Cache Usage Tutorial - MGNet

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Choose the best algorithm(cont‘d)For n large, the multigrid method will alwaysoutperform the others, even if it is badlyimplementedFrequently, however, two methods haveapproximately the same complexity, and thenthe better implemented one will winProf. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 28
Use efficient librariesGood libraries often outperform own softwareClever, sophisticated algorithmsOptimized for target machineMachine-specific implementationProf. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 29
Page 4 and 5: Part IArchitectures and Fundamental
Page 6 and 7: How fast should a solver be?(just a
Page 8 and 9: Comparison of solvers(what got me s
Page 10 and 11: PipeliningProf. Craig C. DouglasUni
Page 12 and 13: CPU trendsEPIC (similar to VLIW) (I
Page 14 and 15: Memory acceleration techniquesInter
Page 16 and 17: CachesFast but small extra memoryHo
Page 18 and 19: Cache issuesUniqueness and transpar
Page 20 and 21: Effect of cache hit ratioThe cache
Page 22 and 23: Cache organizationNumber of cache l
Page 24 and 25: Typical architectures IBM Power 3:
Page 26 and 27: Basic efficiency guidelinesChoose t
Page 30 and 31: Sources for librariesVendor-indepen
Page 32 and 33: Find good compiler optionsModern co
Page 34 and 35: Use suitable data layoutAccess memo
Page 36 and 37: ProfilingSubroutine-level profiling
Page 38 and 39: Profiling: hardware performancecoun
Page 40 and 41: Profiling tools: valgrindMemory/thr
Page 42 and 43: Profiling tools: PAPIPAPI = Perform
Page 44 and 45: hpcview Screen ShotProf. Craig C. D
Page 46 and 47: HPCToolkit Philosophy 2Provide info
Page 48 and 49: Our reference code2D structured mul
Page 50 and 51: Using PCL - Example 1Digital PWS 50
Page 52 and 53: Using PCL - Example 2PCLinit(&descr
Page 54 and 55: Examples of DCPI toolsdcpiwhatcg: W
Page 56 and 57: Using DCPI - Example 2Call the DCPI
Page 58 and 59: Using DCPI - Example 2Static stalls
Page 60 and 61: Part IIOptimization TechniquesforSt
Page 62 and 63: Optimization ofFloating-PointOperat
Page 64 and 65: Loop unrollingSimplest effect of lo
Page 66 and 67: Loop unrolling:Improving flop/load
Page 68 and 69: Fused Multiply-Add (FMA)On many CPU
Page 70 and 71: Exposing ILP (cont’d)Superscalar
Page 72 and 73: AliasingArrays (or other data) that
Page 74 and 75: Aliasing (cont’d)Aliasing is lega
Page 76 and 77: Eliminating overheads:if statements
Page 78 and 79:
Eliminating loop overheadsFor start
Page 80 and 81:
Eliminating subroutine callingoverh
Page 82 and 83:
Data layout optimizationsArray tran
Page 84 and 85:
Data layout optimizationsStride-1 a
Page 86 and 87:
Data layout optimizations:Cache-awa
Page 88 and 89:
Data layout optimizations:Array pad
Page 90 and 91:
Data layout optimizations:Array pad
Page 92 and 93:
Loop optimizationsLoop unrolling (s
Page 94 and 95:
Data access optimizations:Loop fusi
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Data access optimizations:Loop spli
Page 104 and 105:
Data access optimizations:Loop bloc
Page 106 and 107:
Data access optimizations:Loop bloc
Page 108 and 109:
Data access optimizationsLoop block
Page 110 and 111:
Two common multigridalgorithmsV Cyc
Page 112 and 113:
DiMEPACK libraryC++ interface, fast
Page 114 and 115:
Example:Cache-OptimizedMultigrid on
Page 116 and 117:
Data layout optimizationsfor 3D mul
Page 118 and 119:
Data layout optimizationsfor 3D mul
Page 120 and 121:
Data access optimizationsfor 3D mul
Page 122 and 123:
Example:Cache Optimizations forthe
Page 124 and 125:
LBM (cont‘d)Stream: read distribu
Page 126 and 127:
Layout 2: Grid Compression: save me
Page 128 and 129:
3-way blocking (cont’d):Prof. Cra
Page 130 and 131:
4-way blocking (cont’d):Prof. Cra
Page 132 and 133:
Layout: Grid compression, access pa
Page 134 and 135:
Performance resultsMFLOPS for 2D GS
Page 136 and 137:
Memory access behaviorStandard impl
Page 138 and 139:
Memory access behavior2D skewed blo
Page 140 and 141:
Performance results (cont’d)2D LB
Page 142 and 143:
Performance results (cont’d)3D LB
Page 144 and 145:
C++-specific considerationsWe will
Page 146 and 147:
Inlining (cont‘d)Advantages:• R
Page 148 and 149:
Inlining virtual functionsVirtual f
Page 150 and 151:
ExampleDefine a simple vector class
Page 152 and 153:
Example (cont‘d)Need a wrapper cl
Page 154 and 155:
Example (cont‘d)Need overloaded o
Page 156 and 157:
Part IIIOptimization Techniques for
Page 158 and 159:
Is It really unstructured?This is r
Page 160 and 161:
Motivating example• Suppose probl
Page 162 and 163:
Cache aware Gauss-SeidelPreprocessi
Page 164 and 165:
Example of subblock membershipCache
Page 166 and 167:
Standard Gauss-SeidelThe complexity
Page 168 and 169:
Cache boundaries connectedProf. Cra
Page 170 and 171:
Physical boundaries unknownProf. Cr
Page 172 and 173:
Physical boundaries knownProf. Crai
Page 174 and 175:
Physical boundaries knownThe comple
Page 176 and 177:
Preprocessing costsPessimistic (nev
Page 178 and 179:
Numerical experiments: BavariaExper
Page 180 and 181:
Implementation detailsOne parameter
Page 182 and 183:
Journal articles C.C. Douglas, Cach
Page 184 and 185:
Conference proceedingsC. Weiss, W.
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