Cache Usage Tutorial - MGNet

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ExampleDefine a simple vector class in the beginning:class vector {private:int length;double a[];public:vector(int l);double component(int i) { return a[i]; }...};Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 150
Example (cont‘d)Want to efficiently compute vector sums likec= a+b+d;Efficiently implies Avoiding the generation of temporary objects Pumping data through the memory hierarchyseveral times. This is actually the timeconsumingpart. Moving data is moreexpensive than processing data!Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 151
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Part IArchitectures and Fundamental
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How fast should a solver be?(just a
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Comparison of solvers(what got me s
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PipeliningProf. Craig C. DouglasUni
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CPU trendsEPIC (similar to VLIW) (I
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Memory acceleration techniquesInter
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CachesFast but small extra memoryHo
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Cache issuesUniqueness and transpar
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Effect of cache hit ratioThe cache
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Cache organizationNumber of cache l
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Typical architectures IBM Power 3:
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Basic efficiency guidelinesChoose t
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Choose the best algorithm(cont‘d)
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Sources for librariesVendor-indepen
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Find good compiler optionsModern co
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Use suitable data layoutAccess memo
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ProfilingSubroutine-level profiling
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Profiling: hardware performancecoun
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Profiling tools: valgrindMemory/thr
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Profiling tools: PAPIPAPI = Perform
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hpcview Screen ShotProf. Craig C. D
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HPCToolkit Philosophy 2Provide info
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Our reference code2D structured mul
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Using PCL - Example 1Digital PWS 50
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Using PCL - Example 2PCLinit(&descr
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Examples of DCPI toolsdcpiwhatcg: W
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Using DCPI - Example 2Call the DCPI
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Using DCPI - Example 2Static stalls
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Part IIOptimization TechniquesforSt
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Optimization ofFloating-PointOperat
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Loop unrollingSimplest effect of lo
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Loop unrolling:Improving flop/load
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Fused Multiply-Add (FMA)On many CPU
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Exposing ILP (cont’d)Superscalar
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AliasingArrays (or other data) that
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Aliasing (cont’d)Aliasing is lega
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Eliminating overheads:if statements
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Eliminating loop overheadsFor start
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Eliminating subroutine callingoverh
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Data layout optimizationsArray tran
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Data layout optimizationsStride-1 a
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Data layout optimizations:Cache-awa
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Data layout optimizations:Array pad
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Data layout optimizations:Array pad
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Loop optimizationsLoop unrolling (s
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Data access optimizations:Loop fusi
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Page 100 and 101: Data access optimizations:Loop fusi
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 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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Cache Usage Tutorial - MGNet

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