160 lines
No EOL
4.1 KiB
Text
160 lines
No EOL
4.1 KiB
Text
{
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"metadata": {
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"name": "parallel_vectorize"
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},
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"nbformat": 2,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"Parallel Vectorize",
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"------------------",
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"",
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"The `parallel_vectorize.py` module contains a set of llvmpy code generators",
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"for creating mulithreaded _ufunc_. ",
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"It depends on the new `numpy.fromfunc` for turning arbitrary function pointers into _ufunc_.",
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"",
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"From LLVM Function",
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"------------------",
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"",
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"The `parallel_vectorize_from_func` method generates multithreaded _ufunc_ from LLVM functions.",
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"",
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"First, we will implement a workload function:"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from llvm_cbuilder import *",
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"from llvm_cbuilder import shortnames as C",
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"from llvm.core import *",
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"",
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"# Implement a workload",
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"class Square(CDefinition):",
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" _name_ = 'square'",
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" _retty_ = C.double # 1 output: double",
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" _argtys_ = [('x', C.double)] # 1 input: double",
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" ",
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" def body(self, x):",
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" self.ret(x * x)",
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"",
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"m = Module.new('my_module')",
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"llvm_square = Square()(m) # Generate a llvm function",
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"print(llvm_square) "
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"",
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"define double @square(double %x) {",
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"decl:",
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" %x1 = alloca double",
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" br label %body",
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"",
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"body: ; preds = %decl",
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" store double %x, double* %x1",
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" %0 = load double* %x1",
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" %1 = load double* %x1",
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" %2 = fmul double %0, %1",
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" ret double %2",
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"}",
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""
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]
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}
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],
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"prompt_number": 1
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},
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{
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"cell_type": "markdown",
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"source": [
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"Then, we will generate a _ufunc_ from `llvm_square`:"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from llvm.ee import *",
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"engine = EngineBuilder.new(m).create() # Generate JIT engine",
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"",
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"from parallel_vectorize import parallel_vectorize_from_func",
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"ufunc_square = parallel_vectorize_from_func(llvm_square, engine) # Generate UFunc"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "markdown",
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"source": [
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"We are ready to use `ufunc_square` as a regular _ufunc_."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np",
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"A = np.arange(10., dtype=np.double)",
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"ufunc_square(A)"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 3,
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"text": [
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"array([ 0., 1., 4., 9., 16., 25., 36., 49., 64., 81.])"
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]
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}
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],
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"prompt_number": 3
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},
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{
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"cell_type": "markdown",
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"source": [
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"Here's another example that uses three inputs:"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"class SumOfThree(CDefinition):",
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" _name_ = 'sum.of.three'",
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" _retty_ = C.int",
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" _argtys_ = [('x', C.int),",
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" ('y', C.int),",
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" ('z', C.int)]",
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" def body(self, x, y, z):",
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" self.ret( x + y + z )",
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"",
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"llvm_sum3 = SumOfThree()(m)",
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"ufunc_sum3 = parallel_vectorize_from_func(llvm_sum3, engine)",
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"A = np.arange(10, dtype=np.int32)",
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"B = A * 10",
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"C = A * 100",
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"ufunc_sum3(A, B, C)"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 4,
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"text": [
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"array([ 0, 111, 222, 333, 444, 555, 666, 777, 888, 999], dtype=int32)"
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]
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}
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],
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"prompt_number": 4
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}
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]
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}
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]
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} |