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

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Examining CommitteeJose Nels<strong>on</strong> Amaral, Computing SciencePaul Lu, Computing ScienceBruce Cockburn, Electrical and Computer Engineering
Page 4 and 5: To the never ending advance <strong
Page 6: AcknowledgementsFirst and f
Page 9 and 10: List of Tables2.1
Page 11 and 12: List of acronyms a
Page 13 and 14: Chapter 1IntroductionIn recent year
Page 15 and 16: upon the RV770 architecture. A brie
Page 17 and 18: 3. float : 32-bit floating-point4.
Page 19 and 20: 2.1.1 SIMD unitsEach SIMD unit is c
Page 21 and 22: Figure 2.2: Memory arrangements9
Page 23 and 24: Figure 2.3: CAL programming model11
Page 25 and 26: distinct and independent copies <st
Page 27 and 28: float sum = 0.0;for</strong
Page 29 and 30: Table 2.1: Comparision of</
Page 32 and 33: 3.2 NumPyPython ha
Page 34 and 35: 1 import numpy2 a r r = numpy . z e
Page 36 and 37: Python/NumpyCodewi
Page 38 and 39: Python-C API funct
Page 40 and 41: Chapter 4Compiler
Page 42 and 43: access pattern analysis and GPU cod
Page 44 and 45: Typed AST from unPython</st
Page 46 and 47: 1 for i 1 in prang
Page 48 and 49: Definition 6. ∀(kɛN), the value
Page 50 and 51: Thus we have arrived at a contradic
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The algorithm is very conservative
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strides. In general, ERL has the us
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the smallest set E because any valu
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and t mk where 1 ≤ k ≤ d and 1
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each parallel loop to half its orig
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identify loads that can be coalesce
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Chapter 7Experimental evaluationThi
Page 66 and 67:
GPU code that perfor</stron
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CP is a highly parallel kernel suit
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the element and its 4 neighbors and
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Chapter 8Related WorkThe most impor
Page 74 and 75:
loop-invariant symbolic constants i
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with AMD architecture. Their loop o
Page 78 and 79:
Bibliography[1] Nvidia CUDA (2009-0
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