Optimizing BIT1, a Particle-in-Cell Monte Carlo Code, with OpenMP/OpenACC and GPU Acceleration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F24%3A00616709" target="_blank" >RIV/61389021:_____/24:00616709 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-63749-0_22" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-63749-0_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-63749-0_22" target="_blank" >10.1007/978-3-031-63749-0_22</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing BIT1, a Particle-in-Cell Monte Carlo Code, with OpenMP/OpenACC and GPU Acceleration

Popis výsledku v původním jazyce

On the path toward developing the first fusion energy devices, plasma simulations have become indispensable tools for supporting the design and development of fusion machines. Among these critical simulation tools, BIT1 is an advanced Particle-in-Cell code with Monte Carlo collisions, specifically designed for modeling plasma-material interaction and, in particular, analyzing the power load distribution on tokamak divertors. The current implementation of BIT1 relies exclusively on MPI for parallel communication and lacks support for GPUs. In this work, we address these limitations by designing and implementing a hybrid, shared-memory version of BIT1 capable of utilizing GPUs. For shared-memory parallelization, we rely on OpenMP and OpenACC, using a task-based approach to mitigate load-imbalance issues in the particle mover. On an HPE Cray EX computing node, we observe an initial performance improvement of approximately 42%, with scalable performance showing an enhancement of about 38% when using 8 MPI ranks. Still relying on OpenMP and OpenACC, we introduce the first version of BIT1 capable of using GPUs. We investigate two different data movement strategies: unified memory and explicit data movement. Overall, we report BIT1 data transfer findings during each PIC cycle. Among BIT1 GPU implementations, we demonstrate performance improvement through concurrent GPU utilization, especially when MPI ranks are assigned to dedicated GPUs. Finally, we analyze the performance of the first BIT1 GPU porting with the NVIDIA Nsight tools to further our understanding of BIT1’s computational efficiency for large-scale plasma simulations, capable of exploiting current supercomputer infrastructures.

Název v anglickém jazyce

Optimizing BIT1, a Particle-in-Cell Monte Carlo Code, with OpenMP/OpenACC and GPU Acceleration

Popis výsledku anglicky

On the path toward developing the first fusion energy devices, plasma simulations have become indispensable tools for supporting the design and development of fusion machines. Among these critical simulation tools, BIT1 is an advanced Particle-in-Cell code with Monte Carlo collisions, specifically designed for modeling plasma-material interaction and, in particular, analyzing the power load distribution on tokamak divertors. The current implementation of BIT1 relies exclusively on MPI for parallel communication and lacks support for GPUs. In this work, we address these limitations by designing and implementing a hybrid, shared-memory version of BIT1 capable of utilizing GPUs. For shared-memory parallelization, we rely on OpenMP and OpenACC, using a task-based approach to mitigate load-imbalance issues in the particle mover. On an HPE Cray EX computing node, we observe an initial performance improvement of approximately 42%, with scalable performance showing an enhancement of about 38% when using 8 MPI ranks. Still relying on OpenMP and OpenACC, we introduce the first version of BIT1 capable of using GPUs. We investigate two different data movement strategies: unified memory and explicit data movement. Overall, we report BIT1 data transfer findings during each PIC cycle. Among BIT1 GPU implementations, we demonstrate performance improvement through concurrent GPU utilization, especially when MPI ranks are assigned to dedicated GPUs. Finally, we analyze the performance of the first BIT1 GPU porting with the NVIDIA Nsight tools to further our understanding of BIT1’s computational efficiency for large-scale plasma simulations, capable of exploiting current supercomputer infrastructures.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computational Science – ICCS 2024

ISBN

978-3-031-63748-3

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

316-330

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Málaga

Datum konání akce

2. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001279316700022