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 21回となる今回は国外から3人,国内から1人の講演者をお招きし,数値アルゴリズム,自動チューニングなどHPC(High-Performance Computing)に関する様々な分野について議論します。また、若手・女性利用者推薦制度H27年度採択課題の採択者の発表もあります。皆様ふるってご参加ください。


日時:2015 年 12 月 2 日(水)13:00 ~ 18:05
主催:東京大学情報基盤センター スーパーコンピューティング研究部門


21th Advanced Supercomputing Environment (ASE) Seminar

December 2nd (Wednesday), 2015, 13:00 - 18:05

4F Remote Conference Room, Information Technology Center (ITC), The University of Tokyo

13:00 - 13:05 Welcome Address

13:05 - 13:50 Achim Basermann (German Aerospace Center (DLR), Germany)


 The Highly Scalable Iterative Solver Library PHIST


 As modern supercomputers approach the exascale, many numerical libraries face scalability issues due to the massive increase in CPU cores compared to memory bandwidth. Sparse matrix algorithms, e.g. iterative linear and eigenvalue solvers, are particularly affected by the relatively slow memory subsystem.

In the German Research Foundation (DFG) project ESSEX (Equipping Sparse Solvers for Exascale), we developed the flexible software framework PHIST for implementing iterative methods on HPC systems. PHIST (Pipelined Hybrid Iterative Solver Toolkit) has been developed containing an interface to the existing numerical software framework Trilinos originally. PHIST also includes adapters to basic building block libraries so that high-level algorithm developments can benefit from high-performance kernel implementations, e.g. sparse matrix-vector multiplication kernels.
Moreover, PHIST provides systematic and continuous testing of all software components and allows us to develop stable implementations of innovative iterative methods in an evolving hard- and software environment.

Using the PHIST framework, we present recent work on sparse eigenvalue solvers, from scalable basic operations to complete algorithms, with a focus on the difficult problem of finding interior eigenvalues. In this context, we also discuss the application of the CARP-CG method as a preconditioner for applications such as Graphene simulation.

13:50 - 14:00 Break

14:00 - 14:45 Edmond Chow (Georgia Institute of Technology, USA)


 Preconditioning for Parallel Computers


 Preconditioners are approximations to a matrix or to the inverse of a matrix, called implicit and explicit preconditioners, respectively.
Using an implicit preconditioner generally requires triangular solves or other operations that are difficult to parallelize. On the other hand, using an explicit preconditioner generally only requires easy-to-parallelize sparse matrix-vector products. However, many explicit preconditioners, such as sparse approximate inverses, are not able to provide the same convergence rates as implicit preconditioners such as incomplete factorizations, due to their local nature. In this talk, we first review recent research on parallel preconditioning and then present a new explicit preconditioner that is designed to propagate data globally at each step. Although it is expensive compute, this new preconditioner can lead to very fast convergence rates while also being very efficient to apply on parallel computers.

14:45 - 14:55 Break

14:55 - 15:40 Weichung Wang (National Taiwan University, Taiwan)


 High-Performance Linear System and Eigenvalue Solvers for Frequency Domain Photonic Device Simulations


 Large-scale linear systems and eigenvalue problems arise in simulations of three-dimensional photonics devices in frequency domain. In particular, we concern frequency domain photonic device simulations involving periodic or homogeneous structures. These three-dimensional devices can be modeled by the Maxwell vector wave equations with or without absorptive (perfect matching layer) boundary conditions. To efficiently solve these time and memory consuming ill-conditioned problems, our newly developed compressed hierarchical Schur method (ChiS) provides enhanced computation performance.

CHiS takes advantages of the periodic or homogeneous structures to remove the redundant computations and storages. CHiS also uses domain decompositions to achieve coarse-grains parallelism in its elimination tree. The dense BLAS3 operations within the sub-matrices and the Schur complement allow us to achieve fine-grained parallelism on multicore CPU or GPU. Several numerical results in three-dimensional photonics devices including guided wave examples are demonstrated.

15:40 - 15:50 Break

15:50 - 16:20 Satoshi Ohshima (ITC, The University of Tokyo, Japan)


 Auto-Tuning for OpenACC: Utilization and Expansion of ppOpen-AT for OpenACC


 OpenACC makes GPU programming easy. But, in order to obtain high performance, users have to consider various optimization techniques and parameters. In the case of multi-core and many-core environment, we have developed Auto-Tuning language named ppOpen-AT since several years and shown the availability of it. In this study, we investigate the usability of ppOpen-AT for OpenACC. Moreover, we propose to expand ppOpen-AT for further optimization of OpenACC.

16:20 - 16:50 Yuka Kobayashi and Takeshi Ogita (Tokyo Woman's Christian University, Japan)


 Accurate and efficient algorithms for solving ill-conditioned linear systems


 We are concerned with accurate solutions of ill-conditioned linear systems by using floating-point arithmetic.

One of the standard methods of solving linear systems accurately is using LU factorization with the iterative refinement method. It is effective if the problem is not ill-conditioned. However, the method cannot work well for ill-conditioned problems. Another possibility is to use multiple-precision arithmetic. Although it can work for ill-conditioned problems, it requires significant computing time.

To remedy the defects, we propose algorithms based on a preconditioned technique using a result of an LU factorization performed in floating-point arithmetic. The proposed algorithms can provide accurate numerical solutions for ill-conditioned problems beyond the limit of the working precision. Moreover, it requires less computational cost than the previous method using an approximate inverse of the coefficient matrix as a preconditioner.

We conducted numerical experiments using the proposed algorithms on MATLAB. Results of numerical experiments are presented for confirming the effectiveness of the proposed algorithm.

16:50 - 17:00 Break

Special Session: Initiative on Promotion of Supercomputing for Young or Women Researchers

17:00 - 17:30 Naoya Nomura, Akihiro Fujii, Teruo Tanaka (Kogakuin University),Kengo Nakajima (The University of Tokyo)


 Performance analysis of SA-AMG method by setting near-kernel vectors


 SA-AMG ( Smoothed Aggregation – Algebraic Multigrid Method ) method is one of the fastest solvers for large scale linear equations. Convergence of SA-AMG method can be improved by setting near-kernel vectors. Our research aims to investigate effectiveness of setting multiple near-kernel vectors and the method to find important near-kernel vectors for fast convergence. At first stage, we are testing 3-dimensional elastic problem with near-kernel vectors that correspond to parallel transition and rotation. Iteration numbers and execution time with different number of kernel vectors are compared. In addition, we investigate a performance relationship between the number of near-kernel vectors and problem size.

17:30 - 18:00 Hiroto Imachi, Takeo Hoshi (Tottori University, Japan, and JST-CREST)


 Quantum Wave-packet Dynamics Simulation Solvers and Their Performance on Oakleaf-FX10


 We develop a parallel quantum wave-packet dynamics simulation program and investigate its parallel efficiency on Oakleaf-FX10. The program is now a dense solver and we also show preliminary results for a sparse solver.

With the help of recent development of supercomputers, large-scale quantum material simulations for next-generation material design are realizing. The authors have developed theory of massively parallel electronic structure calculations and a simulation code named ELSES (Extra-Large-Scale Electronic Structure calculations). We are recently developing a parallel quantum conduction simulation program as an extension of ELSES.

We model quantum conduction as the time-dependent Schrödinger equation (TDSE), i S dx/dt = H x. Here x, H, S are called a wave-packet vector, a Hamiltonian matrix and an overlap matrix, respectively. For real applications, we have to solve the TDSE whose the dimension m = 10^4 - 10^6 over the number of time steps n = 10^4 - 10^5. It involves computational challenges.

One way to decrease the computational cost to solve the TDSE is dimension reduction by eigenvector expansion. We suppose Hamiltonian H(t) changes slowly as H(t) = H_0 + H_1(t) with a relatively small H_1(t). Then using the solution of the generalized eigenvalue problem H_0 V = S V Λ, we can treat effects of the perturbation term H_1(t) directly and reduce the dimension of an approximate TDSE to solve by selecting eigenvectors to expand the wave-packet x. We show parallel performance of the TDSE solver on Oakleaf-FX10 with input from a real-world application.

The number of non-zero elements in the Hamiltonian and overlap matrices is determined by the number of atoms that each atom interact with. Matrices from systems we are interested in have intermediate property between dense and sparse. Therefore we are developing a sparse solver as a complement of the dense solver with eigenvector expansion. The sparse solver is based on sparse linear equations and we investigate which combination of a sparse linear equation solver and a preconditioner is optimal for our input matrices with LIS (Library of Iterative Solvers for linear systems), a software library of parallel iterated solvers for numerical linear algebra problems. We also show preliminary results on the performance of the sparse solver.

This research is partially supported by Initiative on Promotion of Supercomputing for Young or Women Researchers, Supercomputing Division, Information Technology Center, The University of Tokyo.

18:00 - 18:05 Closing Address


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東京大学 情報基盤センター

ASE研究会幹事 准教授 片桐孝洋