Cache Usage Tutorial - MGNet

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Memory acceleration techniquesInterleaving (independent memory banks storeconsecutive cells of the address space cyclically)• Improves bandwidth• But not latencyCaches (small but fast memory) holding frequentlyused copies of the main memory• Improves latency and bandwidth• Usually comes with 2 or 3 levels nowadays• But only works when access to memory is localProf. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC Cache Aware Methods 14
Principles of locality Temporal locality: an item referenced now will bereferenced again soon Spatial locality: an item referenced now indicates thatneighbors will be referenced soon Cache lines are typically 32-128 bytes with 1024being the longest recently. Lines, not words, aremoved between memory levels. Both principles aresatisfied. There is an optimal line size based on theproperties of the data bus and the memorysubsystem designs.Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC Cache Aware Methods 15
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 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 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
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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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Data access optimizations:Loop spli
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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
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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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