Cache Usage Tutorial - MGNet

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Data access optimizations:Loop fusion// Update red and black nodes in pairsfor i= 1 to n-1 dofor j= 1+(i+1)%2 to n-1 by 2 dorelax(u(j,i))relax(u(j,i-1))end forend forProf. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 100
Data access optimizations:Loop fusion// Update black nodes in last grid rowfor j= 2 to n-1 by 2 dorelax(u(j,n-1))end forSolution vector u passes through thecache only once instead of twice per GSiteration!Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 101
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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
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 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
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ExampleDefine a simple vector class
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Example (cont‘d)Need a wrapper cl
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Example (cont‘d)Need overloaded o
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Part IIIOptimization Techniques for
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Is It really unstructured?This is r
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Motivating example• Suppose probl
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Cache aware Gauss-SeidelPreprocessi
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Example of subblock membershipCache
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Standard Gauss-SeidelThe complexity
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Cache boundaries connectedProf. Cra
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Physical boundaries unknownProf. Cr
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Physical boundaries knownProf. Crai
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Physical boundaries knownThe comple
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Preprocessing costsPessimistic (nev
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Numerical experiments: BavariaExper
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Implementation detailsOne parameter
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Journal articles C.C. Douglas, Cach
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Conference proceedingsC. Weiss, W.
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Cache Usage Tutorial - MGNet

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