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

More documents

Recommendations

Info

$Formatting Instructions for Authors Using LaTeX - the Department of ...$

on the texture unit to bring the data first into a register. Second, the texture units runin parallel to the ALUs. There<strong>for</strong>e, to predict per<strong>for</strong>mance, the number <strong>of</strong> cycles requiredto execute the texture instructions and the number <strong>of</strong> cycles required to execute ALUinstructions are calculated separately. The larger <strong>of</strong> the two values represents the bottleneckin the computation.Consider matrix multiplication <strong>of</strong> matrices <strong>of</strong> size N*N. the syntax used in the examplesis very similar to code accepted by the compiler library, calseum, that I wrote.syntax corresponds roughly to C-like code. First, consider a naive code. This code launchesN*N threads each computing one scalar value. Consider the following pseudo-C code thatrepresents the code being executed by one thread.float[] i0;float[] i1;float o0;float sum = 0.0;<strong>for</strong>(int i=0;i
float sum = 0.0;<strong>for</strong>(int i=0;i
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 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 44: and optimizations detailed later in
Page 45 and 46: Chapter 5Array access analysisArray
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?