Abstract
AbstractThe computational efficiency is critical with the increasing number of GNSS satellites and ground stations since many unknown parameters must be estimated. Although only active parameters are kept in the normal equation in sequential least square estimation, the computational cost for parameter elimination is still a heavy burden. Therefore, it is necessary to optimize the procedure of parameter elimination to enhance the computational efficiency of GNSS network solutions. An efficient parallel algorithm is developed for accelerating parameter estimation based on modern multi-core processors. In the parallel algorithm, a multi-thread guided scheduling scheme, and cache memory traffic optimizations are implemented in parallelized sub-blocks for normal-equation-level operations. Compared with the traditional serial scheme, the computational time of parameter estimations can be reduced by a factor of three due to the new parallel algorithm using a six-core processor. Our results also confirm that the architecture of computers entirely limits the performance of the parallel algorithm. All the parallel optimizations are also investigated in detail according to the characteristics of CPU architecture. This gives a good reference to architecture-oriented parallel programming in the future development of GNSS software. The performance of the multi-thread parallel algorithm is expected to improve further with the upgrade of new multi-core coprocessors.s
Funder
Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZ
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Reference33 articles.
1. Amdahl GM (1967) Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the spring joint computer conference, April 18–20, pp 483–485, https://doi.org/10.1145/1465482.1465560
2. Asanovic K, et al (2006) The landscape of parallel computing research: a view from berkeley. EECS Department, University of California, Berkeley, Technical Report No. UCB/EECS-2006-183, December 18, 2006, https://www2.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-183.pdf
3. Boomkamp H (2010) Global GPS reference frame solutions of unlimited size. Adv Space Res 46(2):136–143. https://doi.org/10.1016/j.asr.2010.02.015
4. Boomkamp H, König R (2004) Bigger, better, faster POD. In: Proceedings of IGS Workshop and symposium, 1–6 March 2004, Berne, Switzerland, 10(3): 3, ftp://192.134.134.6/pub/igs/igscb/resource/pubs/04_rtberne/cdrom/Session9/9_0_Boomkamp.pdf
5. Chen G, Herring TA (1997) Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data. J Geophys Res Solid Earth 102(B9):20489–20502. https://doi.org/10.1029/97JB01739
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献