In this report, we focus on Biofluids problems, specifically the Stokes Equation. The method of regularized Stokeslets can be derived from bound- ary integral equations derived from the Lorentz reciprocal identity. When body forces are known, this is a direct numerical approximation of an in- tegral, resulting in a summation to determine the fluid velocity. In certain cases, which this report is focused on, we know the velocity and want to determine the forces on a structure immersed in a fluid. This results in a lin- ear system Af = u, where A is a square dense matrix. We study different methods to solve this system of equations to determine the force f on the structure. For solving a linear system with a dense coefficient matrix, the backslash command in MATLAB can be used. This will use an efficient and robust direct method for solving a smaller matrix, but this is not an efficient method for a large, dense coefficient matrix. For a large, dense coefficient ma- trix, we will explore other direct methods as well as several iterative methods to determine computation time and error on a test case with an exact solu- tion. For direct methods, we will study backslash, LU factorization and QR factorization methods. For iterative methods, we stuied Jacobi, Gauss-Seidel, SOR, GMRES, CG, CGS, BICGSTAB and Schulz CG methods for these bioflu- ids applications. All of these methods have different requirements. For our coefficient matrix A, we identified specific properties and then used proper methods, both direct and iterative. Result showed that iterative methods are more efficient then direct method for large size A. Schulz CG was slower but had a smaller error for the test case where there was an exact solution.

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Dans ce mémoire de thèse, nous développons d'abord des multiplications matricielles efficaces. Nous créons de nouveaux ordonnancements qui permettent de réduire la taille de la mémoire supplémentaire nécessaire lors d'une multiplication du type Winograd tout en gardant une bonne complexité, grâce au développement d'outils externes ad hoc (jeu de galets), à des calculs fins de complexité et à de nouveaux algorithmes hybrides. Nous utilisons ensuite des technologies parallèles (multicœurs et GPU) pour accélérer efficacement la multiplication entre matrice creuse et vecteur dense (SpMV), essentielles aux algorithmes dits /boîte noire/, et créons de nouveaux formats hybrides adéquats. Enfin, nous établissons des méthodes de /design/ générique orientées vers l'efficacité, notamment par conception par briques de base, et via des auto-optimisations. Nous proposons aussi des méthodes pour améliorer et standardiser la qualité du code de manière à pérenniser et rendre plus robuste le code produit. Cela permet de pérenniser de rendre plus robuste le code produit. Ces méthodes sont appliquées en particulier à la bibliothèque de calcul exact LinBox. / We first expose in this memoir efficient matrix multiplication techniques. We set up new schedules that allow us to minimize the extra memory requirements during a Winograd-style matrix multiplication, while keeping the complexity competitive. In order to get them, we develop external tools (pebble game), tight complexity computations and new hybrid algorithms. Then we use parallel technologies (multicore CPU and GPU) in order to accelerate efficiently the sparse matrix--dense vector multiplication (SpMV), crucial to /blackbox/ algorithms and we set up new hybrid formats to store them. Finally, we establish generic design methods focusing on efficiency, especially via building block conceptions or self-optimization. We also propose tools for improving and standardizing code quality in order to make it more sustainable and more robust. This is in particular applied to the LinBox computer algebra library.

Parallel programming models are quite challenging and emerging topic in the parallel computing era. These models allow a developer to port a sequential application on to a platform with more number of processors so that the problem or application can be solved easily. Adapting the applications in this manner using the Parallel programming models is often influenced by the type of the application, the type of the platform and many others. There are several parallel programming models developed and two main variants of parallel programming models classified are shared and distributed memory based parallel programming models. The recognition of the computing applications that entail immense computing requirements lead to the confrontation of the obstacle regarding the development of the efficient programming models that bridges the gap between the hardware ability to perform the computations and the software ability to support that performance for those applications [25][9]. And so a better programming model is needed that facilitates easy development and on the other hand porting high performance. To answer this challenge this thesis confines and compares four different shared memory based parallel programming models with respect to the development time of the application under a shared memory based parallel programming model to the performance enacted by that application in the same parallel programming model. The programming models are evaluated in this thesis by considering the data parallel applications and to verify their ability to support data parallelism with respect to the development time of those applications. The data parallel applications are borrowed from the Dense Matrix dwarfs and the dwarfs used are Matrix-Matrix multiplication, Jacobi Iteration and Laplace Heat Distribution. The experimental method consists of the selection of three data parallel bench marks and developed under the four shared memory based parallel programming models considered for the evaluation. Also the performance of those applications under each programming model is noted and at last the results are used to analytically compare the parallel programming models. Results for the study show that by sacrificing the development time a better performance is achieved for the chosen data parallel applications developed in Pthreads. On the other hand sacrificing a little performance data parallel applications are extremely easy to develop in task based parallel programming models. The directive models are moderate from both the perspectives and are rated in between the tasking models and threading models. / From this study it is clear that threading model Pthreads model is identified as a dominant programming model by supporting high speedups for two of the three different dwarfs but on the other hand the tasking models are dominant in the development time and reducing the number of errors by supporting high growth in speedup for the applications without any communication and less growth in self-relative speedup for the applications involving communications. The degrade of the performance by the tasking models for the problems based on communications is because task based models are designed and bounded to execute the tasks in parallel without out any interruptions or preemptions during their computations. Introducing the communications violates the purpose and there by resulting in less performance. The directive model OpenMP is moderate in both aspects and stands in between these models. In general the directive models and tasking models offer better speedup than any other models for the task based problems which are based on the divide and conquer strategy. But for the data parallelism the speedup growth however achieved is low (i.e. they are less scalable for data parallel applications) are equally compatible in execution times with threading models. Also the development times are considerably low for data parallel applications this is because of the ease of development supported by those models by introducing less number of functional routines required to parallelize the applications. This thesis is concerned about the comparison of the shared memory based parallel programming models in terms of the speedup. This type of work acts as a hand in guide that the programmers can consider during the development of the applications under the shared memory based parallel programming models. We suggest that this work can be extended in two different ways: one is from the developer‘s perspective and the other is a cross-referential study about the parallel programming models. The former can be done by using a similar study like this by a different programmer and comparing this study with the new study. The latter can be done by including multiple data points in the same programming model or by using a different set of parallel programming models for the study. / C/O K. Manoj Kumar; LGH 555; Lindbloms Vägan 97; 37233; Ronneby. Phone no: 0738743400 Home country phone no: +91 9948671552

L'élaboration de composites à matrice céramique denses et à fibres continues multidirectionnelles par de nouveaux procédés hybrides a été étudiée. Les procédés développés reposent sur le dépôt d'interphases autour des fibres par Infiltration Chimique en phase Vapeur (CVI) puis sur l'introduction de poudres céramiques au sein de préformes fibreuses par infusion de suspensions aqueuses colloïdales concentrées et stables, et enfin sur la consolidation des préformes soit par frittage flash, soit par imprégnation réactive de métaux liquides.La consolidation des composites par frittage flash est très rapide (palier de maintien en température inférieure à 5 minutes) et permet d'obtenir des composites denses. Durant le frittage, la dégradation des fibres de carbone a pu être évitée en adaptant le cycle de pression afin de limiter l'évolution des gaz au sein du système.La densification totale des composites par imprégnation de métaux liquides a été obtenue en contrôlant attentivement les paramètres d'imprégnation afin d'éviter de piéger des espèces gazeuses au sein des préformes fibreuses.Les composites à fibres de carbone consolidés par frittage flash ou par imprégnation réactive de métaux liquide possèdent un comportement mécanique de type élastique endommageable ainsi qu'une contrainte à rupture en flexion voisine de 300 MPa. Ces composites ont montré leur capacité à s'autocicatriser dans des conditions oxydantes. Comparés aux composites à matrice céramiques élaborés par CVI, les composites densifiés par imprégnation de métaux liquide sont eux parfaitement denses et ont un comportement mécanique en traction à température ambiante similaire avec notamment une contrainte à rupture en traction de 220 MPa. / The fabrication of multidirectional continuous carbon fibers reinforced dense self healing Ceramic Matrix Composites by new short time hybrid processes was studied. The processes developed are based, first, on the deposition of fiber interphase and coating by chemical vapor infiltration, next, on the introduction of ceramic powders into the fibrous preform by Slurry Impregnation and, finally, on the densification of the composite by liquid-phase Spark Plasma Sintering (SPS) or by Reactive Melt Infiltration of silicon (RMI).The homogeneous introduction of the ceramic particles into the multidirectional fiber preforms was realized by slurry impregnation from highly concentrated (> 32 %vol.) and well dispersed aqueous colloid suspensions. The densification of the composites by spark plasma sintering was possible with a short (< 5 minutes) dwelling period in temperature. The chemical degradation of the carbon fibers during the fabrication was prevented by adapting the sintering pressure cycle to inhibit gas evolution inside the system. The composites elaborated are dense. The fully densification of the composites by RMI was realised by carefully controlling the impregnation parameters to avoid to entrap some gaseous species inside the fiber preforms. Our carbon fiber reinforced ceramic matrix composites processed by Spark Plasma Sintering or Reactive Melt Infiltration have a damageable mechanical behaviour with a room temperature bending stress at failure around 300 MPa and have shown their ability to self-healing in oxidizing conditions. Compared to the CMC processed by CVI, the composites processed with a final consolidation step by RMI are fully dense and have a similar room temperature tensile test behaviour with an ultimate tensile stress around 220 MPa.