DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit is developed by Sandia National Laboratories, and available under a GNU Lesser General Public License (LGPL). DAKOTA provides the following 4 capabilities; (1) optimization with gradient and nongradient-based methods, (2) uncertainty quantification with sampling, reliability, stochastic expansion and epistemic methods, (3) parameter estimation with nonlinear least squares methods, and (4) sensitivity/variance analysis with design of experiments and parameter study methods. JAGUAR (JAva GUi for Applied Research) is a Java software tool providing an advanced text editor and graphical user interface to manipulate DAKOTA input specifications. In this paper, we report the results of the capabilities of DAKOTA, especially optimization. DAKOTA is applied to some famous optimization examples and is verified that outputs are correct. DAKOTA is applied to the gradient-based local methods with constraints, the derivative-free global method, and multi-objective problem, to confirmative its optimal capabilities. Additionally, DAKOTA is compared with other optimal system.

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In order to accelerate incompressible flow analysis, 2-dimensional Navior-Stokes equation is solved on GPU (Graphics Processing Unit). The code has been developed in CUDA and all the dependent variables are allocated on the global memory without communications between CPU and GPU memories. The SMAC algorithm is applied and the spatial derivatives are represented by the finite difference on Cartesian grid. The Red&Black Multigrid (MG) method improves the convergence of the SOR method for the pressure Poisson equation. The 3-stage Runge-Kutta method is applied to integrate in time for the advection term with 3rd-order upwind scheme. For the neighbor-grid accesses, we use the small-size shared memory as a software-managed cache. The overall performance of the incompressible flow calculation achieves 17 GFLOPS and about 14-times speed-up to compare with the 1-core CPU calculation. We carry out the computing for the flows around a circle and the Strouhal numbers of the GPU results are in good agreement with those obtained experimentally. It is found that the GPU computing has great contribution for incompressible flow analysis from the engineering view point.

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本論文では，ML系高階関数型言語とC言語とのシームレスな連携を実現する外部関数インターフェースを提案する．このインターフェースによって，プログラマは整数や浮動小数点データなどの基本データ型はもちろん，それらを含む構造体や関数へのポインタなどを引数とする種々の型のCの関数を，特別なデータ変換処理などを書くことなく，MLプログラムに直接インポートし，利用することができる．この機構は，整数や浮動小数点データなどの実行時の表現をCと共通な自然なものにする型主導コンパイル技術と，ML系関数型言語とCとの関数表現の違いを克服しコールバックを含む言語間の関数呼び出しを可能にする外部関数とのインターフェース技術によって実現できる．本論文では，この外部関数インターフェイスの理論と実装の詳細を報告する．本論文で報告する外部関数インターフェイスは，Standard MLの拡張言語SML#に実装され，そのソースコードを含め公開されている．

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