The Experts below are selected from a list of 1560 Experts worldwide ranked by ideXlab platform
W F J Muller - One of the best experts on this subject based on the ideXlab platform.
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Fast SIMDized Kalman filter based track fit
2011Co-Authors: S Gorbunov, U Kebschull, I Kisel, Volker Lindenstruth, W F J MullerAbstract:Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman lter. Therefore, the speed of Kalman lter based algorithms is of crucial importance in on-line data processing. This is especially true for the combinatorial track nding stage where the Kalman lter based track t is used very intensively. Therefore, developing fast reconstruction algorithms, which use maximum available power of processors, is important, in particular for initial selection of events interesting for the new physics. One of such powerful feature supported by almost all up-to-date PC processors is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. A novel Cell processor extends the parallelization further combining a general-purpose PowerPC processor core with eight streamlined coprocessing elements which greatly accelerate vector processing applications. In the investigation described here, after a signi cant memory optimization and a comprehensive numerical analysis, the Kalman lter based track tting algorithm of the CBM experiment has been vectorized using inline Operator Overloading. Thus the algorithm continues to be exible with respect to any CPU family used for data reconstruction. Because of all these changes the simdized Kalman lter based track tting algorithm takes 1 µs per track that is 10000 times faster than the initial version. Porting the algorithm to a Cell Blade computer gives another factor of 10 of the speedup. Finally, we compare performance of the tracking algorithm running on three di erent CPU architectures: Intel Xeon, AMD Opteron and Cell Broadband Engine.
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FAIR-EXPERIMENTS-08 Fast SIMDized Kalman filter based track fit ∗
2010Co-Authors: I Kisel, S Gorbunov, U Kebschull, Volker Lindenstruth, W F J MullerAbstract:The core of data reconstruction in high energy physics is the Kalman filter. Therefore, the development of fast Kalman filter based reconstruction algorithms, making maximal use of the available processors power, is of utmost importance. A powerful feature supported by almost all upto-date PC processors is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. A novel Cell processor extends the parallelization further combining a general-purpose PowerPC processor core with 8 streamlined coprocessing elements (SPEs) which greatly accelerate vector-processing applications. In our investigation, after a significant memory optimization and a comprehensive numerical analysis, the Kalman filter based track fitting algorithm of the CBM experiment has been vectorized using inline Operator Overloading [1]. Thus the algorithm continues to be flexible with respect to any CPU family used for data reconstruction. Stage Time/track Speedup Initial scalar version 12 ms — Approximation of the field 240 µs 5
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fast simdized kalman filter based track fit
Computer Physics Communications, 2008Co-Authors: S Gorbunov, U Kebschull, I Kisel, Volker Lindenstruth, W F J MullerAbstract:Abstract Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of many data reconstruction algorithms in high energy physics is the Kalman filter. Therefore, the speed of Kalman filter based algorithms is of crucial importance in on-line data processing. This is especially true for the combinatorial track finding stage where the Kalman filter based track fit is used very intensively. Therefore, developing fast reconstruction algorithms, which use maximum available power of processors, is important, in particular for the initial selection of events which carry signals of interesting physics. One of such powerful feature supported by almost all up-to-date PC processors is a SIMD instruction set, which allows packing several data items in one register and to operate on all of them, thus achieving more operations per clock cycle. The novel Cell processor extends the parallelization further by combining a general-purpose PowerPC processor core with eight streamlined coprocessing elements which greatly accelerate vector processing applications. In the investigation described here, after a significant memory optimization and a comprehensive numerical analysis, the Kalman filter based track fitting algorithm of the CBM experiment has been vectorized using inline Operator Overloading. Thus the algorithm continues to be flexible with respect to any CPU family used for data reconstruction. Because of all these changes the SIMDized Kalman filter based track fitting algorithm takes 1 μs per track that is 10000 times faster than the initial version. Porting the algorithm to a Cell Blade computer gives another factor of 10 of the speedup. Finally, we compare performance of the tracking algorithm running on three different CPU architectures: Intel Xeon, AMD Opteron and Cell Broadband Engine.
Richard Saurel - One of the best experts on this subject based on the ideXlab platform.
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automatic differentiation using Operator Overloading adoo for implicit resolution of hyperbolic single phase and two phase flow models
Journal of Computational Physics, 2019Co-Authors: Francois Fraysse, Richard SaurelAbstract:Abstract Implicit time integration schemes are widely used in computational fluid dynamics to speed-up computations. Indeed, implicit schemes usually allow for less stringent time-step stability constraints than their explicit counterpart. The derivation of an implicit scheme is however a challenging and time-consuming task, increasing substantially with the model equations complexity since this method usually requires fairly accurate evaluation of the spatial scheme's matrix Jacobian. This article presents a flexible method to overcome the difficulties associated to the computation of the derivatives, based on the forward mode of automatic differentiation using Operator Overloading (ADOO). Flexibility and simplicity of the method are illustrated through implicit resolution of various flow models of increasing complexity such as the compressible Euler equations, a two-phase flow model in full equilibrium [28] and a symmetric variant [44] of the two-phase flow model of Baer and Nunziato [2] dealing with mixtures in total disequilibrium.
I Kisel - One of the best experts on this subject based on the ideXlab platform.
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Fast SIMDized Kalman filter based track fit
2011Co-Authors: S Gorbunov, U Kebschull, I Kisel, Volker Lindenstruth, W F J MullerAbstract:Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman lter. Therefore, the speed of Kalman lter based algorithms is of crucial importance in on-line data processing. This is especially true for the combinatorial track nding stage where the Kalman lter based track t is used very intensively. Therefore, developing fast reconstruction algorithms, which use maximum available power of processors, is important, in particular for initial selection of events interesting for the new physics. One of such powerful feature supported by almost all up-to-date PC processors is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. A novel Cell processor extends the parallelization further combining a general-purpose PowerPC processor core with eight streamlined coprocessing elements which greatly accelerate vector processing applications. In the investigation described here, after a signi cant memory optimization and a comprehensive numerical analysis, the Kalman lter based track tting algorithm of the CBM experiment has been vectorized using inline Operator Overloading. Thus the algorithm continues to be exible with respect to any CPU family used for data reconstruction. Because of all these changes the simdized Kalman lter based track tting algorithm takes 1 µs per track that is 10000 times faster than the initial version. Porting the algorithm to a Cell Blade computer gives another factor of 10 of the speedup. Finally, we compare performance of the tracking algorithm running on three di erent CPU architectures: Intel Xeon, AMD Opteron and Cell Broadband Engine.
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FAIR-EXPERIMENTS-08 Fast SIMDized Kalman filter based track fit ∗
2010Co-Authors: I Kisel, S Gorbunov, U Kebschull, Volker Lindenstruth, W F J MullerAbstract:The core of data reconstruction in high energy physics is the Kalman filter. Therefore, the development of fast Kalman filter based reconstruction algorithms, making maximal use of the available processors power, is of utmost importance. A powerful feature supported by almost all upto-date PC processors is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. A novel Cell processor extends the parallelization further combining a general-purpose PowerPC processor core with 8 streamlined coprocessing elements (SPEs) which greatly accelerate vector-processing applications. In our investigation, after a significant memory optimization and a comprehensive numerical analysis, the Kalman filter based track fitting algorithm of the CBM experiment has been vectorized using inline Operator Overloading [1]. Thus the algorithm continues to be flexible with respect to any CPU family used for data reconstruction. Stage Time/track Speedup Initial scalar version 12 ms — Approximation of the field 240 µs 5
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fast simdized kalman filter based track fit
Computer Physics Communications, 2008Co-Authors: S Gorbunov, U Kebschull, I Kisel, Volker Lindenstruth, W F J MullerAbstract:Abstract Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of many data reconstruction algorithms in high energy physics is the Kalman filter. Therefore, the speed of Kalman filter based algorithms is of crucial importance in on-line data processing. This is especially true for the combinatorial track finding stage where the Kalman filter based track fit is used very intensively. Therefore, developing fast reconstruction algorithms, which use maximum available power of processors, is important, in particular for the initial selection of events which carry signals of interesting physics. One of such powerful feature supported by almost all up-to-date PC processors is a SIMD instruction set, which allows packing several data items in one register and to operate on all of them, thus achieving more operations per clock cycle. The novel Cell processor extends the parallelization further by combining a general-purpose PowerPC processor core with eight streamlined coprocessing elements which greatly accelerate vector processing applications. In the investigation described here, after a significant memory optimization and a comprehensive numerical analysis, the Kalman filter based track fitting algorithm of the CBM experiment has been vectorized using inline Operator Overloading. Thus the algorithm continues to be flexible with respect to any CPU family used for data reconstruction. Because of all these changes the SIMDized Kalman filter based track fitting algorithm takes 1 μs per track that is 10000 times faster than the initial version. Porting the algorithm to a Cell Blade computer gives another factor of 10 of the speedup. Finally, we compare performance of the tracking algorithm running on three different CPU architectures: Intel Xeon, AMD Opteron and Cell Broadband Engine.
Joaquim R. R. A. Martins - One of the best experts on this subject based on the ideXlab platform.
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Structural and Multidisciplinary Optimization manuscript No. (will be inserted by the editor) pyOpt: A Python-Based Object-Oriented Framework for Nonlinear Constrained Optimization
2015Co-Authors: Ruben E. Perez, Peter W. Jansen, Joaquim R. R. A. MartinsAbstract:Abstract We present pyOpt, an object-oriented frame-work for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses object-oriented concepts, such as class inheritance and Operator Overloading, to maintain a distinct separation between the problem for-mulation and the optimization approach used to solve the problem. This creates a common interface in a flex-ible environment where both practitioners and devel-opers alike can solve their optimization problems or develop and benchmark their own optimization algo-rithms. The framework is developed in the Python pro-gramming language, which allows for easy integration of optimization software that is programmed in Fortran, C, C++, and other languages. A variety of optimization algorithms are integrated in pyOpt and are accessible through the common interface. We solve a number of problems of increasing complexity to demonstrate how a given problem is formulated using this framework, and how the framework can be used to benchmark the various optimization algorithms
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pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization
Structural and Multidisciplinary Optimization, 2012Co-Authors: Ruben E. Perez, Peter W. Jansen, Joaquim R. R. A. MartinsAbstract:We present pyOpt, an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses object-oriented concepts, such as class inheritance and Operator Overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem. This creates a common interface in a flexible environment where both practitioners and developers alike can solve their optimization problems or develop and benchmark their own optimization algorithms. The framework is developed in the Python programming language, which allows for easy integration of optimization software programmed in Fortran, C, C+ +, and other languages. A variety of optimization algorithms are integrated in pyOpt and are accessible through the common interface. We solve a number of problems of increasing complexity to demonstrate how a given problem is formulated using this framework, and how the framework can be used to benchmark the various optimization algorithms.
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pymdo an object oriented framework for multidisciplinary design optimization
ACM Transactions on Mathematical Software, 2009Co-Authors: Joaquim R. R. A. Martins, Christopher Marriage, Nathan P TedfordAbstract:We present pyMDO, an object-oriented framework that facilitates the usage and development of algorithms for multidisciplinary optimization (MDO). The resulting implementation of the MDO methods is efficient and portable. The main advantage of the proposed framework is that it is flexible, with a strong emphasis on object-oriented classes and Operator Overloading, and it is therefore useful for the rapid development and evaluation of new MDO methods. The top layer interface is programmed in Python and it allows for the layers below the interface to be programmed in C, C++, Fortran, and other languages. We describe an implementation of pyMDO and demonstrate that we can take advantage of object-oriented programming to obtain intuitive, easy-to-read, and easy-to-develop codes that are at the same time efficient. This allows developers to focus on the new algorithms they are developing and testing, rather than on implementation details. Examples demonstrate the user interface and the corresponding results show that the various MDO methods yield the correct solutions.
Gilberto Perez - One of the best experts on this subject based on the ideXlab platform.
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an approach to computing discrete adjoints for mpi parallelized models applied to ice sheet system model 4 11
Geoscientific Model Development, 2016Co-Authors: Eric Larour, Jean Utke, Anton Bovin, Mathieu Morlighem, Gilberto PerezAbstract:Abstract. Within the framework of sea-level rise projections, there is a strong need for hindcast validation of the evolution of polar ice sheets in a way that tightly matches observational records (from radar, gravity, and altimetry observations mainly). However, the computational requirements for making hindcast reconstructions possible are severe and rely mainly on the evaluation of the adjoint state of transient ice-flow models. Here, we look at the computation of adjoints in the context of the NASA/JPL/UCI Ice Sheet System Model (ISSM), written in C++ and designed for parallel execution with MPI. We present the adaptations required in the way the software is designed and written, but also generic adaptations in the tools facilitating the adjoint computations. We concentrate on the use of Operator Overloading coupled with the AdjoinableMPI library to achieve the adjoint computation of the ISSM. We present a comprehensive approach to (1) carry out type changing through the ISSM, hence facilitating Operator Overloading, (2) bind to external solvers such as MUMPS and GSL-LU, and (3) handle MPI-based parallelism to scale the capability. We demonstrate the success of the approach by computing sensitivities of hindcast metrics such as the misfit to observed records of surface altimetry on the northeastern Greenland Ice Stream, or the misfit to observed records of surface velocities on Upernavik Glacier, central West Greenland. We also provide metrics for the scalability of the approach, and the expected performance. This approach has the potential to enable a new generation of hindcast-validated projections that make full use of the wealth of datasets currently being collected, or already collected, in Greenland and Antarctica.