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Idomura, Yasuhiro; Ina, Takuya*; Ali, Y.*; Imamura, Toshiyuki*
Dai-34-Kai Suchi Ryutai Rikigaku Shimpojiumu Koen Rombunshu (Internet), 6 Pages, 2020/12
A new communication avoiding (CA) Krylov solver with a FP16 (half precision) preconditioner is developed for a semi-implicit finite difference solver in the Gyrokinetic Toroidal 5D full-f Eulerian code GT5D. In the solver, the bottleneck of global collective communication is resolved using a CA-Krylov subspace method, and halo data communication is reduced by the FP16 preconditioner, which improves the convergence property. The FP16 preconditioner is designed based on the physics properties of the operator and is implemented using the new support for FP16 SIMD operations on A64FX. The solver is ported also on GPUs, and the performance of ITER size simulations with 
trillion grids is measured on Fugaku (A64FX) and Summit (V100). The new solver accelerates GT5D by 
from the conventional non-CA solver, and excellent strong scaling is obtained up to 5,760 CPUs/GPUs both on Fugaku and Summit.
Idomura, Yasuhiro; Ina, Takuya*; Ali, Y.*; Imamura, Toshiyuki*
Proceedings of International Conference on High Performance Computing, Networking, Storage, and Analysis (SC 2020) (Internet), p.1318 - 1330, 2020/11
The multi-scale full-
simulation of the next generation experimental fusion reactor ITER based on a five dimensional (5D) gyrokinetic model is one of the most computationally demanding problems in fusion science. In this work, a Gyrokinetic Toroidal 5D Eulerian code (GT5D) is accelerated by a new mixed-precision communication-avoiding (CA) Krylov method. The bottleneck of global collective communication on accelerated computing platforms is resolved using a CA Krylov method. In addition, a new FP16 preconditioner, which is designed using the new support for FP16 SIMD operations on A64FX, reduces both the number of iterations (halo data communication) and the computational cost. The performance of the proposed method for ITER size simulations with 0.1 trillion grids on 1,440 CPUs/GPUs on Fugaku and Summit shows 2.8x and 1.9x speedups respectively from the conventional non-CA Krylov method, and excellent strong scaling is obtained up to 5,760 CPUs/GPUs.
Idomura, Yasuhiro; Ina, Takuya*; Ali, Y.*; Imamura, Toshiyuki*
Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.225 - 230, 2020/10
A new communication avoiding (CA) Krylov solver with a FP16 (half precision) preconditioner is developed for a semi-implicit finite difference solver in the Gyrokinetic Toroidal 5D full-f Eulerian code GT5D. In the solver, the bottleneck of global collective communication is resolved using a CA-Krylov subspace method, while the number of halo data communication is reduced by improving the convergence property using the FP16 preconditioner. The FP16 preconditioner is designed based on the physics properties of the operator and is implemented using the new support for FP16 SIMD operations on A64FX. The solver is ported on Fugaku (A64FX) and Summit (V100), which respectively show 
63x and 29x speedups in socket performance compared to the conventional non-CA Krylov solver on JAEA-ICEX (Haswell).
Cs tracerSuzui, Nobuo*; Shibata, Takuya; Yin, Y.-G.*; Funaki, Yoshihito*; Kurita, Keisuke; Hoshina, Hiroyuki*; Yamaguchi, Mitsutaka*; Fujimaki, Shu*; Seko, Noriaki*; Watabe, Hiroshi*; et al.
Scientific Reports (Internet), 10, p.16155_1 - 16155_9, 2020/10
Times Cited Count：0Idomura, Yasuhiro; Onodera, Naoyuki; Yamada, Susumu; Yamashita, Susumu; Ina, Takuya*; Imamura, Toshiyuki*
Supa Kompyuthingu Nyusu, 22(5), p.18 - 29, 2020/09
A communication avoiding multigrid preconditioned conjugate gradient method (CAMGCG) is applied to the pressure Poisson equation in a multiphase CFD code JUPITER, and its computational performance and convergence property are compared against the conventional Krylov methods. The CAMGCG solver has robust convergence properties regardless of the problem size, and shows both communication reduction and convergence improvement, leading to higher performance gain than CA Krylov solvers, which achieve only the former. The CAMGCG solver is applied to extreme scale multiphase CFD simulations with 90 billion DOFs, and its performance is compared against the preconditioned CG solver. In this benchmark, the number of iterations is reduced to 
, and 
speedup is achieved with keeping excellent strong scaling up to 8,000 nodes on the Oakforest-PACS.
Matsumoto, Kazuya*; Idomura, Yasuhiro; Ina, Takuya*; Mayumi, Akie; Yamada, Susumu
Journal of Supercomputing, 75(12), p.8115 - 8146, 2019/12
Times Cited Count：0 Percentile：100(Computer Science, Hardware & Architecture)A communication-avoiding generalized minimum residual method (CA-GMRES) is implemented on a hybrid CPU-GPU cluster, targeted for the performance acceleration of iterative linear system solver in the gyrokinetic toroidal five-dimensional Eulerian code GT5D. In addition to the CA-GMRES, we implement and evaluate a modified variant of CA-GMRES (M-CA-GMRES) proposed in our previous study to reduce the amount of floating-point calculations. This study demonstrates that beneficial features of the CA-GMRES are in its minimum number of collective communications and its highly efficient calculations based on dense matrix-matrix operations. The performance evaluation is conducted on the Reedbush-L GPU cluster, which contains four NVIDIA Tesla P100 GPUs per compute node. The evaluation results show that the M-CA-GMRES is 1.09x, 1.22x and 1.50x faster than the CA-GMRES, the generalized conjugate residual method (GCR), and the GMRES, respectively, when 64 GPUs are used.
Ali, Y.*; Onodera, Naoyuki; Idomura, Yasuhiro; Ina, Takuya*; Imamura, Toshiyuki*
Proceedings of 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA 2019), p.1 - 8, 2019/11
Times Cited Count：3 Percentile：1.37Iterative methods for solving large linear systems are common parts of computational fluid dynamics (CFD) codes. The Preconditioned Conjugate Gradient (P-CG) method is one of the most widely used iterative methods. However, in the P-CG method, global collective communication is a crucial bottleneck especially on accelerated computing platforms. To resolve this issue, communication avoiding (CA) variants of the P-CG method are becoming increasingly important. In this paper, the P-CG and Preconditioned Chebyshev Basis CA CG (P-CBCG) solvers in the multiphase CFD code JUPITER are ported to the latest V100 GPUs. All GPU kernels are highly optimized to achieve about 90% of the roofline performance, the block Jacobi preconditioner is re-designed to extract high computing power of GPUs, and the remaining bottleneck of halo data communication is avoided by overlapping communication and computation. The overall performance of the P-CG and P-CBCG solvers is determined by the competition between the CA properties of the global collective communication and the halo data communication, indicating an importance of the inter-node interconnect bandwidth per GPU. The developed GPU solvers are accelerated up to 2x compared with the former CPU solvers on KNLs, and excellent strong scaling is achieved up to 7,680 GPUs on the Summit.
Idomura, Yasuhiro; Ina, Takuya*; Yamashita, Susumu; Onodera, Naoyuki; Yamada, Susumu; Imamura, Toshiyuki*
Proceedings of 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA 2018) (Internet), p.17 - 24, 2018/11
Times Cited Count：0 Percentile：100A communication avoiding (CA) multigrid preconditioned conjugate gradient method (CAMGCG) is applied to the pressure Poisson equation in a multiphase CFD code JUPITER, and its computational performance and convergence property are compared against CA Krylov methods. In the JUPITER code, the CAMGCG solver has robust convergence properties regardless of the problem size, and shows both communication reduction and convergence improvement, leading to higher performance gain than CA Krylov solvers, which achieve only the former. The CAMGCG solver is applied to extreme scale multiphase CFD simulations with 
billion DOFs, and it is shown that compared with a preconditioned CG solver, the number of iterations is reduced to , and speedup is achieved with keeping excellent strong scaling up to 8,000 nodes on the Oakforest-PACS.
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studied by Cu 
-edge X-ray absorption spectroscopyAsano, Shun*; Ishii, Kenji*; Matsumura, Daiju; Tsuji, Takuya; Ina, Toshiaki*; Suzuki, Kensuke*; Fujita, Masaki*
Journal of the Physical Society of Japan, 87(9), p.094710_1 - 094710_5, 2018/09
Times Cited Count：4 Percentile：42.19(Physics, Multidisciplinary)Seko, Noriaki*; Hoshina, Hiroyuki*; Kasai, Noboru*; Shibata, Takuya; Saiki, Seiichi*; Ueki, Yuji*
Radiation Physics and Chemistry, 143, p.33 - 37, 2018/02
Times Cited Count：5 Percentile：21.94(Chemistry, Physical)Idomura, Yasuhiro; Ina, Takuya*; Mayumi, Akie; Yamada, Susumu; Imamura, Toshiyuki*
Lecture Notes in Computer Science 10776, p.257 - 273, 2018/00
A preconditioned Chebyshev basis communication-avoiding conjugate gradient method (P-CBCG) is applied to the pressure Poisson equation in a multiphase thermal-hydraulic CFD code JUPITER, and its computational performance and convergence properties are compared against a preconditioned conjugate gradient (P-CG) method and a preconditioned communication-avoiding conjugate gradient (P-CACG) method on the Oakforest-PACS, which consists of 8,208 KNLs. The P-CBCG method reduces the number of collective communications with keeping the robustness of convergence properties. Compared with the P-CACG method, an order of magnitude larger communication-avoiding steps are enabled by the improved robustness. It is shown that the P-CBCG method is 
and 
faster than the P-CG and P-CACG methods at 2,000 processors, respectively.
Yamashita, Susumu; Ina, Takuya*; Idomura, Yasuhiro; Yoshida, Hiroyuki
Dai-31-Kai Suchi Ryutai Rikigaku Shimpojiumu Koen Rombunshu (DVD-ROM), 7 Pages, 2017/12
no abstracts in English
Idomura, Yasuhiro; Ina, Takuya*; Mayumi, Akie; Yamada, Susumu; Matsumoto, Kazuya*; Asahi, Yuichi*; Imamura, Toshiyuki*
Proceedings of 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA 2017), p.7_1 - 7_8, 2017/11
A communication-avoiding generalized minimal residual (CA-GMRES) method is applied to the gyrokinetic toroidal five dimensional Eulerian code GT5D, and its performance is compared against the original code with a generalized conjugate residual (GCR) method on the JAEA ICEX (Haswell), the Plasma Simulator (FX100), and the Oakforest-PACS (KNL). The CA-GMRES method has 
higher arithmetic intensity than the GCR method, and thus, is suitable for future Exa-scale architectures with limited memory and network bandwidths. In the performance evaluation, it is shown that compared with the GCR solver, its computing kernels are accelerated by 
, and the cost of data reduction communication is reduced from 
to 
of the total cost at 1,280 nodes.
Yamashita, Susumu; Ina, Takuya; Idomura, Yasuhiro; Yoshida, Hiroyuki
Nuclear Engineering and Design, 322, p.301 - 312, 2017/10
Times Cited Count：13 Percentile：7.51(Nuclear Science & Technology)In recent years, significant attention has been paid to the precise determination of relocation of molten materials in reactor pressure vessels of boiling water reactors (BWRs) during severe accidents. To address this problem, we have developed a computational fluid dynamics code JUPITER, based on thermal-hydraulic equations and multi-phase simulation models. Although the Poisson solver has previously been a performance bottleneck in the JUPITER code, this is resolved by a new hybrid parallel Poisson solver, whose strong scaling is extended up to 200k cores on the K-computer. As a result of the improved computational capability, the problem size and physical models are dramatically expanded. A series of verification and validation studies are enabled, which are in agreement with previous numerical simulations and experiments. These physical and computational capabilities of JUPITER enable us to investigate molten material behaviors in reactor relevant situations.
Yamada, Susumu; Ina, Takuya*; Sasa, Narimasa; Idomura, Yasuhiro; Machida, Masahiko; Imamura, Toshiyuki*
Proceedings of 2017 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW) (Internet), p.1418 - 1425, 2017/08
Times Cited Count：3 Percentile：21.83no abstracts in English
Asahi, Yuichi*; Latu, G.*; Ina, Takuya; Idomura, Yasuhiro; Grandgirard, V.*; Garbet, X.*
IEEE Transactions on Parallel and Distributed Systems, 28(7), p.1974 - 1988, 2017/07
Times Cited Count：4 Percentile：41.29(Computer Science, Theory & Methods)High-dimensional stencil computation from fusion plasma turbulence codes involving complex memory access patterns, the indirect memory access in a Semi-Lagrangian scheme and the strided memory access in a Finite-Difference scheme, are optimized on accelerators such as GPGPUs and Xeon Phi coprocessors. On both devices, the Array of Structure of Array (AoSoA) data layout is preferable for contiguous memory accesses. It is shown that the effective local cache usage by improving spatial and temporal data locality is critical on Xeon Phi. On GPGPU, the texture memory usage improves the performance of the indirect memory accesses in the Semi-Lagrangian scheme. Thanks to these optimizations, the fusion kernels on accelerators become 1.4x - 8.1x faster than those on Sandy Bridge (CPU).
Mayumi, Akie; Idomura, Yasuhiro; Ina, Takuya; Yamada, Susumu; Imamura, Toshiyuki*
Proceedings of 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA 2016) (Internet), p.17 - 24, 2016/11
The left-preconditioned communication avoiding conjugate gradient (LP-CA-CG) method is applied to the pressure Poisson equation in the multiphase CFD code JUPITER. The arithmetic intensity of the LP-CA-CG method is analyzed, and is dramatically improved by loop splitting for inner product operations and for three term recurrence operations. Two LP-CA-CG solvers with block Jacobi preconditioning and with underlap preconditioning are developed. It is shown that on the K computer, the LP-CA-CG solvers with block Jacobi preconditioning is faster, because the performance of local point-to-point communications scales well, and the convergence property becomes worse with underlap preconditioning. The LP-CA-CG solver shows good strong scaling up to 30,000 nodes, where the LP-CA-CG solver achieved higher performance than the original CG solver by reducing the cost of global collective communications by 69%.
Idomura, Yasuhiro; Asahi, Yuichi; Ina, Takuya; Matsuoka, Seikichi
Proceedings of 24th International Congress of Theoretical and Applied Mechanics (ICTAM 2016), p.3106 - 3107, 2016/08
Turbulent transport in fusion plasmas is one of key issues in ITER. To address this issue via the five dimensional (5D) gyrokinetic model, a novel computing technique is developed, and strong scaling of the Gyrokinetic Toroidal 5D Eulerian code GT5D is improved up to 
million cores on the K-computer. The computing technique consists of multi-dimensional/multi-layer domain decomposition, overlap of communication and computation, and optimization of computing kernels for multi-core CPUs. The computing power enabled us to study ITER relevant issues such as the plasma size scaling of turbulent transport. Towards the next generation burning plasma turbulence simulations, the physics model is extended including kinetic electrons and multi-species ions, and computing kernels are further optimized for the latest many-core architectures.
Tc solution from 
Mo to be obtained by (
) reaction; A Preliminary study using inactive Re instead of TcTanase, Masakazu*; Fujisaki, Saburo*; Ota, Akio*; Shiina, Takayuki*; Yamabayashi, Hisamichi*; Takeuchi, Nobuhiro*; Tsuchiya, Kunihiko; Kimura, Akihiro; Suzuki, Yoshitaka; Ishida, Takuya; et al.
Radioisotopes, 65(5), p.237 - 245, 2016/05
no abstracts in English
Shibata, Takuya; Seko, Noriaki; Amada, Haruyo; Kasai, Noboru; Saiki, Seiichi; Hoshina, Hiroyuki; Ueki, Yuji
Radiation Physics and Chemistry, 119, p.247 - 252, 2016/02
Times Cited Count：6 Percentile：38.13(Chemistry, Physical)