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Central Processing Unit (central + processing_unit)
Selected AbstractsLearning computer architecture concepts with the FPGA-based "Move" microprocessorCOMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 2 2006Veselko Gu Abstract In this article we introduce the use of a programmable logic device (PLD) in an application-oriented study as an example of designing a microprocessor based on reduced instruction set computer (RISC) architecture. Since the concept of an in-system configurable logic circuit is becoming increasingly popular, we now use it for the purpose of logic design. We suggest that students use PLDs when constructing a central processing unit (CPU) with their own configured functions that are directly implemented in the logic. Such an approach could greatly increase the understanding of the architectural concept of the CPU. © 2006 Wiley Periodicals, Inc. Comput Appl Eng Educ 14: 135,141, 2006; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20072 [source] Designing the microprocessor with Abel-HDLCOMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 2 2001Veselko Gu Abstract In this article we introduce the use of the programmable logic circuit in students' work as an example of configuring the CPU (central processing unit). Since the concept of an in-system configurable logic circuit is becoming increasingly popular, we now use it for the purpose of logic design. We suggest to students that they use programmable logic, as in constructing the CPU, with their own designed functions that are directly implemented in logic. Such an approach could greatly increase the understanding of the architectural concept of the CPU. © 2001 John Wiley & Sons, Inc. Comput Appl Eng Educ 9: 87,92, 2001 [source] A 3-D non-hydrostatic pressure model for small amplitude free surface flowsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 6 2006J. W. Lee Abstract A three-dimensional, non-hydrostatic pressure, numerical model with k,, equations for small amplitude free surface flows is presented. By decomposing the pressure into hydrostatic and non-hydrostatic parts, the numerical model uses an integrated time step with two fractional steps. In the first fractional step the momentum equations are solved without the non-hydrostatic pressure term, using Newton's method in conjunction with the generalized minimal residual (GMRES) method so that most terms can be solved implicitly. This method only needs the product of a Jacobian matrix and a vector rather than the Jacobian matrix itself, limiting the amount of storage and significantly decreasing the overall computational time required. In the second step the pressure,Poisson equation is solved iteratively with a preconditioned linear GMRES method. It is shown that preconditioning reduces the central processing unit (CPU) time dramatically. In order to prevent pressure oscillations which may arise in collocated grid arrangements, transformed velocities are defined at cell faces by interpolating velocities at grid nodes. After the new pressure field is obtained, the intermediate velocities, which are calculated from the previous fractional step, are updated. The newly developed model is verified against analytical solutions, published results, and experimental data, with excellent agreement. Copyright © 2005 John Wiley & Sons, Ltd. [source] Thermal performance of aluminium-foam CPU heat exchangersINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 11 2006H. Mahdi Abstract This study investigates the performance of existing central processing unit (CPU) heat exchangers and compares it with aluminium-foam heat exchangers in natural convection using an industrial set-up. Kapton flexible heaters are used to replicate the heat produced by a computer's CPU. A number of thermocouples are connected between the heater and the heat sink being used to measure the component's temperature. The thermocouples are also connected to a data-acquisition card to collect the data using LabVIEW program. The values obtained for traditional heat exchangers are compared to published data to validate experiments and set-up. The validated set-up was then utilized to test the aluminium-foam heat exchangers and compare its performance to that of common heat sinks. It is found that thermal resistance is reduced more than 70% by employing aluminium-foam CPU heat exchangers. The results demonstrate that this material provides an advantage on thermal dissipation under natural convection over most available technologies, as it considerably increases the surface-area-to-volume ratio. Furthermore, the aluminium-foam heat exchangers reduce the overall weight. Copyright © 2005 John wiley & Sons, Ltd. [source] Compressed sensing MRI with multichannel data using multicore processorsMAGNETIC RESONANCE IN MEDICINE, Issue 4 2010Ching-Hua Chang Abstract Compressed sensing (CS) is a promising method to speed up MRI. Because most clinical MRI scanners are equipped with multichannel receive systems, integrating CS with multichannel systems may not only shorten the scan time but also provide improved image quality. However, significant computation time is required to perform CS reconstruction, whose complexity is scaled by the number of channels. In this article, we propose a reconstruction procedure that uses ubiquitously available multicore central processing unit to accelerate CS reconstruction from multiple channel data. The experimental results show that the reconstruction efficiency benefits significantly from parallelizing the CS reconstructions and pipelining multichannel data into multicore processors. In our experiments, an additional speedup factor of 1.6,2.0 was achieved using the proposed method on a quad-core central processing unit. The proposed method provides a straightforward way to accelerate CS reconstruction with multichannel data for parallel computation. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [source] Multilevel fast multipole algorithm enhanced by GPU parallel technique for electromagnetic scattering problemsMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 3 2010Kan Xu Abstract Along with the development of graphics processing Units (GPUS) in floating point operations and programmability, GPU has increasingly become an attractive alternative to the central processing unit (CPU) for some of compute-intensive and parallel tasks. In this article, the multilevel fast multipole algorithm (MLFMA) combined with graphics hardware acceleration technique is applied to analyze electromagnetic scattering from complex target. Although it is possible to perform scattering simulation of electrically large targets on a personal computer (PC) through the MLFMA, a large CPU time is required for the execution of aggregation, translation, and deaggregation operations. Thus GPU computing technique is used for the parallel processing of MLFMA and a significant speedup of matrix vector product (MVP) can be observed. Following the programming model of compute unified device architecture (CUDA), several kernel functions characterized by the single instruction multiple data (SIMD) mode are abstracted from components of the MLFMA and executed by multiple processors of the GPU. Numerical results demonstrate the efficiency of GPU accelerating technique for the MLFMA. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 502,507, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24963 [source] | ||||||||||