637 research outputs found
GRAIL β Grid Access and Instrumentation Tool
Since the release of Globus Toolkit 4 Web services enrich the world of Grid Computing. They provide methods to develop modular Grid applications which can be parallelized easily. The access to Web services is mostly solved by complex command line tools which need a good deal of knowledge of the underlaying Grid technologies. GRAIL is intended to fill the gap between existing Grid access methods and both the developer who wants to utilize the Grid for own developments and the user who wants to access the Grid without much additional knowledge. It simplifies the access and the testing of Web services for the Globus Grid middleware. GRAIL provides an easy to use graphical user interface for executing Web services and enables the user to construct complex relationships between services to realize parallel execution. The underlying framework allows an easy integration of any Web service or other arbitrary task without much additional effort for the developer. Existing technologies, shipped with the Globus Toolkit, are seamlessly integrated into GRAIL
ΠΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ°Π½Π½ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ
Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π° ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π³ΡΠ°Π½ΠΈΡ ΠΠ°Π½Π½ΠΈ Π΄Π»Ρ Π±ΡΡΡΡΠΎΠ³ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈ ΠΏΠΎΠΈΡΠΊΠ° ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° Π½Π° ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΈ, ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΏΡΠΈ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ
Analysis of linearized inverse problems in ultrasound transmission imaging
The purpose of this paper is to analyze the linearized inverse problem during the iterativesolution process of the ill-posed nonlinear inverse problem of image reconstruction for ultra-sound transmission imaging. We show that the conjugate gradient applied to normal equation(CGNE) method gives more reliable solutions for linearized systems than Tikhonov regular-ization methods. The linearized systems are more sensitive when treated by CGNE than byTikhonov regularization methods. The Tikhonov regularization is less effective at the be-ginning of the outer-loop iteration, where the nonlinearity is dominating while the conjugategradient for the linearized system stops earlier. Only when the linear approximation is goodenough to describe the whole system, Tikhonov regularization can fully play its role and giveslightly better reconstruction results as compared to CGNE in a very noisy case
Architecture of the Grid Services Toolkit for Process Data Processing
Grid is a rapidly growing new technology that will provide easy access to huge amounts of computer resources, both hardware and software. As these resources become available soon, more and more scientific users are interested in benefiting from them. At this time the main problem accessing the Grid is that scientific users usually need big knowledge of Grid methods and technologies besides their own field of research. To fill the gap between high-level scientific Grid users and low-level functions in Grid environments the Grid Services Toolkit (GST) is developed at the IPE. Aimed to simplify and accelerate the development of parallelized scientific Grid applications, the GST is based on Web services extended by a rich client API. It is especially designed for the field of process data processing providing database access and management, common methods of statistical data analysis and project specific methods
USCT Image Reconstruction: Acceleration using Gauss-Newton Preconditioned Conjugate Gradient
Ultrasound transmission tomography offers quantitative characterization of the tissue or materials by their speed of sound and attenuation. Reconstruction of such images is an inverse problem which is solved iteratively based on a forward model of the Helmholtz equation by paraxial approximation and thus is time-consuming. Hence, developing optimizers that decrease this time, in particular reducing the number of forward propagations is of high relevance in order to bring this technology into clinical practice. In this paper, we solve the inverse problem of reconstruction in a two-level strategy, by an outer and an inner loop. At each iteration of the outer loop, the system is linearized and this linear subproblem is solved in the inner loop with a preconditioned conjugate gradient (CG). A standard Cholesky preconditioning method based on the system matrix is compared with a matrix-free Quasi-Newton update approach, where a preconditioned matrix-vector product is computed at the beginning of every CG iteration. We also use a multigrid scheme with multi-frequency reconstruction to get a convergent rough reconstruction at a lower frequency and then refine it on a higher-resolution grid. The Cholesky preconditioning reduces the number of CG iterations by approx. 70%~85%; but the computation time for determining the system matrix for the Cholesky preconditioner is dominating, offsetting the gains of the reduction of iterations. The matrix-free preconditioning method saves approx. 30% of the computation time on average for single-frequency and multi-frequency reconstruction. For the robust multifrequency reconstruction, we test three breast-like numerical phantoms resulting in a deviation of 0.13 m/s on average in speed of sound reconstruction and a deviation of 5.4% on average in attenuation reconstruction, from the ground truth simulation
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