Réalisez Vos Alimentations Électroniques by Yves Mergy

By Yves Mergy

Lorsqu'un appareil fonctionne mal ou pas du tout, il arrive fréquemment que l'alimentation soit en reason. Les dysfonctionnements occasionnés sont nombreux et variés: surtension, sous-tension, ronflement, parasites, manque de puissance. Ils conduisent parfois à une destruction natural et uncomplicated de ses composants ! C'est pourquoi à chaque circuit doit correspondre une resource d'alimentation adaptée.

Pratique et très illustré, cet ouvrage vous permet de choisir et de réaliser, que vous soyez beginner passionné, étudiant ou électronicien confirmé, l'alimentation dont vous avez besoin. Différentes réalisations sont proposées par ordre croissant de difficulté, les tracés de leurs circuits imprimés sont fournis, ainsi que les directions permettant d'assembler leurs composants. L'ouvrage traite des alimentations avec ou sans transformateur, à commande numérique par microcontrôleur, et à découpage pour élever ou abaisser los angeles rigidity.

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Extra resources for Réalisez Vos Alimentations Électroniques

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The preconditioner matrix M , for our problem, is sparse. We compare our results to the standard Block Incomplete Cholesky preconditioner (for which M = LLT ). We apply the block structure to A and generate L as suggested in [10]. The Block Incomplete Cholesky preconditioners in our results are suffixed with a number like 2n, 4n etc. which denotes the block-size. So for example in a Block-IC preconditioner with blocksize 8n where the matrix −1 . A has N = n × n unknowns the preconditioner will be named like MBlk−IC(8n) Since the data parallelism in Block Incomplete schemes is limited by the blocksize (refer [1] for details) we turn our attention to preconditioners that have more inherent parallelism.

After each step, one column of tiles shifts from the GPUs to the CPUs.                  (a) Task Graph Scheduling (b) Work Partitioning Fig. 1. Scheduling the task graph of the LU factorization, with fine-grained tasks on the critical path being dispatched to individual CPU cores and coarse-grained tasks outside of the critical path being dispatched to GPU devices The implementation relies on a number of state-of-the-art solutions such as: tile data layout, block-cyclic data distribution, parallel recursive panel factorization, GPU kernel autotuning, the technique of lookahead, the use of superscalar scheduling and communication-computation overlapping.

Computed results by ZZ-EFSI on K computer ZZ-HIFU is a code deveeloped by Dr. Okita and others to solve sound propagattion equation to simulate ultra sound in human body for design of cancer treatment shoown in figure 5[4]. Its effectivee performance is about 20 percent using 10,000 nodes oof K computer. Grand Challenge in Life Science on K Computer 21 Fig. 5. Target of ZZ-HIFU UT-Heart is a heart simulation software developed by Prof. Hisada and his group in many years in The University of Tokyo[5].

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