resolution is discussed. Results are shown based on the full 2015 data sample corresponding to an integrated luminosity of 3.2fb-1 .
D Riley - One of the best experts on this subject based on the ideXlab platform.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures with the cms detector
arXiv: Instrumentation and Detectors, 2020Co-Authors: G B Cerati, Peter Elmer, Brian Gravelle, Matti J Kortelainen, M Masciovecchio, K Mcdermott, Boyana Norris, Steven R Lantz, Reinsvold A Hall, D RileyAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures
Journal of Physics: Conference Series, 2018Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, Kevin Mcdermott, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
arXiv: Computational Physics, 2017Co-Authors: G B Cerati, Peter Elmer, Steven Lantz, M Masciovecchio, K Mcdermott, Slava Krutelyov, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
European Physical Journal Web of Conferences, 2017Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, K Mcdermott, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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 Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. 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 at 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 offine. 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 the Kalman filter to NVIDIA GPUs.
M Masciovecchio - One of the best experts on this subject based on the ideXlab platform.
-
parallelizing the unpacking and clustering of detector data for reconstruction of charged Particle Tracks on multi core cpus and many core gpus
arXiv: High Energy Physics - Experiment, 2021Co-Authors: G B Cerati, Peter Elmer, Brian Gravelle, Matti J Kortelainen, V Krutelyov, Steven Lantz, M Masciovecchio, K Mcdermott, Boyana Norris, Allison Reinsvold HallAbstract:We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged Particle Tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures with the cms detector
arXiv: Instrumentation and Detectors, 2020Co-Authors: G B Cerati, Peter Elmer, Brian Gravelle, Matti J Kortelainen, M Masciovecchio, K Mcdermott, Boyana Norris, Steven R Lantz, Reinsvold A Hall, D RileyAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core architectures
Journal of Physics: Conference Series, 2018Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, Kevin Mcdermott, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
arXiv: Computational Physics, 2017Co-Authors: G B Cerati, Peter Elmer, Steven Lantz, M Masciovecchio, K Mcdermott, Slava Krutelyov, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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.
-
parallelized kalman filter based reconstruction of Particle Tracks on many core processors and gpus
European Physical Journal Web of Conferences, 2017Co-Authors: G B Cerati, Peter Elmer, M Masciovecchio, K Mcdermott, Slava Krutelyov, Steven R Lantz, Matthieu Lefebvre, D Riley, M Tadel, Peter WittichAbstract: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 Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. 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 at 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 offine. 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 the Kalman filter to NVIDIA GPUs.