Kalman Filter Tracking on Parallel Architectures

Cerati, G. B.; Elmer, P.; Krutelyov, S.; Lantz, Steven; Lefèbvre, Matthieu; Mcdermott, K.; Riley, D.; Tadel, M.; Wittich, P.; Würthwein, F.; Yagil, Avi

doi:10.1051/epjconf/201612700010

Cited by 14 publications

(15 citation statements)

References 11 publications

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“…Relying on this library, the initial implementation of a vectorized KF track fitting algorithm was demonstrated in a simplified detector geometry [4], followed by the initial implementation of an analogous track finding algorithm [5]. Further developments [6][7][8] allowed the software to achieve satisfactory performance, using Intel Threading Building Blocks (TBB) parallel constructs to comply with the CMS software (CMSSW) code base. A first implementation of the mkFit program on GPGPUs was also pursued [9]: while the Matriplex library was found to outperform standard small-matrix multiplication packages for GPUs, the performance of the KF-based track finding on GPGPUs is as of today not satisfactory.…”

Section: Parallelized Kalman Filter Trackingmentioning

confidence: 99%

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

Cerati

Elmer

Krutelyov

et al. 2018

J. Phys.: Conf. Ser.

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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.

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Section: Parallelized Kalman Filter Trackingmentioning

confidence: 99%

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

Cerati

Elmer

Krutelyov

et al. 2018

J. Phys.: Conf. Ser.

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“…As the hit collection is completely determined after track building, track fitting can repeatedly apply the KF algorithm without branching, making this the ideal place to start in porting KF tracking to Xeon and Xeon Phi, with our first results shown at ACAT2014 [8]. In more recent works (CHEP2016 [11], CtD2016 [12]), we discussed the improvements made to target a more realistic geometry. Profiling and recent computational optimizations were also outlined.…”

Section: Kalman Filter Trackingmentioning

confidence: 99%

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

et al. 2017

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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 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 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 the Kalman filter to NVIDIA GPUs. a

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“…The KF pattern recognition which proceeds sequentially hit by hit with possible branching on each layer is hard to parallelize effectively [9]. An algorithm that reconstructs several parts of the track independently would allow for high degree of parallelization.…”

Section: Pos(ichep2016)194mentioning

confidence: 99%

Impact of tracker layout and algorithmic choices on cost of computing at high pileup.

Krutelyov¹,

Cerati²,

Tadel³

et al. 2017

Proceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016)

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Kalman Filter Tracking on Parallel Architectures

Abstract: Abstract. Power density constraints are limiting the performance improvements of modern CPUs.

Cited by 14 publications

References 11 publications

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

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

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

Impact of tracker layout and algorithmic choices on cost of computing at high pileup.

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