Cache Usage Tutorial - MGNet

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Implementation detailsOne parameter tuning: usable cache size, which is normally ~60% ofphysical cache size.Current code• Fortran + C (strict spaghetti coding style and fast).• Requires help from authors to add a new domain and coarsegrid.New code• C++• Should not require any author to be present.• Is supposed to be available by end of September and willprobably be done earlier. Aiming for inclusion in ML (seesoftware.sandia.gov)Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC Cache Aware Methods 180
Books W. Briggs, V.E. Henson, S.M. McCormick, A Multigrid Tutorial,SIAM, 2000. D. Bulka, D. Mayhew, Efficient C++, Addison-Wesley, 2000. C.C. Douglas, G. Haase, U. Langer, A Tutorial on Elliptic PDESolvers and Their Parallelization, SIAM, 2003. S. Goedecker, A. Hoisie: Performance Optimization ofNumerically Intensive Codes, SIAM, 2001. J. Handy, The Cache Memory Book, Academic Press, 1998J. L. Hennessy and D. A. Patterson, Computer Architecture: AQuantitative Approach, 2 nd ed., Morgan Kauffmann Publishers,1996.U. Trottenberg, A. Schuller, C. Oosterlee, Multigrid, AcademicPress, 2000.Prof. Craig C. DouglasUniversity of Kentucky andYale UniversityHiPC2003, 12/17/2003 vHPC Cache Aware Methods 181
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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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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
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 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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