Improvements of the particle-in-cell code EUTERPE for petascaling machines

Sáez, X.; Soba, Alejandro; Sánchez, E.; Kleiber, R.; Castejón, F.; María, José

doi:10.1016/j.cpc.2010.12.038

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…VPIC [13] is a relativistic electromagnetic 3D PIC code which aims at minimizing data movement to increase performance. EUTERPE [14] is a gyrokinetic code for the simulation of fusion plasmas, for which also different load balancing strategies have been evaluated as a major influence on performance.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of Particle-in-Cell Codes for Nonlinear Kinetic Models from Mathematical Physics

Korch

Ramming

Rein

2013

2013 42nd International Conference on Parallel Processing

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This paper considers the parallelization of two Particle-in-Cell (PIC) codes which simulate the time evolution of galaxies and globular clusters in the Newtonian or the general relativistic framework. The corresponding models are known as the Vlasov-Poisson or the Einstein-Vlasov system, and the latter is designed in particular to study the formation of black holes and spacetime singularities. We start with a stepby-step shared-memory parallelization of the Vlasov-Poisson code using POSIX Threads and finally develop message passing codes using MPI. The parallel codes have been investigated on three modern supercomputer systems using up to 4096 cores, and speedups above 1300 have been reached. The speedup obtained through parallelization has already helped in finding new numerical results, such as oscillating solutions of the Vlasov-Poisson system.

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Section: Introductionmentioning

confidence: 99%

Parallelization of Particle-in-Cell Codes for Nonlinear Kinetic Models from Mathematical Physics

Korch

Ramming

Rein

2013

2013 42nd International Conference on Parallel Processing

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“…Figure 8. 15. This time, the different lengths of the tasks and the changeable pace at which tasks are dequeued reveal us the presence of lock contention.…”

Section: Pull Phasementioning

confidence: 99%

“…Up to that moment, the scaling of EUTERPE had been studied up to 512 processors in the framework of the project DEISA [12], but our test with 23,552 processors was a turning point and was included into a journal article [14]. Subsequently, we achieved the number of 61,440 processors that we published in [13,15] too.…”

Section: Contributionsmentioning

confidence: 99%

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Particle-in-Cell Algorithms for Plasma Simulations on Heterogeneous Architectures

Sáez

Soba

María

et al. 2011

2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing

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During the last two decades, High-Performance Computing (HPC) has grown rapidly in performance by improving single-core processors at the cost of a similar growth in power consumption. The single-core processor improvement has led many scientists to exploit mainly the process level parallelism in their codes. However, the performance of HPC systems is becoming increasingly limited by power consumption and power density, which have become a primary concern for the design of new computer systems. As a result, new supercomputers are designed based on the power efficiency provided by new homogeneous and heterogeneous architectures.The growth in computational power has introduced a new approach to science, Computational Physics. Its impact on the study of nuclear fusion and plasma physics has been very significant. This is because the experiments are difficult and expensive to perform whereas computer simulations of plasma are an efficient way to progress. Particle-InCell (PIC) is one of the most used methods to simulate plasma. The improvement in the processing power has enabled an increase in the size and complexity of the PIC simulations. Most PIC codes have been designed with a strong emphasis on the physics and have traditionally included only process level parallelism. This approach has not taken advantage of multiprocessor platforms. Therefore, these codes exploit inefficiently the new computing platforms and, as a consequence, they are still limited to using simplified models.The aim of this thesis is to incorporate in a PIC code the latest technologies available in computer science in order to take advantage of the upcoming multiprocessor supercomputers. This will enable an improvement in the simulations, either by introducing more physics in the code or by incorporating more detail to the simulations.This thesis analyses a PIC code named EUTERPE on different computing platforms. EUTERPE is a production code used to simulate fusion plasma instabilities in fusion reactors. It has been implemented for traditional HPC clusters and it has been parallelized prior to this work using only Message Passing Interface (MPI). Our study of its scalability iv has reached up to tens of thousands of processors, which is several orders of magnitude higher than the scalability achieved when this thesis was initiated.This thesis also describes the strategies adopted for porting a PIC code to a multicore architecture, such as introducing thread level parallelism, distributing the work among different computing devices, and developing a new thread-safe solver. These strategies have been evaluated by applying them to the EUTERPE code. With respect to heterogeneous architectures, it has been possible to port this kind of plasma physics codes by rewriting part of the code or by using a programming model called OpenMP Super-scalar (OmpSs). This programming model is specially designed to make this computing power easily available to scientists without requiring expert knowledge on computing.Last but not least, this thesis sho...

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“…The machines synchronize at every time step, by communicating the contribution of their particles to the charge density, in order to update the electric field. A successful hybrid approach, known as domain cloning [18,23], consists of using domain decomposition in order to create subdomains that are just small enough for particle decomposition to apply.…”

Section: Introductionmentioning

confidence: 99%

A Space and Bandwidth Efficient Multicore Algorithm for the Particle-in-Cell Method

Barsamian

Charguéraud

Ketterlin

2018

Parallel Processing and Applied Mathematics

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Abstract. The Particle-in-Cell (PIC) method allows solving partial differential equation through simulations, with important applications in plasma physics. To simulate thousands of billions of particles on clusters of multicore machines, prior work has proposed hybrid algorithms that combine domain decomposition and particle decomposition with carefully optimized algorithms for handling particles processed on each multicore socket. Regarding the multicore processing, existing algorithms either suffer from suboptimal execution time, due to sorting operations or use of atomic instructions, or suffer from suboptimal space usage. In this paper, we propose a novel parallel algorithm for two-dimensional PIC simulations on multicore hardware that features asymptotically-optimal memory consumption, and does not perform unnecessary accesses to the main memory. In practice, our algorithm reaches 65% of the maximum bandwidth, and shows excellent scalability on the classical Landau damping and two-stream instability test cases.

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Improvements of the particle-in-cell code EUTERPE for petascaling machines

Cited by 6 publications

References 9 publications

Parallelization of Particle-in-Cell Codes for Nonlinear Kinetic Models from Mathematical Physics

Parallelization of Particle-in-Cell Codes for Nonlinear Kinetic Models from Mathematical Physics

Particle-in-Cell Algorithms for Plasma Simulations on Heterogeneous Architectures

A Space and Bandwidth Efficient Multicore Algorithm for the Particle-in-Cell Method

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