Cache Usage Tutorial - MGNet

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Using PCL – Example 2PCLinit(&descr);if(PCLquery(descr,counter_list,2,flags)!=PCL_SUCCESS)// Issue error message …else {PCL_start(descr, counter_list, 2, flags);// Do computational work here …PCLstop(descr,i_result,fp_result,2);printf(“%i fp instructions, MFLOPS: %f\n”,i_result[0], fp_result[1]);}PCLexit(descr);return 0;}Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 52
Using DCPIAlpha-based machines running Tru64 UNIXHow to proceed when using DCPI1. Start the DCPI daemon (dcpid)2. Run your code3. Stop the DCPI daemon4. Use DCPI tools to analyze the profilingdataProf. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC <strong>Cache</strong> Aware Methods 53
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 28 and 29: Choose the best algorithm(cont‘d)
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 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
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Data access optimizations:Loop bloc
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Data access optimizationsLoop block
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Two common multigridalgorithmsV Cyc
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DiMEPACK libraryC++ interface, fast
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Example:Cache-OptimizedMultigrid on
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Data layout optimizationsfor 3D mul
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Data layout optimizationsfor 3D mul
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Data access optimizationsfor 3D mul
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Example:Cache Optimizations forthe
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LBM (cont‘d)Stream: read distribu
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Layout 2: Grid Compression: save me
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3-way blocking (cont’d):Prof. Cra
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4-way blocking (cont’d):Prof. Cra
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Layout: Grid compression, access pa
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Performance resultsMFLOPS for 2D GS
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Memory access behaviorStandard impl
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Memory access behavior2D skewed blo
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Performance results (cont’d)2D LB
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Performance results (cont’d)3D LB
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C++-specific considerationsWe will
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Inlining (cont‘d)Advantages:• R
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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
Page 166 and 167:
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
Page 174 and 175:
Physical boundaries knownThe comple
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Preprocessing costsPessimistic (nev
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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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