Abstract
Spectroscopy devices suffer from the pulse pileup phenomenon, caused by overlapping of the signals. The energy-domain based pileup correction algorithm estimates the pulse energy distribution by measuring the duration and energies of pileups directly and does not need to identify each individual pulses. The correction algorithm can efficiently recovers the energy spectrum even under a very high photon arrival rate. However, the correction algorithm is sequential in nature and is slow when the energy resolution is high. A fast parallel implementation of the original correction algorithm is proposed in this paper. The parallel counterpart leverages state-of-the-art many-core system technology and achieves a nearly linear acceleration when the problem size scales. The speedup ratio exceeds 1,000 when the energy spectrum is split into 2,048 bins.
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References
Mclean, C., Pauley, M., Manton, J.H.: Non-parametric decompounding of pulse pile-up under gaussian noise with finite data sets. IEEE Trans Signal Process 68, 2114–2127 (2020)
Bolic, M., Drndarevic, V., Gueaieb, W.: Pileup correction algorithms for very-high-count-rate gamma-ray spectrometry with Nai(Tl) detectors. IEEE Trans. Instru. Meas. 59(1), 122–130 (2010)
McLean, C., Pauley, M., Manton, J.H,: Limitations of decision based pile-up correction algorithms. In: IEEE Workshop SSP, 2018, pp. 693–697 (2018)
Trigano, T., Dautremer, T., et al.: Pile-up correction algorithms for nuclear spectrometry. In: IEEE ICASSP, vol. 4, pp. iv/441-iv/444 Vol. 4 (2005)
Trigano, T., Barat, E., et al.: Fast digital filtering of spectrometric data for pile-up correction. IEEE Signal Proc. Lett. 22(7), 973–977 (2015)
Trigano, T., Souloumiac, A., Montagu, T., et al.: Statistical pileup correction method for HPGe detectors. IEEE Trans. Signal Process. 55(10), 4871–4881 (2007)
Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. In: ACM SAC, pp. 1637–1641 (2009)
Li, J., Ming, Z., et al.: Resource allocation robustness in multi-core embedded systems with inaccurate information. J. Syst. Architect. 57(9), 840–849 (2011)
Qiu, M., Jia, Z., et al..: Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP. J. VLSI Signal Process. Syst. Signal Image Video Technol. 46, 55–73 (2007)
Qiu, M., Yang, L., et al.: Dynamic and leakage energy minimization with soft real-time loop scheduling and voltage assignment. IEEE Trans. Very Large Scale Integr. 18(3), 501–504 (2009)
Qiu, M., Xue, C., et al..: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE DATE, 2007, pp. 1–6 (2007)
Zhang, L., Qiu, M., et al.: Variable partitioning and scheduling for MPSoC with virtually shared scratch pad memory. J. Signal Process. Stys. 58(2), 247–265 (2018)
Hu, F., Lakdawala, S., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Info. Tech. Bio. 13(4), 656–663 (2009)
Qiu, M., Sha, E., et al.: Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP. J. Paralell Distrib. Comput. 68(4), 443–455 (2008)
Shao, Z., Wang, M., et al.: Real-time dynamic voltage loop scheduling for multi-core embedded systems. IEEE Trans. Cir. Sys. II 54(5), 445–449 (2007)
Gai, K., et al.: Electronic health record error prevention approach using ontology in big data. In: IEEE 17th HPCC (2015)
Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. In: IEEE Trans. Intell. Transp. Syst. 22. 4560–4569 (2020)
Gai, K., Qiu, M., et al.: In-memory big data analytics under space constraints using dynamic programming. Fut. Gen. Ccomput. Syst. 83, 219–227 (2018)
Wu, G., Zhang, H., et al.: A decentralized approach for mining event correlations in distributed system monitoring. J. Paralell Distrib. Comput. 73(3), 330–340 (2013)
Qiu, M., Khisamutdinov, E., et al.: RNA nanotechnology for computer design and in vivo computation. Philoso. Trans. Ser. A 371(2000), 20120310 (2013)
Kong, X., Zheng, X., Zhu, Y., et al.: Custom computing design and implementation for multiple dedispersion with GPU. In: CSCloud/EdgeCom, pp. 103–108 (2021)
Huang, Y., Zheng, X., Zhu, Y., et al..: CPU-GPU collaborative acceleration of bulletproofs-a zero-knowledge proof algorithm. In: IEEE ISPA/BDCloud/SocialCom/SustainCom, 2021, pp. 674–680 (2021)
Qiu, M., et al.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE DATE, 2007, pp. 1–6 (2007)
Acknowledgment
This work was supported partially by the National SKA Program of China (Grant No. 2020SKA0120202), the National Natural Science Foundation of China (Grant No. U2032125), the Science and Technology Commission of Shanghai Municipality (Grant No. 21511101400), and Shanghai Talent Development Fund (Grant No. E1322E1). Sincere gratitude to all the people who helped me during this period.
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Chen, Z., Kong, X., Zheng, X., Zhu, Y., Trigano, T. (2023). Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_24
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