We present a new approach to searching for Continuous gravitational Waves (CWs) emitted by isolated rotating neutron stars, using the high parallel computing efficiency and computational power of modern Graphic Processing Units (GPUs). Specifically, in this paper the porting of one of the algorithms used to search for CW signals, the so-called FrequencyHough transform, on the TensorFlow framework, is described. The new code has been fully tested and its performance on GPUs has been compared to those in a CPU multicore system of the same class, showing a factor of 10 speed-up. This demonstrates that GPU programming with general purpose libraries (the those of the TensorFlow framework) of a high-level programming language can provide a significant improvement of the performance of data analysis, opening new perspectives on wide-parameter searches for CWs.
Continuous gravitational-wave data analysis with general purpose computing on graphic processing units / La Rosa, I.; Astone, P.; D'antonio, S.; Frasca, S.; Leaci, P.; Miller, A. L.; Palomba, C.; Piccinni, O. J.; Pierini, L.; Regimbau, T.. - In: UNIVERSE. - ISSN 2218-1997. - 7:7(2021), p. 218. [10.3390/universe7070218]
Continuous gravitational-wave data analysis with general purpose computing on graphic processing units
La Rosa I.;Astone P.;Frasca S.;Leaci P.;Miller A. L.;Palomba C.;Piccinni O. J.;Pierini L.;
2021
Abstract
We present a new approach to searching for Continuous gravitational Waves (CWs) emitted by isolated rotating neutron stars, using the high parallel computing efficiency and computational power of modern Graphic Processing Units (GPUs). Specifically, in this paper the porting of one of the algorithms used to search for CW signals, the so-called FrequencyHough transform, on the TensorFlow framework, is described. The new code has been fully tested and its performance on GPUs has been compared to those in a CPU multicore system of the same class, showing a factor of 10 speed-up. This demonstrates that GPU programming with general purpose libraries (the those of the TensorFlow framework) of a high-level programming language can provide a significant improvement of the performance of data analysis, opening new perspectives on wide-parameter searches for CWs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
