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Electrical Engineering & Computer Science - University of Kansas Colloquium / 55 Exhaustive Phase Order Search Space Exploration and Evaluation by Prasad Kulkarni (Florida State University)

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 552 Compiler Optimizations To improve efficiency of compiler generated code Optimization phases require enabling conditions need specific patterns in the code many also need available registers Phases interact with each other Applying optimizations in different orders generates different code

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 553 Phase Ordering Problem To find an ordering of optimization phases that produces optimal code with respect to possible phase orderings Evaluating each sequence involves compiling, assembling, linking, execution and verifying results Best optimization phase ordering depends on source application target platform implementation of optimization phases Long standing problem in compiler optimization!!

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 554 Phase Ordering Space Current compilers incorporate numerous different optimization phases 15 distinct phases in our compiler backend 15! = 1,307,674,368,000 Phases can enable each other any phase can be active multiple times 15 15 = 437,893,890,380,859,375 cannot restrict sequence length to 15 15 44 = 5.598 * 10 51

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 555 Addressing Phase Ordering Exhaustive Search universally considered intractable We are now able to exhaustively evaluate the optimization phase order space.

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 556 Re-stating of Phase Ordering Earlier approach explicitly enumerate all possible optimization phase orderings Our approach explicitly enumerate all function instances that can be produced by any combination of phases

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 557 Outline Experimental framework Exhaustive phase order space evaluation Faster conventional compilation Conclusions Summary of my other work Future research directions

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 558 Outline Experimental framework Exhaustive phase order space evaluation Faster conventional compilation Conclusions Summary of my other work Future research directions

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 559 Experimental Framework We used the VPO compilation system established compiler framework, started development in 1988 comparable performance to gcc O2 VPO performs all transformations on a single representation (RTLs), so it is possible to perform most phases in an arbitrary order Experiments use all the 15 re-orderable optimization phases in VPO Target architecture was the StrongARM SA-100 processor

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5510 VPO Optimization Phases IDOptimization PhaseIDOptimization Phase bbranch chaininglloop transformations ccommon subexpr. elim.ncode abstraction dremv. unreachable codeoeval. order determin. gloop unrollingqstrength reduction hdead assignment elim.rreverse branches iblock reorderingsinstruction selection jminimize loop jumpsuremv. useless jumps kregister allocation

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5511 Disclaimers Did not include optimization phases normally associated with compiler front ends no memory hierarchy optimizations no inlining or other interprocedural optimizations Did not vary how phases are applied Did not include optimizations that require profile data

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5512 Benchmarks 12 MiBench benchmarks; 244 functions CategoryProgramDescription auto bitcounttest processor bit manipulation abilities qsortsort strings using quicksort sorting algorithm network dijkstraDijkstras shortest path algorithm patriciaconstruct patricia trie for IP traffic telecomm fftfast fourier transform adpcmcompress 16-bit linear PCM samples to 4-bit consumer jpegimage compression and decompression tiff2bwconvert color.tiff image to b&w image security shasecure hash algorithm blowfishsymmetric block cipher with variable length key office stringsearchsearches for given words in phrases ispellfast spelling checker

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5513 Outline Experimental framework Exhaustive phase order space evaluation Faster conventional compilation Conclusions Summary of my other work Future research directions

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5514 Terminology Active phase An optimization phase that modifies the function representation Dormant phase A phase that is unable to find any opportunity to change the function Function instance any semantically, syntactically, and functionally correct representation of the source function (that can be produced by our compiler)

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5515 Nave Optimization Phase Order Space All combinations of optimization phase sequences are attempted a b c d a bc dadadad bcbcbc L2 L1 L0

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5516 Eliminating Consecutively Applied Phases A phase just applied in our compiler cannot be immediately active again a b c d bc dadada cbbc L2 L1 L0 a bc d

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5517 Eliminating Dormant Phases Get feedback from the compiler indicating if any transformations were successfully applied in a phase. L2 L1 L0 a b c d bc dadad cb a bc

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5518 Identical Function Instances Some optimization phases are independent example: branch chaining & register allocation Different phase sequences can produce the same code r[2] = 1; r[2] = 1; r[3] = r[4] + r[2]; r[3] = r[4] + r[2]; instruction selection r[3] = r[4] + 1; r[3] = r[4] + 1; r[2] = 1; r[2] = 1; r[3] = r[4] + r[2]; r[3] = r[4] + r[2]; constant propagation r[2] = 1; r[2] = 1; r[3] = r[4] + 1; r[3] = r[4] + 1; dead assignment elimination r[3] = r[4] + 1; r[3] = r[4] + 1;

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Electrical Engineering & Computer Science - University of Kansas Colloquium / 5519 Equivalent Function Instances sum = 0; for (i = 0; i < 1000; i++ ) sum += a [ i ]; Source Code r[10]=0; r[12]=HI[a]; r[12]=r[12]+LO[a]; r[1]=r[12]; r[9]=4000+r[12]; L3 r[8]=M[r[1]]; r[10]=r[10]+r[8]; r[1]=r[1]+4; IC=r[1]?r[9]; PC=IC