基准测试JS和Python之间的性能差异

问题描述 投票：2回答：2

到目前为止，我们有一个应用程序专门保留在前端，但是在使用大型网格配置进行测试时，我们遇到了几个处理和内存限制。因此，我们正在探索将资源需求任务推向后端的选项。

因此，我目前正在运行一些基准测试，以获得我们可能会看到的性能差异的一些迹象。更具体地说，鉴于性能瓶颈的很大一部分是嵌套循环，我在js和python中编写了一个简单的代码片段来测试处理嵌套循环和构建不同大小的数组的性能。

令我惊讶的是，无论我测试过什么样的排列，看起来js总是更快。

JS片段：

var timeTotal = 0;
var benchmarkTests = 100;
var testSizes = [50, 100, 500, 1000, 3000];

for (var a = 0; a < testSizes.length; a++) {
  var minX = 0;
  var maxX = testSizes[a];
  var minY = 0;
  var maxY = testSizes[a];
  var cellDimensionsX = 0.991; // cell width
  var cellDimensionsY = 1.652; // cell height
  var cellDimensionsXHalf = cellDimensionsX / 2; // half cell width
  var cellDimensionsYHalf = cellDimensionsY / 2; // half cell height

  var maxCellsXCount = Math.floor(maxX / cellDimensionsX);
  var maxCellsYCount = Math.floor(maxY / cellDimensionsY);

  console.log("X:", maxCellsXCount, "| Y", maxCellsYCount, "| Total", maxCellsXCount * maxCellsYCount);

  for (var k = 0; k < benchmarkTests; k++) {
    var cellCoordsTime = new Date().getTime();

    var cellCoords = {};
    var index = 0;

    for (var i = 0; i < maxCellsXCount; i++) {
      var xCoord = (i * cellDimensionsX) + cellDimensionsXHalf;

      for (var d = 0; d < maxCellsYCount; d++) {
        cellCoords[index] = {
          x: xCoord,
          y: (d * cellDimensionsY) + cellDimensionsYHalf
        };
        index++;
      }
    }

    var thisTime = new Date().getTime() - cellCoordsTime;
    timeTotal += thisTime;
    // console.log('cellCoords', thisTime, 'for grid with', index, 'cells');
  }
  console.log('Testing with a', testSizes[a], '*', testSizes[a], 'grid area. Total time', timeTotal, '. Avg', (timeTotal / benchmarkTests), 'for', benchmarkTests, 'tests');
}

Python代码段：

#!/usr/bin/python
import time

timeTotal = 0
benchmarkTests = 100

testSizes = [50, 100, 500, 1000, 3000]

for a in range(len(testSizes)):
    minX = 0
    maxX = testSizes[a]
    minY = 0
    maxY = testSizes[a]
    cellDimensionsX = 0.991 # cell width
    cellDimensionsY = 1.652 # cell height
    cellDimensionsXHalf = cellDimensionsX / 2 # half cell width
    cellDimensionsYHalf = cellDimensionsY / 2 # half cell height

    maxCellsXCount = int(maxX / cellDimensionsX)
    maxCellsYCount = int(maxY / cellDimensionsY)

    print("X: %s | Y %s | Total %s" % (maxCellsXCount, maxCellsYCount, maxCellsXCount * maxCellsYCount))

    for k in range(benchmarkTests):
        start = time.time()
        cellCoords = {}
        index = 0

        for i in range(maxCellsXCount):
            xCoord = (i * cellDimensionsX) + cellDimensionsXHalf
            for d in range(maxCellsYCount):
                cellCoords[index] = {'x': xCoord, 'y': (d * cellDimensionsY) + cellDimensionsYHalf}
                index += 1

        thisTime = (time.time() - start) * 1000;
        timeTotal = timeTotal + thisTime;
        # print("Elapsed Time: %s for grid with %s cells" % (thisTime, index))

    print("Testing with a %s*%s grid area. Total time %s. Avg %s for %s tests" % (testSizes[a], testSizes[a], timeTotal, (timeTotal / benchmarkTests), benchmarkTests))

运行这些时，我得到：

JS：

Testing with a 50 * 50 grid area. Total time 26 . Avg 0.26 for 100 tests
Testing with a 100 * 100 grid area. Total time 85 . Avg 0.85 for 100 tests
Testing with a 500 * 500 grid area. Total time 4539 . Avg 45.39 for 100 tests
Testing with a 1000 * 1000 grid area. Total time 23160 . Avg 231.6 for 100 tests
Testing with a 3000 * 3000 grid area. Total time 243760 . Avg 2437.6 for 100 tests

蟒蛇：

Testing with a 50*50 grid area. Total time 50.8642196655. Avg 0.508642196655 for 100 tests
Testing with a 100*100 grid area. Total time 262.931108475. Avg 2.62931108475 for 100 tests
Testing with a 500*500 grid area. Total time 6338.83333206. Avg 63.3883333206 for 100 tests
Testing with a 1000*1000 grid area. Total time 30769.4478035. Avg 307.694478035 for 100 tests
Testing with a 3000*3000 grid area. Total time 304995.391846. Avg 3049.95391846 for 100 tests

所有时间都以ms为单位，所有测试都在同一个localhost上运行。

我原本期望python比js快得多。有什么我想念的吗？

更新＃1（将所有内容移动到一个函数中|整体增益~17％）：

Testing with a 50*50 grid area. Total time 41.2473678589. Avg 0.412473678589 for 100 tests
Testing with a 100*100 grid area. Total time 174.555540085. Avg 1.74555540085 for 100 tests
Testing with a 500*500 grid area. Total time 5617.09475517. Avg 56.1709475517 for 100 tests
Testing with a 1000*1000 grid area. Total time 21199.390173. Avg 211.99390173 for 100 tests
Testing with a 3000*3000 grid area. Total time 255921.251535. Avg 2559.21251535 for 100 tests

更新＃2（xrange的交换范围|更新后的总体附加增益＃1~15％|与初始代码相比的总增益~30％）：

Testing with a 50*50 grid area. Total time 38.7289524078. Avg 0.387289524078 for 100 tests
Testing with a 100*100 grid area. Total time 176.453590393. Avg 1.76453590393 for 100 tests
Testing with a 500*500 grid area. Total time 5346.49443626. Avg 53.4649443626 for 100 tests
Testing with a 1000*1000 grid area. Total time 21618.1008816. Avg 216.181008816 for 100 tests
Testing with a 3000*3000 grid area. Total time 213622.769356. Avg 2136.22769356 for 100 tests

更新＃3（列表的交换字典|更新后的总体额外增益＃2~35％|与初始代码相比的总增益~55％）：

Testing with a 50*50 grid area. Total time 20.7185745239. Avg 0.207185745239 for 100 tests
Testing with a 100*100 grid area. Total time 100.9953022. Avg 1.009953022 for 100 tests
Testing with a 500*500 grid area. Total time 3033.61153603. Avg 30.3361153603 for 100 tests
Testing with a 1000*1000 grid area. Total time 12399.708271. Avg 123.99708271 for 100 tests
Testing with a 3000*3000 grid area. Total time 140118.921518. Avg 1401.18921518 for 100 tests

将JS与Python的UPDATE＃3匹配（数组的交换对象|与初始代码相比的总损失~165％）：

Testing with a 50 * 50 grid area. Total time 30 . Avg 0.3 for 100 tests
Testing with a 100 * 100 grid area. Total time 48 . Avg 0.48 for 100 tests
Testing with a 500 * 500 grid area. Total time 12694 . Avg 126.94 for 100 tests
Testing with a 1000 * 1000 grid area. Total time 81402 . Avg 814.02 for 100 tests
Testing with a 3000 * 3000 grid area. Total time 625615 . Avg 6256.15 for 100 tests

更新＃4（将Cython拉入战斗中|更新后的总体额外增益＃3~26％|总体增益与初始代码相比~66％）：

Testing with a 50*50 grid area. Total time 30. Avg 0.300 for 100 tests
Testing with a 100*100 grid area. Total time 68. Avg 0.680 for 100 tests
Testing with a 500*500 grid area. Total time 2475. Avg 24.750 for 100 tests
Testing with a 1000*1000 grid area. Total time 9924. Avg 99.240 for 100 tests
Testing with a 3000*3000 grid area. Total time 101697. Avg 1016.970 for 100 tests

更新＃5（输入变量| UPDATE后的总体附加增益＃4~23％|与初始代码相比的总增益~74％）：

Testing with a 50*50 grid area. Total time 5. Avg 0.048 for 100 tests
Testing with a 100*100 grid area. Total time 43. Avg 0.426 for 100 tests
Testing with a 500*500 grid area. Total time 1851. Avg 18.511 for 100 tests
Testing with a 1000*1000 grid area. Total time 8020. Avg 80.202 for 100 tests
Testing with a 3000*3000 grid area. Total time 78350. Avg 783.502 for 100 tests

（HOPEFULLY FINAL）UPDATE＃6（与Cython相关的优化，包括将其并行化为2个内核| UPDATE后的总体额外增益＃5~88％|与初始代码相比的总增益~97％）：

Testing with a 50*50 grid area. Total time 0.668. Avg 0.007 for 100 tests
Testing with a 100*100 grid area. Total time 1.584. Avg 0.016 for 100 tests
Testing with a 500*500 grid area. Total time 57.374. Avg 0.574 for 100 tests
Testing with a 1000*1000 grid area. Total time 521.210. Avg 5.212 for 100 tests
Testing with a 3000*3000 grid area. Total time 10113.633. Avg 101.136 for 100 tests

与原始实现相比，当前版本平均快30到35倍。因此，它暂时退出。

最终更新（测试超线程8核心机器（不再是localhost，不再是苹果与苹果的比较）|进一步的cython相关优化，并缓存y的值| UPDATE后的总体额外增益＃6~78％|总体增益与初始代码~99.3％）：

Testing with a 50*50 grid area. Total time 0.498. Avg 0.005 for 100 tests
Testing with a 100*100 grid area. Total time 1.146. Avg 0.011 for 100 tests
Testing with a 500*500 grid area. Total time 22.856. Avg 0.229 for 100 tests
Testing with a 1000*1000 grid area. Total time 113.819. Avg 1.138 for 100 tests
Testing with a 3000*3000 grid area. Total time 2228.098ms. Avg 22.281 for 100 tests
Testing with a 10000*10000 grid area. Total time 29407.874ms. Avg 294.79 for 100 tests
Testing with a 20000*20000 grid area. Total time 157185.469ms. Avg 1571.855 for 100 tests
javascript python python-2.7 performance benchmarking
2个回答
2
投票

使用更合适的Python数据类型（一个元组而不是创建{x: .., y: ..} dicts，使用列表而不是带有连续整数索引的dict），使用xrange而不是range，并将所有内容包装到函数中，给出以下代码：

import time


def foo():
    timeTotal = 0
    benchmarkTests = 100

    testSizes = [50, 100, 500, 1000, 3000]

    for a in range(len(testSizes)):
        minX = 0
        maxX = testSizes[a]
        minY = 0
        maxY = testSizes[a]
        cellDimensionsX = 0.991 # cell width
        cellDimensionsY = 1.652 # cell height
        cellDimensionsXHalf = cellDimensionsX / 2 # half cell width
        cellDimensionsYHalf = cellDimensionsY / 2 # half cell height

        maxCellsXCount = int(maxX / cellDimensionsX)
        maxCellsYCount = int(maxY / cellDimensionsY)

        print("X: %s | Y %s | Total %s" % (maxCellsXCount, maxCellsYCount, maxCellsXCount * maxCellsYCount))

        for k in xrange(benchmarkTests):
            start = time.time()
            # cellCoords = {}
            cellCoords = []

            for i in xrange(maxCellsXCount):
                xCoord = (i * cellDimensionsX) + cellDimensionsXHalf
                for d in xrange(maxCellsYCount):
                    # cellCoords[index] = {'x': xCoord, 'y': (d * cellDimensionsY) + cellDimensionsYHalf}
                    # cellCoords[index] = (xCoord, (d * cellDimensionsY) + cellDimensionsYHalf)
                    cellCoords.append((xCoord, (d * cellDimensionsY) + cellDimensionsYHalf))
                    # index += 1

            thisTime = (time.time() - start) * 1000;
            timeTotal = timeTotal + thisTime;
            # print("Elapsed Time: %s for grid with %s cells" % (thisTime, index))

        print("Testing with a %s*%s grid area. Total time %s. Avg %s for %s tests" % (testSizes[a], testSizes[a], timeTotal, (timeTotal / benchmarkTests), benchmarkTests))

foo()

提供大约50％的加速：

X: 50 | Y 30 | Total 1500
Testing with a 50*50 grid area. Total time 33.9999198914. Avg 0.339999198914 for 100 tests
X: 100 | Y 60 | Total 6000
Testing with a 100*100 grid area. Total time 191.999912262. Avg 1.91999912262 for 100 tests
X: 504 | Y 302 | Total 152208
Testing with a 500*500 grid area. Total time 4790.99988937. Avg 47.9099988937 for 100 tests
X: 1009 | Y 605 | Total 610445
Testing with a 1000*1000 grid area. Total time 24529.9999714. Avg 245.299999714 for 100 tests
X: 3027 | Y 1815 | Total 5494005
Testing with a 3000*3000 grid area. Total time 201085.000038. Avg 2010.85000038 for 100 tests

与我机器上的原始代码相比：

X: 50 | Y 30 | Total 1500
Testing with a 50*50 grid area. Total time 94.0001010895. Avg 0.940001010895 for 100 tests
X: 100 | Y 60 | Total 6000
Testing with a 100*100 grid area. Total time 495.000123978. Avg 4.95000123978 for 100 tests
X: 504 | Y 302 | Total 152208
Testing with a 500*500 grid area. Total time 10732.0001125. Avg 107.320001125 for 100 tests
X: 1009 | Y 605 | Total 610445
Testing with a 1000*1000 grid area. Total time 49074.0001202. Avg 490.740001202 for 100 tests
X: 3027 | Y 1815 | Total 5494005
Traceback (most recent call last):
  File "pyperf-orig.py", line 31, in <module>
    cellCoords[index] = {'x': xCoord, 'y': (d * cellDimensionsY) + cellDimensionsYHalf}
MemoryError
1
投票

一个改进是把所有东西都放到一个函数中，因为显然，Python对于全局变量并不是那么好：

运行代码：

Testing with a 50*50 grid area. Total time 52.17409133911133. Avg 0.5217409133911133 for 100 tests
Testing with a 100*100 grid area. Total time 262.12358474731445. Avg 2.6212358474731445 for 100 tests
Testing with a 500*500 grid area. Total time 6206.289052963257. Avg 62.06289052963257 for 100 tests
Testing with a 1000*1000 grid area. Total time 30345.27611732483. Avg 303.4527611732483 for 100 tests

将代码放入函数内部时，速度会快得多：

Testing with a 50*50 grid area. Total time 37.11581230163574. Avg 0.3711581230163574 for 100 tests
Testing with a 100*100 grid area. Total time 176.71799659729004. Avg 1.7671799659729004 for 100 tests
Testing with a 500*500 grid area. Total time 4659.825325012207. Avg 46.59825325012207 for 100 tests
Testing with a 1000*1000 grid area. Total time 23246.346712112427. Avg 232.46346712112427 for 100 tests
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