Parallel colt: A high-performance java library for scientific computing and image processing

ACM Transactions on Mathematical Software

Volumn 37, Issue 3, 2010, Pages

  Wendykier, Piotr ^a   Nagy, James G ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

Deconvolution; FFT; Inverse problems; Iterative methods; Motion correction; Multithreading; PET; Regularization

Indexed keywords

FFT; MOTION CORRECTION; MULTI-THREADING; PET; REGULARIZATION;

DIFFERENTIAL EQUATIONS; FAST FOURIER TRANSFORMS; IMAGE PROCESSING; IMAGING SYSTEMS; INVERSE PROBLEMS; MULTITASKING; PARALLEL ARCHITECTURES; PROGRAM PROCESSORS; SOFTWARE DESIGN;

ITERATIVE METHODS;

EID: 77957572870     PISSN: 00983500     EISSN: 15577295     Source Type: Journal    
DOI: 10.1145/1824801.1824809     Document Type: Article

References (64)

1. ACCELEREYES. 2009. Jacket. http://accelereyes.com/.
2. AHMED, N.,NATARAJAN, T., AND RAO, K. R. 1974. Discrete cosine transfom. Trans. Comput. C-23, 1, 90-93.
3. AMERDO, B., BODNARTCHOUK, V., CAROMEL, D., DELBÉ, C., HUET, F., AND TABOADA, G. L. 2008. Current state of Java for HPC. Tech. rep. inria-00312039, INRIA.
4. ANDERSON, E., BAI, Z., BISCHOF, C., BLACKFORD, L. S., DEMMEL, J., DONGARRA, J. J., CROZ, J. D., HAMMARLING, S., GREENBAUM, A.,MCKENNEY, A., AND SORENSEN, D. 1999. LAPACK Users' Guide, 3rd Ed. SIAM, Philadelphia, PA.
5. ARNDT, H., BUNDSCHUS, M., AND NäGELE, A. 2009. Towards a next-generation matrix library for Java. In Proceedings of the 33rd Annual IEEE International Computer Software and Applications Conference.
6. BARRETT, R., BERRY, M., CHAN, T. F., DEMMEL, J., DONATO, J., DONGARRA, J., EIJKHOUT, V., POZO, R., ROMINE, C., AND DER VORST, H. V. 1994. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods 2nd Ed. SIAM, Philadelphia, PA.
7. BERTERO, M. AND BOCCACCI, P. 1998. Introduction to Inverse Problems in Imaging. IOP Publishing Ltd., London.
8. BJÖRCK, Å. 1996. Numerical Methods for Least Squares Problems. SIAM, Philadelphia, PA.
9. BLACKFORD, L. S., DEMMEL, J., DONGARRA, J., DUFF, I., HAMMARLING, S., HENRY, G., HEROUX, M., KAUFMAN, L., LUMSDAINE, A., PETITET, A., POZO, R., REMINGTON, K., AND WHALEY, R. C. 2002. An updated set of Basic Linear Algebra Subprograms (BLAS). ACM Trans. Math. Softw. 28, 2, 135-151. (Pubitemid 135701673)
10. BLUESTEIN, L. I. 1968. A linear filtering approach to the computation of the discrete Fourier transform. Northeast Electronics Research and Engineering Meeting Record 10, 218-219.
11. BUTTARI, A., LANGOU, J., KURZAK, J., AND DONGARRA, J. 2007. A class of parallel tiled linear algebra algorithms for multicore architectures. Tech. rep., Innovative Computing Laboratory.
12. BYOUS, J. 2003. Java technology: The early years. http://java.sun.com/ features/1998/05/birthday.html.
13. CHUNG, J., NAGY, J. G., AND O'LEARY, D. P. 2008. A weighted GCV method for Lanczos hybrid regularization. Elec. Trans. Numer. Anal. 28, 149-167.
14. COOLEY, J. W. AND TUKEY, J. W. 1965. An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19, 90, 297-301.
15. DAUTELLE, J.-M. 2007. JScience Project. http://jscience.org/.
16. DAVIS, T. 2009. The University of Florida sparse matrix collection. http://www.cise.ufl.edu/research/sparse/matrices/.
17. DAVIS, T. A. 2006. Direct Methods for Sparse Linear Systems. SIAM, Philadelphia, PA.
18. DOOLIN, D. M., DONGARRA, J., AND SEYMOUR, K. 1999. JLAPACK - Compiling LAPACK Fortran to Java. Sci. Program. 7, 2, 111-138.
19. DOUGHERTY, R. 2005. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming. In Proceedings of the 11th AIAA/CEAS Aeroacoustics Conference.
20. DRA SYSTEMS. 2000. OR-Objects. http://opsresearch.com/OR-Objects/.
21. FABER, T. L., RAGHUNATH, N., TUDORASCU, D., AND VOTAW, J. R. 2009. Motion correction of PET brain images through deconvolution: I. Theoretical development and analysis in software simulations. Phys. Med. Biol. 54, 3, 797-811.
22. FRIGO, M. AND JOHNSON, S. G. 2005. The design and implementation of FFTW3. Proc. IEEE 93, 2, 216-231.
23. FRIGO, M. AND JOHNSON, S. G. 2009. FFT benchmark methodology. http://www.fftw.org/speed/method.html.
24. GOLUB, G. H. AND LOAN, C. F. V. 1996. Matrix Computations (Johns Hopkins Studies in Mathematical Sciences). The Johns Hopkins University Press.
25. GOMEZ, R. 2009. JQuantLib project. http://www.jquantlib.org/index.php/ Main-Page.
26. HANSEN, P. C. 1997. Rank-Deficient and Discrete Ill-Posed Problems. SIAM, Philadelphia, PA.
27. HARTLEY, R. 1942. A more symmetrical Fourier analysis applied to transmission problems. In Proceedings of the Institute of Radio Engineer (IRE).
28. HEIMSUND, B.-O. 2007. Matrix toolkits for Java. http://ressim.berlios.de/ .
29. HENRIKSSON, J. 2009. Endrov project. http://www.endrov.net/index.php/ Main-Page.
30. HICKLIN, J., MOLER, C., WEBB, P., BOISVERT, R. F., MILLER, B., POZO, R., AND REMINGTON, K. 2005. JAMA: A Java matrix package. http://math.nist.gov/ javanumerics/jama/.
31. HOSCHEK, W. 2000. Uniform, versatile and efficient dense and sparse multi-dimensional arrays. http://acs.lbl.gov/%7Ehoschek/publications/ ACMJava2000.pdf.
32. HOSCHEK, W. 2004. Colt project. http://dsd.lbl.gov/%7Ehoschek/colt/index. html.
33. HUDSON, M. AND LARKIN, R. 1994. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imag. 13, 601-609.
34. JCUDA. 2009. JCuda project. http://www.jcuda.org/jcuda/JCuda.html.
35. JUNIT COMMUNITY. 2009. JUnit Project. http://www.junit.org/.
36. KAHAN, W. AND DARCY, J. D. 1998. How Java's floating-point hurts everyone everywhere. http://www.cs.berkeley.edu/%7Ewkahan/JAVAhurt.pdf.
37. LIEBKE, D. 2009. Incanter project. http://wiki.github.com/liebke/ incanter.
38. MENKE, M. A. AND BUCKLEY, K. 1996. Compensation methods for head motion detected during PET imaging. IEEE Trans. Nucl. Sci. 43, 310-317.
39. MCINTOSH, A. R., BOOKSTEIN, F. L., HAXBY, J. V., AND GRADY, C. L. 1996. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3, 143-157.
40. MESSAOUDI, C., BOUDIER, T., SORZANO, C., AND MARCO, S. 2007. TomoJ: Tomography software for three-dimensional reconstruction in transmission electron microscopy. BMC Bioinf. 8, 1, 288.
41. NAGY, J. G., PALMER, K., AND PERRONE, L. 2004. Iterative methods for image deblurring: A MATLAB object-oriented approach. Numer. Algo. 36, 1, 73-93.
42. NAGY, J. G. AND STRAKOŠ, Z. 2000. Enforcing nonnegativity in image reconstruction algorithms. In Proceedings of the Mathematical Modeling, Estimation, and Imaging, D.C. Wilson et al. Ed., Vol.4121. 182-190.
43. NVIDIA CORPORATION. 2009. CUDA zone. http://www.nvidia.com/object/cuda- home.html.
44. OOURA, T. 2006. General purpose FFT (fast Fourier/cosine/sine transform) package. http://www.kurims.kyoto-u.ac.jp/%7Eooura/fft.html.
45. PICARD, Y. AND THOMPSON, C. J. 1997. Motion correction of PET images using multiple acquisition frames. IEEE Trans. Med. Imag. 16, 137-144.
46. RASBAND, W. S. 2009. ImageJ, U.S. National Institutes of Health, Bethesda, MD. http://rsb.info.nih.gov/ij/.
47. SAAD, Y. 1994. ILUT: A dual threshold incomplete ILU factorization. Numer. Linear Algebr. Appl. 1, 387-402.
48. SCHATZMAN, J. C. 1996. Accuracy of the discrete Fourier transform and the fast Fourier transform. SIAM J. Sci. Comput. 17, 5, 1150-1166.
49. STROTHER, S., MCINTOSH, A. R., SOMJI, I., ODER, A., SPRENG, N., WALLER, J., WONG, F., WRIGHT, D., YOURGANOV, G., AND ZHAO, R. 2009. PLS/NPAIRS-J project. http://code.google.com/p/plsnpairs/.
50. STROTHER, S. C., ANDERSON, J., AND HANSEN, L. K. 2002. The quantitative evaluation of functional neuroimaging experiments: The NPAIRS. NeuroImage 15, 4, 747-771.
51. SUN MICROSYSTEMS. 2004. New features and enhancements J2SE 5.0. http://java.sun.com/j2se/1.5.0/docs/relnotes/features.html.
52. SWARZTRAUBER, P. N. 2004. FFTPACK5. http://www.cisl.ucar.edu/css/ software/fftpack5/.
53. VANEK, P.,MANDEL, J., AND BREZINA, M. 1996. Algebraic multigrid based on smoothed aggregation for second and fourth order problems. Comput. 56, 179-196.
54. VERRILL, S. 2009. Nonlinear optimization Java package. http://www1.fpl.fs.fed.us/optimization.html.
55. VOGEL, C. R. 2002. Computational Methods for Inverse Problems. SIAM, Philadelphia, PA.
56. WENDYKIER, P. 2009a. CSparseJ project. http://sites.google.com/site/ piotrwendykier/software/csparsej.
57. WENDYKIER, P. 2009b. JPlasma project. http://sites.google.com/site/ piotrwendykier/software/jplasma.
58. WENDYKIER, P. 2009c. JTransforms project. http://sites.google.com/site/ piotrwendykier/software/jtransforms.
59. WENDYKIER, P. 2009d. Parallel Colt project. http://sites.google.com/site/ piotrwendykier/software/parallelcolt.
60. WENDYKIER, P. 2009e. Parallel HRRT Deconvolution project. http://sites.google.com/site/piotrwendykier/software/deconvolution/ parallelhrrtdeconvolution.
61. WENDYKIER, P. 2009f. Parallel iterative deconvolution project. http://sites.google.com/site/piotrwendykier/software/deconvolution/ paralleliterativedeconvolution.
62. WHALEY, R. C. AND DONGARRA, J. J. 1998. Automatically tuned linear algebra software ATLAS. In Proceedings of Supercomputing Conference.
63. YIP, P. AND RAO, K. R. 1980. A fast computational algorithm for the discrete sine transform. IEEE Trans. Comm. 28, 2, 304-307.
64. ZHANG, B. 2005. Java FFTPack project. http://jfftpack.sourceforge.net/.