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Journal articles on the topic 'CELL Broadband Engine'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Shi, Guochun, Volodymyr Kindratenko, Frederico Pratas, Pedro Trancoso, and Michael Gschwind. "Application Acceleration with the Cell Broadband Engine." Computing in Science & Engineering 12, no. 1 (January 2010): 76–81. http://dx.doi.org/10.1109/mcse.2010.4.

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2

Riley, Mack W., and Mike Genden. "Cell Broadband Engine Debugging for Unknown Events." IEEE Design & Test of Computers 24, no. 5 (September 2007): 486–93. http://dx.doi.org/10.1109/mdt.2007.157.

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3

Kurzak, Jakub, and Jack Dongarra. "QR Factorization for the Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 31–42. http://dx.doi.org/10.1155/2009/239720.

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The QR factorization is one of the most important operations in dense linear algebra, offering a numerically stable method for solving linear systems of equations including overdetermined and underdetermined systems. Modern implementations of the QR factorization, such as the one in the LAPACK library, suffer from performance limitations due to the use of matrix–vector type operations in the phase of panel factorization. These limitations can be remedied by using the idea of updating of QR factorization, rendering an algorithm, which is much more scalable and much more suitable for implementation on a multi-core processor. It is demonstrated how the potential of the cell broadband engine can be utilized to the fullest by employing the new algorithmic approach and successfully exploiting the capabilities of the chip in terms of single instruction multiple data parallelism, instruction level parallelism and thread-level parallelism.

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4

de Kruijf, M., and K. Sankaralingam. "MapReduce for the Cell Broadband Engine Architecture." IBM Journal of Research and Development 53, no. 5 (September 2009): 10:1–10:12. http://dx.doi.org/10.1147/jrd.2009.5429076.

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5

Johns, C. R., and D. A. Brokenshire. "Introduction to the Cell Broadband Engine Architecture." IBM Journal of Research and Development 51, no. 5 (September 2007): 503–19. http://dx.doi.org/10.1147/rd.515.0503.

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6

Shimizu, K., H. P. Hofstee, and J. S. Liberty. "Cell Broadband Engine processor vault security architecture." IBM Journal of Research and Development 51, no. 5 (September 2007): 521–28. http://dx.doi.org/10.1147/rd.515.0521.

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7

Riley, M. W., J. D. Warnock, and D. F. Wendel. "Cell Broadband Engine processor: Design and implementation." IBM Journal of Research and Development 51, no. 5 (September 2007): 545–57. http://dx.doi.org/10.1147/rd.515.0545.

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8

MURASE, Masana. "Eliminating Cell Broadband Engine™ DMA Buffer Overflows." IEICE Transactions on Information and Systems E93-D, no. 5 (2010): 1062–69. http://dx.doi.org/10.1587/transinf.e93.d.1062.

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9

Bader, David A., Virat Agarwal, and Seunghwa Kang. "Computing discrete transforms on the Cell Broadband Engine." Parallel Computing 35, no. 3 (March 2009): 119–37. http://dx.doi.org/10.1016/j.parco.2008.12.007.

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10

Sarje, A., and S. Aluru. "Parallel Genomic Alignments on the Cell Broadband Engine." IEEE Transactions on Parallel and Distributed Systems 20, no. 11 (November 2009): 1600–1610. http://dx.doi.org/10.1109/tpds.2008.254.

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11

Gschwind, Michael, Fred Gustavson, and Jan F. Prins. "High Performance Computing with the Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 1–2. http://dx.doi.org/10.1155/2009/979236.

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12

Shi, Guochun, Volodymyr V. Kindratenko, Ivan S. Ufimtsev, Todd J. Martinez, James C. Phillips, and Steven A. Gottlieb. "Implementation of Scientific Computing Applications on the Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 135–51. http://dx.doi.org/10.1155/2009/589561.

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The Cell Broadband Engine architecture is a revolutionary processor architecture well suited for many scientific codes. This paper reports on an effort to implement several traditional high-performance scientific computing applications on the Cell Broadband Engine processor, including molecular dynamics, quantum chromodynamics and quantum chemistry codes. The paper discusses data and code restructuring strategies necessary to adapt the applications to the intrinsic properties of the Cell processor and demonstrates performance improvements achieved on the Cell architecture. It concludes with the lessons learned and provides practical recommendations on optimization techniques that are believed to be most appropriate.

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13

McConnell, Sabine M. "Self-Organizing Maps on the Cell Broadband Engine Architecture." Journal of Physics: Conference Series 256 (November 1, 2010): 012013. http://dx.doi.org/10.1088/1742-6596/256/1/012013.

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14

Xia, Yinglong, and Viktor K. Prasanna. "Parallel exact inference on the Cell Broadband Engine processor." Journal of Parallel and Distributed Computing 70, no. 5 (May 2010): 558–72. http://dx.doi.org/10.1016/j.jpdc.2010.01.008.

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15

Liu, Y., H. Jones, S. Vaidya, M. Perrone, B. Tydlitat, and A. K. Nanda. "Speech recognition systems on the Cell Broadband Engine processor." IBM Journal of Research and Development 51, no. 5 (September 2007): 583–91. http://dx.doi.org/10.1147/rd.515.0583.

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16

Kanase, Padmaja, Ankush Mittal, and Kuldip Singh. "Parallel Singular Value Decomposition Algorithm on Cell Broadband Engine Architecture." i-manager's Journal on Software Engineering 5, no. 2 (December 15, 2010): 16–25. http://dx.doi.org/10.26634/jse.5.2.1331.

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17

Pérez-Miguel, Carlos, Jose Miguel-Alonso, and Alexander Mendiburu. "Porting Estimation of Distribution Algorithms to the Cell Broadband Engine." Parallel Computing 36, no. 10-11 (October 2010): 618–34. http://dx.doi.org/10.1016/j.parco.2010.07.003.

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18

Ismail, Leila, and Driss Guerchi. "Performance Evaluation of Convolution on the Cell Broadband Engine Processor." IEEE Transactions on Parallel and Distributed Systems 22, no. 2 (February 2011): 337–51. http://dx.doi.org/10.1109/tpds.2010.70.

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19

Goldrian, Gottfried, Thomas Huth, Benjamin Krill, Jack Lauritsen, Heiko Schick, Ibrahim Ouda, Simon Heybrock, et al. "QPACE: Quantum Chromodynamics Parallel Computing on the Cell Broadband Engine." Computing in Science & Engineering 10, no. 6 (November 2008): 46–54. http://dx.doi.org/10.1109/mcse.2008.153.

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20

Diaz, Daniel, Salvador Abreu, and Philippe Codognet. "Targeting the Cell Broadband Engine for constraint-based local search." Concurrency and Computation: Practice and Experience 24, no. 6 (October 20, 2011): 647–60. http://dx.doi.org/10.1002/cpe.1855.

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21

Rohrer, J., and L. Gong. "Accelerating 3D nonrigid registration using the Cell Broadband Engine processor." IBM Journal of Research and Development 53, no. 5 (September 2009): 12:1–12:10. http://dx.doi.org/10.1147/jrd.2009.5429078.

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22

Rabie, T., H. K. Kidwai, and F. N. Sibai. "Massive video-surveillance parallelization on the Cell Broadband Engine processor." IBM Journal of Research and Development 54, no. 6 (November 2010): 11:1–11:8. http://dx.doi.org/10.1147/jrd.2010.2074930.

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23

Gschwind, Michael, David Erb, Sid Manning, and Mark Nutter. "An Open Source Environment for Cell Broadband Engine System Software." Computer 40, no. 6 (June 2007): 37–47. http://dx.doi.org/10.1109/mc.2007.192.

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24

Ohara, M., H. Inoue, Y. Sohda, H. Komatsu, and T. Nakatani. "MPI microtask for programming the Cell Broadband Engine™ processor." IBM Systems Journal 45, no. 1 (2006): 85–102. http://dx.doi.org/10.1147/sj.451.0085.

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25

McCullagh, Barry. "Real-time disparity map computation using the cell broadband engine." Journal of Real-Time Image Processing 7, no. 2 (February 23, 2010): 87–93. http://dx.doi.org/10.1007/s11554-010-0155-8.

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26

Sugano, Hiroki, and Ryusuke Miyamoto. "Highly optimized implementation of OpenCV for the Cell Broadband Engine." Computer Vision and Image Understanding 114, no. 11 (November 2010): 1273–81. http://dx.doi.org/10.1016/j.cviu.2010.03.022.

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27

Kim, Jusub, and Joseph JaJa. "Streaming Model Based Volume Ray Casting Implementation for Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 173–84. http://dx.doi.org/10.1155/2009/248465.

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Interactive high quality volume rendering is becoming increasingly more important as the amount of more complex volumetric data steadily grows. While a number of volumetric rendering techniques have been widely used, ray casting has been recognized as an effective approach for generating high quality visualization. However, for most users, the use of ray casting has been limited to datasets that are very small because of its high demands on computational power and memory bandwidth. However the recent introduction of the Cell Broadband Engine (Cell B.E.) processor, which consists of 9 heterogeneous cores designed to handle extremely demanding computations with large streams of data, provides an opportunity to put the ray casting into practical use. In this paper, we introduce an efficient parallel implementation of volume ray casting on the Cell B.E. The implementation is designed to take full advantage of the computational power and memory bandwidth of the Cell B.E. using an intricate orchestration of the ray casting computation on the available heterogeneous resources. Specifically, we introduce streaming model based schemes and techniques to efficiently implement acceleration techniques for ray casting on Cell B.E. In addition to ensuring effective SIMD utilization, our method provides two key benefits: there is no cost for empty space skipping and there is no memory bottleneck on moving volumetric data for processing. Our experimental results show that we can interactively render practical datasets on a single Cell B.E. processor.

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28

Bader, David A., Virat Agarwal, Kamesh Madduri, and Seunghwa Kang. "High performance combinatorial algorithm design on the Cell Broadband Engine processor." Parallel Computing 33, no. 10-11 (November 2007): 720–40. http://dx.doi.org/10.1016/j.parco.2007.09.005.

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29

Sachdeva, Vipin, Michael Kistler, Evan Speight, and Tzy-Hwa Kathy Tzeng. "Exploring the viability of the Cell Broadband Engine for bioinformatics applications." Parallel Computing 34, no. 11 (November 2008): 616–26. http://dx.doi.org/10.1016/j.parco.2008.04.001.

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30

Warnock, J., D. Wendel, T. Aipperspach, E. Behnen, R. A. Cordes, S. H. Dhong, K. Hirairi, et al. "Circuit Design Techniques for a First-Generation Cell Broadband Engine Processor." IEEE Journal of Solid-State Circuits 41, no. 8 (August 2006): 1692–706. http://dx.doi.org/10.1109/jssc.2006.877234.

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31

Johnsson, D., F. Bjarkeson, M. Hell, and F. Hug. "Searching for New Convolutional Codes using the Cell Broadband Engine Architecture." IEEE Communications Letters 15, no. 5 (May 2011): 560–62. http://dx.doi.org/10.1109/lcomm.2011.040111.101624.

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32

Xu, Meilian, Parimala Thulasiraman, and Sima Noghanian. "Microwave tomography for breast cancer detection on Cell broadband engine processors." Journal of Parallel and Distributed Computing 72, no. 9 (September 2012): 1106–16. http://dx.doi.org/10.1016/j.jpdc.2011.10.013.

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33

Biberstein, M., S. Dori-Hacohen, Y. Harel, A. Heilper, B. Mendelson, U. Shvadron, E. Treister, J. Turek, and M. S. Chang. "Cell Broadband Engine processor performance optimization: Tracing tools implementation and use." IBM Journal of Research and Development 53, no. 5 (September 2009): 7:1–7:14. http://dx.doi.org/10.1147/jrd.2009.5429073.

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34

Vishwas, B. C., Abhishek Gadia, and Mainak Chaudhuri. "Implementing a Parallel Matrix Factorization Library on the Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 3–29. http://dx.doi.org/10.1155/2009/710321.

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Matrix factorization (or often called decomposition) is a frequently used kernel in a large number of applications ranging from linear solvers to data clustering and machine learning. The central contribution of this paper is a thorough performance study of four popular matrix factorization techniques, namely, LU, Cholesky, QR and SVD on the STI Cell broadband engine. The paper explores algorithmic as well as implementation challenges related to the Cell chip-multiprocessor and explains how we achieve near-linear speedup on most of the factorization techniques for a range of matrix sizes. For each of the factorization routines, we identify the bottleneck kernels and explain how we have attempted to resolve the bottleneck and to what extent we have been successful. Our implementations, for the largest data sets that we use, running on a two-node 3.2 GHz Cell BladeCenter (exercising a total of sixteen SPEs), on average, deliver 203.9, 284.6, 81.5, 243.9 and 54.0 GFLOPS for dense LU, dense Cholesky, sparse Cholesky, QR and SVD, respectively. The implementations achieve speedup of 11.2, 12.8, 10.6, 13.0 and 6.2, respectively for dense LU, dense Cholesky, sparse Cholesky, QR and SVD, when running on sixteen SPEs. We discuss the interesting interactions that result from parallelization of the factorization routines on a two-node non-uniform memory access (NUMA) Cell Blade cluster.

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35

Wirawan, Adrianto, Bertil Schmidt, Huiliang Zhang, and Chee Keong Kwoh. "High Performance Protein Sequence Database Scanning on the Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 97–111. http://dx.doi.org/10.1155/2009/615038.

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The enormous growth of biological sequence databases has caused bioinformatics to be rapidly moving towards a data-intensive, computational science. As a result, the computational power needed by bioinformatics applications is growing rapidly as well. The recent emergence of low cost parallel multicore accelerator technologies has made it possible to reduce execution times of many bioinformatics applications. In this paper, we demonstrate how the Cell Broadband Engine can be used as a computational platform to accelerate two approaches for protein sequence database scanning: exhaustive and heuristic. We present efficient parallelization techniques for two representative algorithms: the dynamic programming based Smith–Waterman algorithm and the popular BLASTP heuristic. Their implementation on a Playstation®3 leads to significant runtime savings compared to corresponding sequential implementations.

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36

Guofu, Feng, Dong Xiaoshe, Wang Xuhao, Chu Ying, and Zhang Xingjun. "An Efficient Software-Managed Cache Based on Cell Broadband Engine Architecture." International Journal of Distributed Sensor Networks 5, no. 1 (January 2009): 16. http://dx.doi.org/10.1080/15501320802506034.

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37

Chen, T., R. Raghavan, J. N. Dale, and E. Iwata. "Cell Broadband Engine Architecture and its first implementation—A performance view." IBM Journal of Research and Development 51, no. 5 (September 2007): 559–72. http://dx.doi.org/10.1147/rd.515.0559.

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38

Perez, J. M., P. Bellens, R. M. Badia, and J. Labarta. "CellSs: Making it easier to program the Cell Broadband Engine processor." IBM Journal of Research and Development 51, no. 5 (September 2007): 593–604. http://dx.doi.org/10.1147/rd.515.0593.

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39

Chan, Steven C., Phillip J. Restle, Thomas J. Bucelot, John S. Liberty, Stephen Weitzel, John M. Keaty, Brian Flachs, Richard Volant, Peter Kapusta, and Jeffrey S. Zimmerman. "A Resonant Global Clock Distribution for the Cell Broadband Engine Processor." IEEE Journal of Solid-State Circuits 44, no. 1 (January 2009): 64–72. http://dx.doi.org/10.1109/jssc.2008.2007147.

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40

Kidwai, Hashir Karim, Fadi N. Sibai, and Tamer Rabie. "Parallelization and Performance Evaluation of an Edge Detection Algorithm on a Streaming Multi-Core Engine." Journal of Information Technology Research 2, no. 4 (October 2009): 81–91. http://dx.doi.org/10.4018/jitr.2009062906.

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In the world of multi-core processors, the STI Cell Broadband Engine (BE) stands out as a heterogeneous 9-core processor with a PowerPC host processor (PPE) and 8 synergic processor engines (SPEs). The Cell BE architecture is designed to improve upon conventional processors in graphics and related areas by integrating 8 computation engines each with multiple execution units and large register sets to achieve a high performance per area return. In this paper, we discuss the parallelization, implementation and performance evaluation of an edge detection image processing application based on the Roberts edge detector on the Cell BE. The authors report the edge detection performance measured on a computer with one Cell processor and with varying numbers of synergic processor engines enabled. These results are compared to the results obtained on the Cell’s single PPE with all 8 SPEs disabled. The results indicate that edge detection performs 10 times faster on the Cell BE than on modern RISC processors.

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41

Khoury, Raymes, Bernd Burgstaller, and Bernhard Scholz. "Accelerating the Execution of Matrix Languages on the Cell Broadband Engine Architecture." IEEE Transactions on Parallel and Distributed Systems 22, no. 1 (January 2011): 7–21. http://dx.doi.org/10.1109/tpds.2010.58.

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42

Shaffer, Andrew, Bruce Einfalt, and Padma Raghavan. "PFFTC: An improved fast Fourier transform for the IBM cell broadband engine." Procedia Computer Science 1, no. 1 (May 2010): 1045–54. http://dx.doi.org/10.1016/j.procs.2010.04.116.

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43

Ikoma, Norikazu, and Akihiro Asahara. "Real Time Color Object Tracking on Cell Broadband Engine Using Particle Filters." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 3 (April 20, 2010): 272–80. http://dx.doi.org/10.20965/jaciii.2010.p0272.

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Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.

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44

Caulfield, Emmet, and Andreas Hellander. "CellMC—a multiplatform model compiler for the Cell Broadband Engine and ×86." Bioinformatics 26, no. 3 (December 8, 2009): 426–28. http://dx.doi.org/10.1093/bioinformatics/btp662.

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45

Sakamoto, M., and M. Murase. "A parallel implementation of 3D CT image reconstruction on the Cell Broadband Engine." International Journal of Adaptive Control and Signal Processing 24, no. 2 (February 2010): 117–27. http://dx.doi.org/10.1002/acs.1116.

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46

Sibai, F. N., and H. K. Kidwai. "Implementation and performance analysis of parallel conjugate gradient on the Cell Broadband Engine." IBM Journal of Research and Development 54, no. 6 (November 2010): 10:1–10:12. http://dx.doi.org/10.1147/jrd.2010.2071191.

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47

Stürmer, Markus, Jan Götz, Gregor Richter, Arnd Dörfler, and Ulrich Rüde. "Fluid flow simulation on the Cell Broadband Engine using the lattice Boltzmann method." Computers & Mathematics with Applications 58, no. 5 (September 2009): 1062–70. http://dx.doi.org/10.1016/j.camwa.2009.04.006.

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48

Gschwind, Michael. "The Cell Broadband Engine: Exploiting Multiple Levels of Parallelism in a Chip Multiprocessor." International Journal of Parallel Programming 35, no. 3 (April 6, 2007): 233–62. http://dx.doi.org/10.1007/s10766-007-0035-4.

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49

Tseng, Chien Hsun. "Analysis of Parallel Multidimensional Wave Digital Filtering Network on IBM Cell Broadband Engine." Journal of Computational Engineering 2014 (February 17, 2014): 1–13. http://dx.doi.org/10.1155/2014/793635.

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As an alternative approach for the numerical integration of physical systems, the MDWDF technique has become of importance in the field of numerical analysis due to its attractive features, for example, massive parallelism and high accuracy both inherent in nature. In this study, speed-up efficiencies of a MDWDF network are studied for the linearized shallow water system, which plays an important role in fluid dynamics. To achieve the goal, the full parallelism of the MDWDF network is established in the first place based on the chained MD retiming technique. Following the implementation on the IBM Cell Broadband Engine (Cell/BE), excellent performance of the full parallel architecture is revealed. The IBM Cell/BE containing 1 power processor element (PPE) and 8 synergistic processor elements (SPEs) perfectly fits the architecture of the retimed MDWDF model. Empirical results have demonstrated that the full parallelized model with 8 processors (1PPE + 7SPEs) outperforms the other three models: partial right/left-loop retimed models and the full sequential model with 4× improvements for scheduled grids 51×51. In addition, for scheduled fine grids 201×201, the full parallel model is shown to possess significant performance over these models by up to 7× improvements.

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50

Abreu, Salvator, Daniel Diaz, and Philippe Codognet. "Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)." Electronic Proceedings in Theoretical Computer Science 5 (October 8, 2009): 97–111. http://dx.doi.org/10.4204/eptcs.5.8.

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