## Parallel Processing and Parallel Algorithms: Theory and ComputationMotivation It is now possible to build powerful single-processor and multiprocessor systems and use them efficiently for data processing, which has seen an explosive ex pansion in many areas of computer science and engineering. One approach to meeting the performance requirements of the applications has been to utilize the most powerful single-processor system that is available. When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. On the other hand, in parallel computation several processors cooperate to solve a problem, which reduces computing time because several operations can be carried out simultaneously. Using several processors that work together on a given computation illustrates a new paradigm in computer problem solving which is completely different from sequential processing. From the practical point of view, this provides sufficient justification to investigate the concept of parallel processing and related issues, such as parallel algorithms. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. The first step is to understand the nature of computations in the specific application domain. |

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### Contents

II | 1 |

III | 2 |

IV | 4 |

V | 6 |

VI | 13 |

VII | 23 |

VIII | 30 |

IX | 35 |

LXXV | 275 |

LXXVI | 284 |

LXXVII | 294 |

LXXVIII | 302 |

LXXIX | 306 |

LXXX | 312 |

LXXXI | 315 |

LXXXII | 319 |

X | 38 |

XI | 39 |

XII | 43 |

XIII | 50 |

XIV | 54 |

XV | 57 |

XVI | 58 |

XVII | 66 |

XVIII | 67 |

XIX | 68 |

XX | 70 |

XXI | 71 |

XXII | 72 |

XXIV | 74 |

XXV | 75 |

XXVI | 76 |

XXVII | 77 |

XXVIII | 79 |

XXIX | 82 |

XXX | 83 |

XXXI | 90 |

XXXII | 100 |

XXXIII | 102 |

XXXIV | 105 |

XXXV | 109 |

XXXVI | 112 |

XXXVII | 113 |

XXXVIII | 117 |

XXXIX | 120 |

XL | 123 |

XLI | 125 |

XLIII | 126 |

XLV | 128 |

XLVI | 129 |

XLVII | 130 |

XLVIII | 131 |

XLIX | 133 |

L | 137 |

LI | 140 |

LII | 149 |

LIII | 165 |

LIV | 177 |

LV | 191 |

LVI | 204 |

LVII | 214 |

LVIII | 217 |

LIX | 220 |

LX | 222 |

LXI | 226 |

LXII | 227 |

LXIII | 228 |

LXIV | 230 |

LXV | 232 |

LXVI | 233 |

LXVII | 234 |

LXIX | 237 |

LXX | 239 |

LXXI | 244 |

LXXII | 253 |

LXXIII | 259 |

LXXIV | 262 |

LXXXIII | 320 |

LXXXIV | 324 |

LXXXV | 329 |

LXXXVI | 333 |

LXXXVII | 336 |

LXXXVIII | 341 |

LXXXIX | 349 |

XC | 351 |

XCI | 355 |

XCII | 357 |

XCIII | 363 |

XCIV | 367 |

XCV | 370 |

XCVI | 374 |

XCVII | 380 |

XCVIII | 390 |

XCIX | 392 |

C | 397 |

CI | 405 |

CII | 407 |

CIII | 411 |

CIV | 412 |

CV | 414 |

CVI | 418 |

CVII | 423 |

CVIII | 425 |

CIX | 429 |

CX | 436 |

CXI | 439 |

CXII | 441 |

CXIII | 453 |

CXIV | 464 |

CXV | 474 |

CXVI | 477 |

CXVII | 480 |

CXVIII | 482 |

CXIX | 483 |

CXX | 486 |

CXXI | 489 |

CXXII | 491 |

CXXIV | 494 |

CXXV | 496 |

CXXVI | 497 |

CXXVII | 501 |

CXXVIII | 502 |

CXXIX | 504 |

CXXX | 505 |

CXXXI | 508 |

CXXXII | 510 |

CXXXIII | 513 |

CXXXIV | 518 |

CXXXV | 524 |

CXXXVI | 527 |

CXXXVII | 531 |

CXXXVIII | 532 |

CXXXIX | 535 |

CXL | 547 |

CXLI | 551 |

555 | |

### Common terms and phrases

adjacency matrix array associated breadth-first search C-Linda called chare color communication compiler concurrent consists control unit created data flow architectures data parallel data type defined depth-first depth-first search distributed edge endfor endif evaluation example execution Fortran functional programming graph G hypercube implementation input instruction integer interconnection network iteration loop machine MasPar matching matrix multiplication meaning memory location mesh message-passing method MIMD MIMD computers minimum spanning tree Modula-2 module multiprocessor node number of processors operating system output parallel algorithm parallel architectures parallel computation Parallel Processing parallel programming partitioning performed pipeline prefix sums primitives problem proc procedure Processing and Parallel processing elements programming language Prolog provides queue recursive represented result sequence sequential shared shared-memory shortest path SIMD solution specify speedup statement step synchronization systolic array task thread tion tuple space utilizing variable vector vertex vertices

### References to this book

Cit 2004: Proceedings Of The 7th International Conference On Information ... Gautam Das,V.P. Gulati No preview available - 2004 |