1 Introduction. - 1. 1 Parallel Architectures. - 1. 2 Embedding In A Hypercube. - 1. 3 Performance Measures. - 2 Fundamental Operations. - 2. 1 Data Broadcasting. - 2. 2 Window Broadcast. - 2. 3 Data Sum. - 2. 4 Prefix Sum. - 2. 5 Shift. - 2. 6 Data Circulation. - 2. 7 Even Odd And All Shifts. - 2. 8 Consecutive Sum. - 2. 9 Adjacent Sum. - 2. 10 Data Accumulation. - 2. 11 Rank. - 2. 12 Concentrate. - 2. 13 Distribute. - 2. 14 Generalize. - 2. 15 Sorting. - 2. 16 Random Access Read. - 2. 17 Random Access Write. - 2. 18 BPC Permutations. - 2. 19 Summary. - 3 SIMD Matrix Multiplication. - 3. 1 n3 Processors. - 3. 2 n2 Processors. - 3. 3 n2r 1? r ? n Processors. - 3. 4 r2 1? r ? n Processors. - 3. 5 Summary. - 4 One Dimensional Convolution. - 4. 1 The Problem. - 4. 2 O(M) Memory Algorithms. - 4. 3 O(1) Memory MIMD Algorithm. - 4. 4 O(l) Memory SIMD Algorithm. - 5 Template Matching. - 5. 1 The Problem. - 5. 2 General Square Templates. - 5. 3 Kirsch Motivated Templates. - 5. 4 Medium Grain Template Matching. - 6 Hough Transform. - 6. 1 Introduction. - 6. 2 MIMD Algorithm. - 6. 3 SIMD Algorithms. - 6. 4 NCUBE Algorithms. - 7 Clustering. - 7. 1 Introduction. - 7. 2 NM Processor Algorithms. - 7. 3 Clustering On An NCUBE Hypercube. - 8 Image Transformations. - 8. 1 Introduction. - 8. 2 Shrinking and Expanding. - 8. 3 Translation. - 8. 4 Rotation. - 8. 5 Scaling. - 9 SIMD String Editing. - 9. 1 Introduction. - 9. 2 Dynamic Programming Formulation. - 9. 3 Shared Memory Parallel Algorithm. - 9. 4 SIMD Hypercube Mapping. - References. Language: English