A Compiler for Parallel Exeuction of Numerical Python Programs on ...

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Typed AST from un<strong>Python</strong>Typed ASTTyped ASTArray access analysisLoop optimizationsData transferparametersIL Code generationAMD ILAMD CAL IL compilerDatatransferparams(opt)GPU binary codeData transfer and executionFigure 4.2: Block diagram <strong>of</strong> un<strong>Python</strong> <strong>for</strong> CPU code generation4.3 Final RemarksThis chapter described the design <strong>of</strong> a new compiler system that combines an ahead-<strong>of</strong>-timecompiler, a just-in-time compiler and the AMD CAL IL compiler to create a compilationpath <strong>for</strong> parallel-annotated <strong>Python</strong> numerical code. This compilation infrastructure generatesflexible code that execute either in a GPU or in a multicore CPU.32
Chapter 5Array access analysisArray access analysis is per<strong>for</strong>med by a compiler to compute the set <strong>of</strong> memory locationsthat may be accessed by array references occuring in a loop nest. Jit4GPU per<strong>for</strong>ms arrayaccess analysis to compute the set <strong>of</strong> memory locations accessed in a parallel loop to beexecuted on the GPU. Jit4GPU then computes a one-to-one mapping <strong>of</strong> the set <strong>of</strong> memorylocations accessed in the system memory to a set <strong>of</strong> memory locations on the on-boardmemory <strong>of</strong> the GPU. Jit4GPU also checks if all the memory potentially accessed withinthe loop can fit in the limited on-board memory <strong>of</strong> the GPU. If the data accessed duringcomputation does not fit on the GPU, then jit4GPU attempts to tile the loop. Finally, if thearray access analysis and potential tiling <strong>of</strong> the loop are successful, then jit4GPU generatesdata transfer parameters and GPU binary and executes the loop.This chapter describes a new array access analysis algorithm used by jit4GPU .5.1 Representation and problem statementTo execute a loop nest on the GPU, the compiler first needs to compute the set <strong>of</strong> memorylocations that can potentially be accessed by the loop nest and then transfer the data to theGPU. The compiler also needs to compute the size <strong>of</strong> the set <strong>of</strong> memory locations accessedto determine if the set will fit on the GPU. In this work, the analysis is restricted to loopnests <strong>of</strong> the <strong>for</strong>m shown in Figure 5.1.In Figure 5.1, a loop nest <strong>of</strong> depth d, where the top m loops are parallel, is shown.Variables i 1 to i d above are loop counters while u 1 to u d are upper bounds <strong>of</strong> the loops. Theloop body may contain zero or more array accesses. The set <strong>of</strong> memory locations accessedby an array reference depends on the subscript expression used in the reference, the mapping<strong>of</strong> the subscript expression to memory locations based on strides <strong>of</strong> the array and on theloop bounds.For array access analysis, the compiler requires a <strong>for</strong>mal representation <strong>of</strong> the memoryaccess pattern. Such a representation is a mapping from the d-dimensional space <strong>of</strong> tuples <strong>of</strong>33
Page 3 and 4: Examining CommitteeJose Nelson Amar
Page 5 and 6: AbstractModern Graphics Processing
Page 8 and 9: 5.3.3 Multidimensional RCSLMADs wit
Page 10 and 11: List of Figures2.1
Page 12 and 13: RAMRISCSIMDSPSPMDSSETEXTPUVDVLIWbDE
Page 14 and 15: much lower than equivalent C progra
Page 16 and 17: Chapter 2AMD RV770 architecture <st
Page 18 and 19: Figure 2.1: RV770 Block Diagram. So
Page 20 and 21: 2.2 AMD CAL Programming modelVariou
Page 22 and 23: a context. The predefined names are
Page 24 and 25: 2.3 Hardware execution model2.3.1 P
Page 26 and 27: on the texture unit to bring the da
Page 28 and 29: float4 temp1 = i1[i][threadId.y/4];
Page 30: Chapter 3Python an
Page 33 and 34: Unlike C, NumPy does not impose any
Page 35 and 36: Windows. The module initialization
Page 37 and 38: 3.5.1 Type annotationsType declarat
Page 39 and 40: UnPython generates
Page 41 and 42: Python source code
Page 43: and optimizations detailed later in
Page 47 and 48: LMADs can represent a wide variety
Page 49 and 50: Figure 5.2: Visualization o
Page 51 and 52: Algorithm 1 solves the data transfe
Page 53 and 54: 0 ≤ j < 5 (5.26)In the example, t
Page 55 and 56: case, assume we had a C array start
Page 57 and 58: solve for unknown
Page 59 and 60: approach, the number of</st
Page 61 and 62: Chapter 6Loop transfor</str
Page 63 and 64: Algorithm 6 perfor
Page 65 and 66: with suitable GPU code annotations.
Page 67 and 68: Table 7.3: Execution time f
Page 69 and 70: time and the execution time on the
Page 71 and 72: suite. The serial version o
Page 73 and 74: However Shedskin does not support e
Page 75 and 76: thread. Device functions are writte
Page 77 and 78: Chapter 9ConclusionsThis thesis int
Page 79: [14] Francois Labonte, Peter Mattso

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