Pregled bibliografske jedinice broj: 845365
Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods
Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods // LNCS - High Performance Scientific Computing / Di Napoli, E. et all. (ur.).
Aachen, Njemačka: Springer, 2017. str. 200-211 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods
Autori
Fabregat-Traver, Diego ; Davidović, Davor ; Höhnerbach, Markus ; Di Napoli, Edoardo
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
LNCS - High Performance Scientific Computing
/ Di Napoli, E. et all. - : Springer, 2017, 200-211
ISBN
978-3-319-53861-7
Skup
JARA-HPC Symposium
Mjesto i datum
Aachen, Njemačka, 04.10.2016. - 05.10.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
hybrid computing ; GPU ; FLEUR ; electronic structure
Sažetak
In this paper we focus on the integration of high-performance numerical libraries in ab initio codes and the portability of performance and scalability. The target of our work is FLEUR, a software for electronic structure calculations developed in the Forschungszentrum J\"ulich over the course of two decades. The presented work follows up on a previous effort to modernize legacy code by re-engineering and rewriting it in terms of highly optimized libraries. We illustrate how this initial effort to get efficient and portable shared-memory code enables fast porting of the code to emerging heterogeneous architectures. More specifically, we port the code to nodes equipped with multiple GPUs. We divide our study in two parts. First, we show considerable speedups attained by minor and relatively straightforward code changes to off-load parts of the computation to the GPUs. Then, we identify further possible improvements to achieve even higher performance and scalability. On a system consisting of 16-cores and 2 GPUs, we observe speedups of up to 5x with respect to our optimized shared-memory code, which in turn means between 7.5x and 12.5x speedup with respect to the original FLEUR code.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo
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Časopis indeksira:
- Scopus