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Asahi, Yuichi; Padioleau, T.*; Latu, G.*; Bigot, J.*; Grandgirard, V.*; Obrejan, K.*
Dai-36-Kai Suchi Ryutai Rikigaku Shimpojiumu Koen Rombunshu (Internet), 8 Pages, 2022/12
We implement a kinetic plasma simulation code with multiple performance portable frameworks and evaluated its performance on Intel Icelake, NVIDIA V100 and A100 GPUs, and AMD MI100 GPU. Relying on the language standard parallelism stdpar and proposed language standard multi-dimensional array support mdspan, we demonstrate a performance portable implementation without harming the readability and productivity. With stdpar, we obtain a good overall performance for a kinetic plasma mini-application in the range of 
20% to the Kokkos version on Icelake, V100, A100 and MI100. We conclude that stdpar can be a good candidate to develop a performance portable and productive code targeting Exascale era platforms, assuming this programming model will be available on AMD and/or Intel GPUs in the future.
Asahi, Yuichi; Padioleau, T.*; Latu, G.*; Bigot, J.*; Grandgirard, V.*; Obrejan, K.*
Proceedings of 2022 International Workshop on Performance, Portability, and Productivity in HPC (P3HPC) (Internet), p.68 - 80, 2022/11
Times Cited Count：0 Percentile：0(Computer Science, Theory & Methods)This paper presents the performance portable implementation of a kinetic plasma simulation code with C++ parallel algorithm to run across multiple CPUs and GPUs. Relying on the language standard parallelism stdpar and proposed language standard multi-dimensional array support mdspan, we demonstrate that a performance portable implementation is possible without harming the readability and productivity. We obtain a good overall performance for a mini-application in the range of 20% to the Kokkos version on Intel Icelake, NVIDIA V100, and A100 GPUs. Our conclusion is that stdpar can be a good candidate to develop a performance portable and productive code targeting the Exascale era platform, assuming this approach will be available on AMD and/or Intel GPUs in the future.
Asahi, Yuichi; Latu, G.*; Bigot, J.*; Grandgirard, V.*
Proceedings of 2021 International Workshop on Performance, Portability, and Productivity in HPC (P3HPC) (Internet), p.79 - 91, 2021/11
This paper presents optimization strategies dedicated to a kinetic plasma simulation code that makes use of OpenACC/OpenMP directives and Kokkos performance portable framework to run across multiple CPUs and GPUs. We evaluate the impacts of optimizations on multiple hardware platforms: Intel Xeon Skylake, Fujitsu Arm A64FX, and Nvidia Tesla P100 and V100. After the optimizations, the OpenACC/OpenMP version achieved the acceleration of 1.07 to 1.39. The Kokkos version in turn achieved the acceleration of 1.00 to 1.33. Since the impact of optimizations under multiple combinations of kernels, devices and parallel implementations is demonstrated, this paper provides a widely available approach to accelerate a code keeping the performance portability. To achieve an excellent performance on both CPUs and GPUs, Kokkos could be a reasonable choice which offers more flexibility to manage multiple data and loop structures with a single codebase.
Asahi, Yuichi; Latu, G.*; Bigot, J.*; Grandgirard, V.*
Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.218 - 224, 2020/10
Performance portability is expected to be a critical issue in the upcoming exascale era. We explore a performance portable approach for a fusion plasma turbulence simulation code employing the kinetic model, namely the GYSELA code. For this purpose, we extract the key features of GYSELA such as the high dimensionality (more than 4D) and the semi-Lagrangian scheme, and encapsulate them into a mini-application which solves the similar but a simplified Vlasov-Poisson system as GYSELA. We implement the mini-app with OpenACC, OpenMP4.5 and Kokkos, where we suppress unnecessary duplications of code lines. Based on our experience, we discuss the advantages and disadvantages of OpenACC, OpenMP4.5 and Kokkos, from the view point of performance portability, readability and productivity.
Asahi, Yuichi*; Latu, G.*; Bigot, J.*; Maeyama, Shinya*; Grandgirard, V.*; Idomura, Yasuhiro
Concurrency and Computation; Practice and Experience, 32(5), p.e5551_1 - e5551_21, 2020/03
Times Cited Count：1 Percentile：14.03(Computer Science, Software Engineering)Two five-dimensional gyrokinetic codes GYSELA and GKV were ported to the modern accelerators, Xeon Phi KNL and Tesla P100 GPU. Serial computing kernels of GYSELA on KNL and GKV on P100 GPU were respectively 1.3x and 7.4x faster than those on a single Skylake processor. Scaling tests of GYSELA and GKV were respectively performed from 16 to 512 KNLs and from 32 to 256 P100 GPUs, and data transpose communications in semi-Lagrangian kernels in GYSELA and in convolution kernels in GKV were found to be main bottlenecks, respectively. In order to mitigate the communication costs, pipeline-based and task-based communication overlapping were implemented in these codes.
Asahi, Yuichi; Latu, G.*; Bigot, J.*; Grandgirard, V.*
no journal, ,
To prepare the performance portable version of the kinetic plasma simulation code, we develop a simplified but self-contained semi-Lagrangian mini-app with Kokkos performance portable framework and OpenMP/OpenACC which works on both CPUs and GPUs. We investigate the performance of the mini-app over the novel arm-based processor Fujitsu A64FX, Nvidia Tesla GPU, and Intel Skylake, where the arm-based architectures and GPUs are supposed to be major architectures in the exa-scale supercomputing era. The porting cost is highly suppressed with both Kokkos and directive implementations, where the code duplication is avoided. The higher performance portability is achieved with OpenMP/OpenACC, particularly for the compute intense kernels among the hotspots. Unfortunately, a relatively low performance is obtained on A64FX for kernels with indirect memory accesses. We also discuss what kind of Kokkos/OpenMP/OpenACC features are useful to improve the readability and productivity.
Grandgirard, V.*; Asahi, Yuichi; Bigot, J.*; Bourne, E.*; Dif-Pradalier, G.*; Donnel, P.*; Garbet, X.*; Ghendrih, P.*
no journal, ,
Core transport modelling in tokamak plasmas has now reached maturity with non-linear 5D gyrokinetic codes in the world available to address this issue. However, despite numerous successes, their predictive capabilities are still challenged, especially for optimized discharges. Bridging this gap requires extending gyrokinetic modelling in the edge and close to the material boundaries, preferably addressing edge and core transport on an equal footing. This is one of the long term challenges for the petascale code GYSELA [V. Grandgirard et al., CPC 2017 (35)]. Edge-core turbulent plasma simulations with kinetic electrons will require exascale HPC capabilities. We present here the different strategies that we are currently exploring to target the disruptive use of billions of computing cores expected in exascale-class supercomputer as OpenMP4.5 tasks for overlapping computations and MPI communications, KOKKOS for performant portability programming and code refactoring.
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; Idomura, Yasuhiro; Onodera, Naoyuki; et al.
no journal, ,
We have established an in-situ data analysis method for large scale fluid simulation data and developed deep learning based surrogate models to predict fluid simulation results. Firstly, we have developed an in-situ data processing approach, which loosely couples the MPI application and python scripts. It has been shown that this approach is simple and efficient which offers the speedup of 2.7 compared to post hoc data processing. Secondly, we have developed a deep learning model for predicting multiresolution steady flow fields. The deep learning model can give reasonably accurate predictions of simulation results with orders of magnitude faster compared to simulations.
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Obrejan, K.*; Padioleau, T.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; et al.
no journal, ,
We will demonstrate the performance portable implementation of a kinetic plasma code over CPUs, Nvidia and AMD GPUs. We will also discuss the performance portability of the code with C++ parallel algorithm. Deep learning based surrogate models for fluid simulations will also be demonstrated.
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Obrejan, K.*; Padioleau, T.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; et al.
no journal, ,
We will demonstrate the performance portable implementation of a kinetic plasma code over CPUs, Nvidia and AMD GPUs. We will also discuss the performance portability of the code with C++ parallel algorithm. Deep learning based surrogate models for fluid simulations will also be demonstrated.
Asahi, Yuichi; Hasegawa, Yuta; Padioleau, T.*; Millan, A.*; Bigot, J.*; Grandgirard, V.*; Obrejan, K.*
no journal, ,
Generally, production-ready scientific simulations consist of many different tasks including computations, communications and file I/O. Compared to the accelerated computations with GPUs, communications and file I/O would be slower which can be major bottlenecks. It is thus quite important to manage these tasks concurrently to suppress these costs. In the present talk, we employ the proposed language standard C++ senders/receivers to mask the costs of communications and file I/O. As a case study, we implement a 2D turbulence simulation code with the local ensemble transform Kalman filter (LETKF) using C++ senders/receivers. In LETKF, the mock observation data are read from files followed by MPI communications and dense matrix operations on GPUs. We demonstrate the performance portable implementation with this framework, while exploiting the performance gain with the introduced concurrency.
