From 052b55874ddb831e65562d98c969a853a135d76f Mon Sep 17 00:00:00 2001
From: cycleuser <cycleuser@cycleuser.org>
Date: Fri, 15 Dec 2017 20:32:51 +0800
Subject: [PATCH] =?UTF-8?q?=E5=9F=BA=E6=9C=AC=E7=BF=BB=E8=AF=91=E5=AE=8C?=
 =?UTF-8?q?=E7=BB=93?=
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---
 .../.gitignore"                               |   0
 .../Topic20_Parallel_Programming/.gitignore"  |   0
 .../Introduction to HPC.ipynb"                | 692 +++++++++++++
 .../Mandel.ipynb"                             | 941 +++++++++++++++++
 .../Lecture01/Untitled0.ipynb"                |   0
 .../Introduction to HPC.ipynb"                | 752 --------------
 .../Mandel.ipynb"                             | 971 ------------------
 .../Matices.ipynb"                            |   0
 8 files changed, 1633 insertions(+), 1723 deletions(-)
 rename "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic17_MCMC_2/.gitignore" => "\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic17_MCMC_2 \347\251\272\347\231\275\347\253\240\350\212\202/.gitignore" (100%)
 rename "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/.gitignore" => "\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/.gitignore" (100%)
 create mode 100644 "\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb"
 create mode 100644 "\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb"
 rename "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Lecture01/Untitled0.ipynb" => "\346\227\240\351\234\200\347\277\273\350\257\221\347\232\204\345\205\267\346\234\211\345\276\210\345\244\232\351\207\215\345\244\215\345\206\205\345\256\271\347\232\204\351\203\250\345\210\206/Lecture01/Untitled0.ipynb" (100%)
 delete mode 100644 "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb"
 delete mode 100644 "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb"
 rename "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Matices.ipynb" => "\350\213\261\346\226\207\345\216\237\347\211\210\350\257\276\344\273\266\350\265\204\346\226\231/Matices.ipynb" (100%)

diff --git "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic17_MCMC_2/.gitignore" "b/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic17_MCMC_2 \347\251\272\347\231\275\347\253\240\350\212\202/.gitignore"
similarity index 100%
rename from "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic17_MCMC_2/.gitignore"
rename to "\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic17_MCMC_2 \347\251\272\347\231\275\347\253\240\350\212\202/.gitignore"
diff --git "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/.gitignore" "b/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/.gitignore"
similarity index 100%
rename from "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/.gitignore"
rename to "\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/.gitignore"
diff --git "a/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb" "b/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb"
new file mode 100644
index 0000000..b0b937c
--- /dev/null
+++ "b/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb"	
@@ -0,0 +1,692 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用传统的蒙特卡罗方法来估计圆周率，Traditional Monte Carlo estimation of $\\pi$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cythonmagic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 回顾优化，Review of optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pi_python(n):\n",
+    "    s = np.zeros(n, dtype=np.bool)\n",
+    "    for i in range(n):\n",
+    "        x, y = np.random.uniform(-1, 1, 2)\n",
+    "        s[i] = x**2 + y**2 < 1\n",
+    "    return 4.0*np.sum(s)/n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%cython\n",
+    "import cython\n",
+    "import numpy as np\n",
+    "cimport numpy as np\n",
+    "\n",
+    "cdef gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937)\n",
+    "\n",
+    "@cython.cdivision\n",
+    "@cython.boundscheck(False)\n",
+    "@cython.wraparound(False)\n",
+    "def pi_cython(long n):\n",
+    "    cdef int[:] s = np.zeros(n, dtype=np.int32)\n",
+    "    cdef int i = 0\n",
+    "    cdef double x, y\n",
+    "    for i in range(n):\n",
+    "        x = np.random.uniform(-1, 1)\n",
+    "        y = np.random.uniform(-1, 1)\n",
+    "        s[i] = x**2 + y**2 < 1\n",
+    "    cdef int hits = 0\n",
+    "    for i in range(n):\n",
+    "        hits += s[i]\n",
+    "    return 4.0*hits/n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pi_numpy(n):\n",
+    "    xs = np.random.uniform(-1, 1, (n,2))\n",
+    "    return 4.0*((xs**2).sum(axis=1).sum() < 1)/n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 650 ms per loop\n",
+      "10 loops, best of 3: 29.8 ms per loop\n",
+      "100 loops, best of 3: 4.61 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = int(1e5)\n",
+    "%timeit pi_python(n)\n",
+    "%timeit pi_cython(n)\n",
+    "%timeit pi_numpy(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 尴尬的并行问题？Embarassingly parallel problems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 使用多线程，Using Multiprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import multiprocessing\n",
+    "\n",
+    "num_procs = multiprocessing.cpu_count()\n",
+    "num_procs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pi_multiprocessing(n):\n",
+    "    \"\"\"Split a job of length n into num_procs pieces.\"\"\"\n",
+    "    import multiprocessing\n",
+    "    m = multiprocessing.cpu_count()\n",
+    "    pool = multiprocessing.Pool(m)\n",
+    "    results = pool.map(pi_numpy, [n/m]*m)\n",
+    "    pool.close()\n",
+    "    return np.mean(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 58.4 ms per loop\n",
+      "10 loops, best of 3: 61.1 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = int(1e6)\n",
+    "%timeit pi_numpy(n)\n",
+    "%timeit pi_multiprocessing(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**问题越大，需要的并行规模就越大；The bigger the problem, the more scope there is for parallelism**\n",
+    "\n",
+    "根据Amhdahls 定律（Amhdahls' law），对并行计算的加速受限制于整个算法中可并行代码与不可约的序列代码的比值。\n",
+    "然而，对于大数据分析，Gustafson 定律（Gustafson's Law）则表明，我们总是试图增加可并行部分的规模，可并行与不可并行的代码的比例不是一个静态固定值，而是依赖数据规模而有所变化。\n",
+    "例如，吉布斯采样（Gibbs Sampling）就有不可约的连续本质（irreducibly serial nature）但对于大量样本来说，每次迭代都可以对上万亿的数据点并行进行概率密度函数估计（perform PDF evaluations）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 663 ms per loop\n",
+      "1 loops, best of 3: 367 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = int(1e7)\n",
+    "%timeit pi_numpy(n)\n",
+    "%timeit pi_multiprocessing(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 并行单元之间的通信，Communication across parallel workers\n",
+    "\n",
+    "不是所有的任务都适合使用并行。在一些问题中，我们需要再多个并行单元之间进行通信。通常有两种方式来实现，一种是共享内存（例如 OpenMP），另一种就是特定的通信机制（例如 MPI）。多线程（Multiprocessing）（以及 GPU 显卡计算）可以同时利用这两种方法。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**共享内存，Using shared memory**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "396\n",
+      "1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# illustrating a race condition\n",
+    "\n",
+    "from multiprocessing import Pool, Value, Array, Lock, current_process\n",
+    "\n",
+    "n = 4\n",
+    "val = Value('i')\n",
+    "arr = Array('i', n)\n",
+    "\n",
+    "val.value = 0\n",
+    "for i in range(n):\n",
+    "    arr[i] = 0\n",
+    "\n",
+    "def count1(i):\n",
+    "    ix = current_process().pid % n\n",
+    "    val.value += 1\n",
+    "    \n",
+    "def count2(i):\n",
+    "    ix = current_process().pid % n\n",
+    "    arr[ix] += 1\n",
+    "    \n",
+    "pool = Pool(n)\n",
+    "pool.map(count1, range(1000))\n",
+    "pool.map(count2, range(1000))\n",
+    "\n",
+    "pool.close()\n",
+    "print val.value\n",
+    "print sum(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Using message passing to communicate\n",
+    "\n",
+    "from multiprocessing import Process, Queue, Pool\n",
+    "import time\n",
+    "\n",
+    "def f(i):\n",
+    "    n = q.get()\n",
+    "    q.put(n+1)\n",
+    "    \n",
+    "if __name__ == '__main__':\n",
+    "    q = Queue()\n",
+    "    q.put(0)\n",
+    "    pool = Pool(4)\n",
+    "    pool.map(f, range(100))\n",
+    "    print q.get()\n",
+    "    pool.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 使用 IPython 并行来进行交互式并行计算，Using IPython parallel for interactive parallel computing\n",
+    "\n",
+    "\n",
+    "在 IPython Notebook 界面中启动一个运算单元，或者可以输入下面的命令：\n",
+    "\n",
+    "```ipcluster start -n 4```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "time.sleep(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.parallel import Client, interactive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**直观展示，Direct view**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "rc = Client()\n",
+    "print rc.ids\n",
+    "dv = rc[:]\n",
+    "num_procs = len(rc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dv.map_sync(lambda x: x*x, range(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AsyncResult: finished>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dv.use_dill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dv.map_sync(lambda x: x*x, range(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mandel_python(xs, ys, max_iters):\n",
+    "    c = np.zeros((len(xs), len(ys)), np.int)\n",
+    "    for i, x in enumerate(xs):\n",
+    "        for j, y in enumerate(ys):\n",
+    "            a = complex(x, y)\n",
+    "            z = 0.0j\n",
+    "            for k in range(max_iters):\n",
+    "                z = z*z + a\n",
+    "                if z.real*z.real + z.imag*z.imag >= 4:\n",
+    "                    c[i,j] = k\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mandel_numpy(xs, ys, max_iters):\n",
+    "    X, Y = np.meshgrid(xs, ys)\n",
+    "    A = X + 1j*Y\n",
+    "    z = np.zeros_like(A, np.complex)\n",
+    "    c = np.zeros_like(A, np.int)\n",
+    "    for i in range(max_iters):\n",
+    "        z = z*z + A\n",
+    "        out = z.real*z.real + z.imag*z.imag >= 4\n",
+    "        c[out & (c==0)] = i\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xmin, xmax, ymin, ymax = -2, 1, -1, 1\n",
+    "xs = np.linspace(xmin, xmax, int(num_procs*20*(xmax-xmin)))\n",
+    "ys = np.linspace(ymin, ymax, int(num_procs*20*(ymax-ymin)))\n",
+    "max_iters = 100\n",
+    "num_procs = 8\n",
+    "\n",
+    "mandel = lambda x: mandel_numpy(x, ys=ys, max_iters=max_iters)\n",
+    "\n",
+    "def mandel_parallel(xs):   \n",
+    "    chunks = np.split(xs, num_procs)\n",
+    "    return np.hstack(dv.map_sync(mandel, chunks)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 1.67 s per loop\n",
+      "10 loops, best of 3: 51.7 ms per loop\n",
+      "10 loops, best of 3: 47.6 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "dv.execute('import numpy as np')\n",
+    "dv['ys'] = ys\n",
+    "dv['num_procs'] = len(rc)\n",
+    "dv['mandel' ] = mandel\n",
+    "dv['mandel_numpy'] = mandel_numpy\n",
+    "dv['max_iters'] = max_iters\n",
+    "\n",
+    "old_settings = np.seterr(all='ignore') \n",
+    "%timeit mandel_python(xs, ys, max_iters)\n",
+    "%timeit mandel_numpy(xs, ys, max_iters)\n",
+    "%timeit mandel_parallel(xs)\n",
+    "np.seterr(**old_settings);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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X70mw7W8PcWZXBwVk6giTvtaCFhKQRnX4PrAdYu/wltQ+LhLkUcbJQL1Epw38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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1095d44d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xmin, xmax, ymin, ymax = -2, 1, -1, 1\n",
+    "xs = np.linspace(xmin, xmax, int(num_procs*20*(xmax-xmin)))\n",
+    "ys = np.linspace(ymin, ymax, int(num_procs*20*(ymax-ymin)))\n",
+    "c = mandel_parallel(xs)\n",
+    "plt.imshow(np.log1p(c), extent=[xmin, xmax, ymin, ymax]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**负载均衡监视，Load balanced view**\n",
+    "\n",
+    "如果并行处理的任务是异构（heterogeneous）的，建议使用负载均衡监控并行单元的效率。\n",
+    "这个负载均衡监视（load-balanced view）很类似多线程包（multiprocessing package）当中的 Pool()类。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lv = rc.load_balanced_view()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7 6 2 9 7 3 5 5 2 4 8 1 3 6 6 1 2 4 9 2]\n",
+      "1 loops, best of 3: 31 s per loop\n",
+      "1 loops, best of 3: 28 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "def snooze(x):\n",
+    "    import time\n",
+    "    time.sleep(x)\n",
+    "\n",
+    "naps = np.random.randint(1, 10, 20)\n",
+    "print naps\n",
+    "\n",
+    "%timeit dv.map_sync(snooze, naps) \n",
+    "%timeit lv.map_sync(snooze, naps) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/json": {
+       "Software versions": [
+        {
+         "module": "Python",
+         "version": "2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)]"
+        },
+        {
+         "module": "IPython",
+         "version": "2.1.0"
+        },
+        {
+         "module": "OS",
+         "version": "posix [darwin]"
+        },
+        {
+         "module": "numpy",
+         "version": "1.8.1"
+        },
+        {
+         "module": "multiprocessing",
+         "version": "0.70a1"
+        },
+        {
+         "module": "cython",
+         "version": "0.20.1"
+        }
+       ]
+      },
+      "text/html": [
+       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)]</td></tr><tr><td>IPython</td><td>2.1.0</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>numpy</td><td>1.8.1</td></tr><tr><td>multiprocessing</td><td>0.70a1</td></tr><tr><td>cython</td><td>0.20.1</td></tr><tr><td colspan='2'>Fri Jun 13 12:45:00 2014 EDT</td></tr></table>"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{|l|l|}\\hline\n",
+       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
+       "Python & 2.7.7 |Anaconda 2.0.1 (x86\\letterunderscore{}64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)] \\\\ \\hline\n",
+       "IPython & 2.1.0 \\\\ \\hline\n",
+       "OS & posix [darwin] \\\\ \\hline\n",
+       "numpy & 1.8.1 \\\\ \\hline\n",
+       "multiprocessing & 0.70a1 \\\\ \\hline\n",
+       "cython & 0.20.1 \\\\ \\hline\n",
+       "\\hline \\multicolumn{2}{|l|}{Fri Jun 13 12:45:00 2014 EDT} \\\\ \\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/plain": [
+       "Software versions\n",
+       "Python 2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)]\n",
+       "IPython 2.1.0\n",
+       "OS posix [darwin]\n",
+       "numpy 1.8.1\n",
+       "multiprocessing 0.70a1\n",
+       "cython 0.20.1\n",
+       "<tr><td colspan='2'>Fri Jun 13 12:45:00 2014 EDT</td></tr>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%load_ext version_information\n",
+    "\n",
+    "%version_information numpy, multiprocessing, cython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git "a/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb" "b/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb"
new file mode 100644
index 0000000..9595c07
--- /dev/null
+++ "b/\345\267\262\347\273\217\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb"
@@ -0,0 +1,941 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import division\n",
+    "import os\n",
+    "import sys\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "%precision 4\n",
+    "plt.style.use('ggplot')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cythonmagic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import jit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mandel_python(xs, ys, max_iters):\n",
+    "    c = np.zeros((len(xs), len(ys)), np.int)\n",
+    "    for i, x in enumerate(xs):\n",
+    "        for j, y in enumerate(ys):\n",
+    "            a = complex(x, y)\n",
+    "            z = 0.0j\n",
+    "            for k in range(max_iters):\n",
+    "                z = z*z + a\n",
+    "                if z.real*z.real + z.imag*z.imag >= 4:\n",
+    "                    c[i,j] = k\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xmin, xmax, ymin, ymax = -2, 1, -1, 1\n",
+    "xs = np.linspace(xmin, xmax, 200)\n",
+    "ys = np.linspace(ymin, ymax, 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 4.37 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit mandel_python(xs, ys, 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZrdzzrN9HuS3h8J6uo3iN+J3OZN8HYlAsJX8Ja71wukfJkyBTymD2o4AiE/IN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\nPDzWOf8PtumsAl7CL5gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1161bd350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = mandel_python(xs, ys, 200)\n",
+    "plt.imshow(np.log1p(c.T), extent=[xmin, xmax, ymin, ymax]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@jit\n",
+    "def mandel_numba(xs, ys, max_iters):\n",
+    "    c = np.zeros((len(xs), len(ys)), np.int)\n",
+    "    for i, x in enumerate(xs):\n",
+    "        for j, y in enumerate(ys):\n",
+    "            a = complex(x, y)\n",
+    "            z = 0.0j\n",
+    "            for k in range(max_iters):\n",
+    "                z = z*z + a\n",
+    "                if z.real*z.real + z.imag*z.imag >= 4:\n",
+    "                    c[i,j] = k\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 43.2 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit mandel_numba(xs, ys, 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZrdzzrN9HuS3h8J6uo3iN+J3OZN8HYlAsJX8Ja71wukfJkyBTymD2o4AiE/IN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\nl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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1174c6b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = mandel_numba(xs, ys, 200)\n",
+    "plt.imshow(np.log1p(c.T), extent=[xmin, xmax, ymin, ymax]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<div class=\"cython\"><pre class='cython line score-0'>&#xA0;01: </pre>\n",
+       "<pre class='cython line score-19' onclick='toggleDiv(this)'>+02: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
+       "<pre class='cython code score-19'>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n",
+       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
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+       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
+       "/* … */\n",
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+       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
+       "</pre><pre class='cython line score-0'>&#xA0;03: <span class=\"k\">cimport</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
+       "<pre class='cython line score-0'>&#xA0;04: <span class=\"k\">cimport</span> <span class=\"nn\">cython</span></pre>\n",
+       "<pre class='cython line score-0'>&#xA0;05: </pre>\n",
+       "<pre class='cython line score-0'>&#xA0;06: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
+       "<pre class='cython line score-0'>&#xA0;07: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
+       "<pre class='cython line score-66' onclick='toggleDiv(this)'>+08: <span class=\"k\">def</span> <span class=\"nf\">mandel_cython</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">xs</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">ys</span><span class=\"p\">,</span> <span class=\"nb\">int</span> <span class=\"n\">max_iters</span><span class=\"p\">):</span></pre>\n",
+       "<pre class='cython code score-66'>/* Python wrapper */\n",
+       "static PyObject *__pyx_pw_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
+       "static PyMethodDef __pyx_mdef_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython = {\"mandel_cython\", (PyCFunction)__pyx_pw_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython, METH_VARARGS|METH_KEYWORDS, 0};\n",
+       "static PyObject *__pyx_pw_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
+       "  __Pyx_memviewslice __pyx_v_xs = { 0, 0, { 0 }, { 0 }, { 0 } };\n",
+       "  __Pyx_memviewslice __pyx_v_ys = { 0, 0, { 0 }, { 0 }, { 0 } };\n",
+       "  int __pyx_v_max_iters;\n",
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+       "  <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"mandel_cython (wrapper)\", 0);\n",
+       "  {\n",
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+       "    PyObject* values[3] = {0,0,0};\n",
+       "    if (unlikely(__pyx_kwds)) {\n",
+       "      Py_ssize_t kw_args;\n",
+       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
+       "      switch (pos_args) {\n",
+       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
+       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
+       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
+       "        case  0: break;\n",
+       "        default: goto __pyx_L5_argtuple_error;\n",
+       "      }\n",
+       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
+       "      switch (pos_args) {\n",
+       "        case  0:\n",
+       "        if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_xs)) != 0)) kw_args--;\n",
+       "        else goto __pyx_L5_argtuple_error;\n",
+       "        case  1:\n",
+       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_ys)) != 0)) kw_args--;\n",
+       "        else {\n",
+       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"mandel_cython\", 1, 3, 3, 1); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n",
+       "        }\n",
+       "        case  2:\n",
+       "        if (likely((values[2] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_max_iters)) != 0)) kw_args--;\n",
+       "        else {\n",
+       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"mandel_cython\", 1, 3, 3, 2); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n",
+       "        }\n",
+       "      }\n",
+       "      if (unlikely(kw_args &gt; 0)) {\n",
+       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"mandel_cython\") &lt; 0)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n",
+       "      }\n",
+       "    } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 3) {\n",
+       "      goto __pyx_L5_argtuple_error;\n",
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+       "  __PYX_XDEC_MEMVIEW(&amp;__pyx_t_8, 1);\n",
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+       "</pre><pre class='cython line score-0'>&#xA0;09: </pre>\n",
+       "<pre class='cython line score-0' onclick='toggleDiv(this)'>+10:     <span class=\"n\">m</span> <span class=\"o\">=</span> <span class=\"n\">xs</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
+       "<pre class='cython code score-0'>  __pyx_v_m = (__pyx_v_xs.shape[0]);\n",
+       "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+11:     <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"n\">ys</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
+       "<pre class='cython code score-0'>  __pyx_v_n = (__pyx_v_ys.shape[0]);\n",
+       "</pre><pre class='cython line score-0'>&#xA0;12: </pre>\n",
+       "<pre class='cython line score-52' onclick='toggleDiv(this)'>+13:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span>[<span class=\"p\">:,::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">((</span><span class=\"n\">m</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">),</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">int32</span><span class=\"p\">)</span></pre>\n",
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+       "</pre><pre class='cython line score-0'>&#xA0;14:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span><span class=\"p\">,</span> <span class=\"nf\">j</span><span class=\"p\">,</span> <span class=\"nf\">k</span></pre>\n",
+       "<pre class='cython line score-0'>&#xA0;15:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"kt\">complex</span> <span class=\"nf\">a</span><span class=\"p\">,</span> <span class=\"nf\">z</span></pre>\n",
+       "<pre class='cython line score-0'>&#xA0;16: </pre>\n",
+       "<pre class='cython line score-0' onclick='toggleDiv(this)'>+17:     <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">m</span><span class=\"p\">):</span></pre>\n",
+       "<pre class='cython code score-0'>  __pyx_t_6 = __pyx_v_m;\n",
+       "  for (__pyx_t_9 = 0; __pyx_t_9 &lt; __pyx_t_6; __pyx_t_9+=1) {\n",
+       "    __pyx_v_i = __pyx_t_9;\n",
+       "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+18:         <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">):</span></pre>\n",
+       "<pre class='cython code score-0'>    __pyx_t_10 = __pyx_v_n;\n",
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+       "      __pyx_v_j = __pyx_t_11;\n",
+       "</pre><pre class='cython line score-1' onclick='toggleDiv(this)'>+19:             <span class=\"n\">a</span><span class=\"o\">.</span><span class=\"n\">real</span> <span class=\"o\">=</span> <span class=\"n\">xs</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span></pre>\n",
+       "<pre class='cython code score-1'>      __pyx_t_12 = __pyx_v_i;\n",
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+       "</pre><pre class='cython line score-1' onclick='toggleDiv(this)'>+20:             <span class=\"n\">a</span><span class=\"o\">.</span><span class=\"n\">imag</span> <span class=\"o\">=</span> <span class=\"n\">ys</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span></pre>\n",
+       "<pre class='cython code score-1'>      __pyx_t_13 = __pyx_v_j;\n",
+       "      <span class='pyx_macro_api'>__Pyx_SET_CIMAG</span>(__pyx_v_a, (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_ys.data) + __pyx_t_13)) ))));\n",
+       "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+21:             <span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"mf\">0.0</span><span class=\"n\">j</span></pre>\n",
+       "<pre class='cython code score-0'>      __pyx_v_z = __pyx_t_double_complex_from_parts(0, 0.0);\n",
+       "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+22:             <span class=\"k\">for</span> <span class=\"n\">k</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">max_iters</span><span class=\"p\">):</span></pre>\n",
+       "<pre class='cython code score-0'>      __pyx_t_14 = __pyx_v_max_iters;\n",
+       "      for (__pyx_t_15 = 0; __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {\n",
+       "        __pyx_v_k = __pyx_t_15;\n",
+       "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+23:                 <span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"n\">z</span><span class=\"o\">*</span><span class=\"n\">z</span> <span class=\"o\">+</span> <span class=\"n\">a</span></pre>\n",
+       "<pre class='cython code score-0'>        __pyx_v_z = __Pyx_c_sum(__Pyx_c_prod(__pyx_v_z, __pyx_v_z), __pyx_v_a);\n",
+       "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+24:                 <span class=\"k\">if</span> <span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">real</span><span class=\"o\">*</span><span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">real</span> <span class=\"o\">+</span> <span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">imag</span><span class=\"o\">*</span><span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">imag</span> <span class=\"o\">&gt;=</span> <span class=\"mf\">4</span><span class=\"p\">:</span></pre>\n",
+       "<pre class='cython code score-4'>        __pyx_t_16 = ((((<span class='pyx_macro_api'>__Pyx_CREAL</span>(__pyx_v_z) * <span class='pyx_macro_api'>__Pyx_CREAL</span>(__pyx_v_z)) + (<span class='pyx_macro_api'>__Pyx_CIMAG</span>(__pyx_v_z) * <span class='pyx_macro_api'>__Pyx_CIMAG</span>(__pyx_v_z))) &gt;= 4.0) != 0);\n",
+       "        if (__pyx_t_16) {\n",
+       "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+25:                     <span class=\"n\">c</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">,</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">k</span></pre>\n",
+       "<pre class='cython code score-0'>          __pyx_t_17 = __pyx_v_i;\n",
+       "          __pyx_t_18 = __pyx_v_j;\n",
+       "          *((int *) ( /* dim=1 */ ((char *) (((int *) ( /* dim=0 */ (__pyx_v_c.data + __pyx_t_17 * __pyx_v_c.strides[0]) )) + __pyx_t_18)) )) = __pyx_v_k;\n",
+       "          goto __pyx_L9;\n",
+       "        }\n",
+       "        __pyx_L9:;\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "</pre><pre class='cython line score-1' onclick='toggleDiv(this)'>+26:     <span class=\"k\">return</span> <span class=\"n\">c</span></pre>\n",
+       "<pre class='cython code score-1'>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
+       "  __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_c, 2, (PyObject *(*)(char *)) __pyx_memview_get_int, (int (*)(char *, PyObject *)) __pyx_memview_set_int, 0);;<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n",
+       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
+       "  __pyx_r = __pyx_t_1;\n",
+       "  __pyx_t_1 = 0;\n",
+       "  goto __pyx_L0;\n",
+       "</pre></div></body></html>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML at 0x1161f7590>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%cython -a\n",
+    "\n",
+    "import numpy as np\n",
+    "cimport numpy as np\n",
+    "cimport cython\n",
+    "\n",
+    "@cython.boundscheck(False)\n",
+    "@cython.wraparound(False)\n",
+    "def mandel_cython(double[::1] xs, double[::1] ys, int max_iters):\n",
+    "\n",
+    "    m = xs.shape[0]\n",
+    "    n = ys.shape[0]\n",
+    "\n",
+    "    cdef int[:,::1] c = np.zeros((m, n), np.int32)\n",
+    "    cdef int i, j, k\n",
+    "    cdef double complex a, z\n",
+    "\n",
+    "    for i in range(m):\n",
+    "        for j in range(n):\n",
+    "            a.real = xs[i]\n",
+    "            a.imag = ys[j]\n",
+    "            z = 0.0j\n",
+    "            for k in range(max_iters):\n",
+    "                z = z*z + a\n",
+    "                if z.real*z.real + z.imag*z.imag >= 4:\n",
+    "                    c[i,j] = k\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 44.6 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit mandel_cython(xs, ys, 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZrdzzrN9HuS3h8J6uo3iN+J3OZN8HYlAsJX8Ja71wukfJkyBTymD2o4AiE/IN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\nPDzWOf8PtumsAl7CL5gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117110b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = mandel_cython(xs, ys, 200)\n",
+    "plt.imshow(np.log1p(c.T), extent=[xmin, xmax, ymin, ymax]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Lecture01/Untitled0.ipynb" "b/\346\227\240\351\234\200\347\277\273\350\257\221\347\232\204\345\205\267\346\234\211\345\276\210\345\244\232\351\207\215\345\244\215\345\206\205\345\256\271\347\232\204\351\203\250\345\210\206/Lecture01/Untitled0.ipynb"
similarity index 100%
rename from "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Lecture01/Untitled0.ipynb"
rename to "\346\227\240\351\234\200\347\277\273\350\257\221\347\232\204\345\205\267\346\234\211\345\276\210\345\244\232\351\207\215\345\244\215\345\206\205\345\256\271\347\232\204\351\203\250\345\210\206/Lecture01/Untitled0.ipynb"
diff --git "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb" "b/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb"
deleted file mode 100644
index e7710b3..0000000
--- "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Introduction to HPC.ipynb"	
+++ /dev/null
@@ -1,752 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:1ef1708ecc9ada72b86108393c8e0833653fbb0a185639df76bc1d9ef3f49f23"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Traditional Monte Carlo estimation of $\\pi$ "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "from matplotlib import pyplot as plt\n",
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Review of optimization"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def pi_python(n):\n",
-      "    s = np.zeros(n, dtype=np.bool)\n",
-      "    for i in range(n):\n",
-      "        x, y = np.random.uniform(-1, 1, 2)\n",
-      "        s[i] = x**2 + y**2 < 1\n",
-      "    return 4.0*np.sum(s)/n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "import cython\n",
-      "import numpy as np\n",
-      "cimport numpy as np\n",
-      "\n",
-      "cdef gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937)\n",
-      "\n",
-      "@cython.cdivision\n",
-      "@cython.boundscheck(False)\n",
-      "@cython.wraparound(False)\n",
-      "def pi_cython(long n):\n",
-      "    cdef int[:] s = np.zeros(n, dtype=np.int32)\n",
-      "    cdef int i = 0\n",
-      "    cdef double x, y\n",
-      "    for i in range(n):\n",
-      "        x = np.random.uniform(-1, 1)\n",
-      "        y = np.random.uniform(-1, 1)\n",
-      "        s[i] = x**2 + y**2 < 1\n",
-      "    cdef int hits = 0\n",
-      "    for i in range(n):\n",
-      "        hits += s[i]\n",
-      "    return 4.0*hits/n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def pi_numpy(n):\n",
-      "    xs = np.random.uniform(-1, 1, (n,2))\n",
-      "    return 4.0*((xs**2).sum(axis=1).sum() < 1)/n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "n = int(1e5)\n",
-      "%timeit pi_python(n)\n",
-      "%timeit pi_cython(n)\n",
-      "%timeit pi_numpy(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 650 ms per loop\n",
-        "10 loops, best of 3: 29.8 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100 loops, best of 3: 4.61 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Embarassingly parallel problems"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Using Multiprocessing"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import multiprocessing\n",
-      "\n",
-      "num_procs = multiprocessing.cpu_count()\n",
-      "num_procs"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "4"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def pi_multiprocessing(n):\n",
-      "    \"\"\"Split a job of length n into num_procs pieces.\"\"\"\n",
-      "    import multiprocessing\n",
-      "    m = multiprocessing.cpu_count()\n",
-      "    pool = multiprocessing.Pool(m)\n",
-      "    results = pool.map(pi_numpy, [n/m]*m)\n",
-      "    pool.close()\n",
-      "    return np.mean(results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "n = int(1e6)\n",
-      "%timeit pi_numpy(n)\n",
-      "%timeit pi_multiprocessing(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 58.4 ms per loop\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 61.1 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**The bigger the problem, the more scope there is for parallelism**\n",
-      "\n",
-      "Amhdahls' law says that the speedup from parallelization is bounded by the ratio of parallelizable to irreducibly serial code in the aloorithm. However, for big data analysis, Gustafson's Law  which says that we are nearly always interested in increasing the size of the parallelizable bits, and the ratio of parallelizable to irreducibly serial code is not a static quantity but depends on data size. For example, Gibbs sampling has an irreducibly serial nature, but for large samples, each iteration may be able perform PDF evaluations in parallel for zillions of data points."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "n = int(1e7)\n",
-      "%timeit pi_numpy(n)\n",
-      "%timeit pi_multiprocessing(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 663 ms per loop\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 367 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Communication across parallel workers\n",
-      "\n",
-      "Not all tasks are embarassingly parallel. In these problems, we need to communicate across parallel workers. There are two ways to do this - via shared memory (exemplar is OpenMP) and by explicit communication mechanisms (exemplar is MPI). Multiprocessing (and GPU computing) can use both mechanisms."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Using shared memory**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# illustrating a race condition\n",
-      "\n",
-      "from multiprocessing import Pool, Value, Array, Lock, current_process\n",
-      "\n",
-      "n = 4\n",
-      "val = Value('i')\n",
-      "arr = Array('i', n)\n",
-      "\n",
-      "val.value = 0\n",
-      "for i in range(n):\n",
-      "    arr[i] = 0\n",
-      "\n",
-      "def count1(i):\n",
-      "    ix = current_process().pid % n\n",
-      "    val.value += 1\n",
-      "    \n",
-      "def count2(i):\n",
-      "    ix = current_process().pid % n\n",
-      "    arr[ix] += 1\n",
-      "    \n",
-      "pool = Pool(n)\n",
-      "pool.map(count1, range(1000))\n",
-      "pool.map(count2, range(1000))\n",
-      "\n",
-      "pool.close()\n",
-      "print val.value\n",
-      "print sum(arr)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "396\n",
-        "1000\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "### Using message passing to communicate\n",
-      "\n",
-      "from multiprocessing import Process, Queue, Pool\n",
-      "import time\n",
-      "\n",
-      "def f(i):\n",
-      "    n = q.get()\n",
-      "    q.put(n+1)\n",
-      "    \n",
-      "if __name__ == '__main__':\n",
-      "    q = Queue()\n",
-      "    q.put(0)\n",
-      "    pool = Pool(4)\n",
-      "    pool.map(f, range(100))\n",
-      "    print q.get()\n",
-      "    pool.close()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Using IPython parallel for interactive parallel computing\n",
-      "\n",
-      "Start a cluster of workers using IPython notebook interface. Alternatively, enter\n",
-      "\n",
-      "```ipcluster start -n 4```\n",
-      "\n",
-      "at the command line."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "time.sleep(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.parallel import Client, interactive"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Direct view**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "rc = Client()\n",
-      "print rc.ids\n",
-      "dv = rc[:]\n",
-      "num_procs = len(rc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0, 1, 2, 3]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dv.map_sync(lambda x: x*x, range(10))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dv.use_dill()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "<AsyncResult: finished>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dv.map_sync(lambda x: x*x, range(10))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def mandel_python(xs, ys, max_iters):\n",
-      "    c = np.zeros((len(xs), len(ys)), np.int)\n",
-      "    for i, x in enumerate(xs):\n",
-      "        for j, y in enumerate(ys):\n",
-      "            a = complex(x, y)\n",
-      "            z = 0.0j\n",
-      "            for k in range(max_iters):\n",
-      "                z = z*z + a\n",
-      "                if z.real*z.real + z.imag*z.imag >= 4:\n",
-      "                    c[i,j] = k\n",
-      "    return c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def mandel_numpy(xs, ys, max_iters):\n",
-      "    X, Y = np.meshgrid(xs, ys)\n",
-      "    A = X + 1j*Y\n",
-      "    z = np.zeros_like(A, np.complex)\n",
-      "    c = np.zeros_like(A, np.int)\n",
-      "    for i in range(max_iters):\n",
-      "        z = z*z + A\n",
-      "        out = z.real*z.real + z.imag*z.imag >= 4\n",
-      "        c[out & (c==0)] = i\n",
-      "    return c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xmin, xmax, ymin, ymax = -2, 1, -1, 1\n",
-      "xs = np.linspace(xmin, xmax, int(num_procs*20*(xmax-xmin)))\n",
-      "ys = np.linspace(ymin, ymax, int(num_procs*20*(ymax-ymin)))\n",
-      "max_iters = 100\n",
-      "num_procs = 8\n",
-      "\n",
-      "mandel = lambda x: mandel_numpy(x, ys=ys, max_iters=max_iters)\n",
-      "\n",
-      "def mandel_parallel(xs):   \n",
-      "    chunks = np.split(xs, num_procs)\n",
-      "    return np.hstack(dv.map_sync(mandel, chunks)) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dv.execute('import numpy as np')\n",
-      "dv['ys'] = ys\n",
-      "dv['num_procs'] = len(rc)\n",
-      "dv['mandel' ] = mandel\n",
-      "dv['mandel_numpy'] = mandel_numpy\n",
-      "dv['max_iters'] = max_iters\n",
-      "\n",
-      "old_settings = np.seterr(all='ignore') \n",
-      "%timeit mandel_python(xs, ys, max_iters)\n",
-      "%timeit mandel_numpy(xs, ys, max_iters)\n",
-      "%timeit mandel_parallel(xs)\n",
-      "np.seterr(**old_settings);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 1.67 s per loop\n",
-        "10 loops, best of 3: 51.7 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10 loops, best of 3: 47.6 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xmin, xmax, ymin, ymax = -2, 1, -1, 1\n",
-      "xs = np.linspace(xmin, xmax, int(num_procs*20*(xmax-xmin)))\n",
-      "ys = np.linspace(ymin, ymax, int(num_procs*20*(ymax-ymin)))\n",
-      "c = mandel_parallel(xs)\n",
-      "plt.imshow(np.log1p(c), extent=[xmin, xmax, ymin, ymax]);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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X70mw7W8PcWZXBwVk6giTvtaCFhKQRnX4PrAdYu/wltQ+LhLkUcbJQL1Epw38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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1095d44d0>"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Load balanced view**\n",
-      "\n",
-      "When the tasks to be performed in parallel are heterogeneous, it is better to use a load-balanced view of the workers for efficiency. The load-balanced view behaves very similarly to the Pool() class from the multiprocessing package."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "lv = rc.load_balanced_view()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def snooze(x):\n",
-      "    import time\n",
-      "    time.sleep(x)\n",
-      "\n",
-      "naps = np.random.randint(1, 10, 20)\n",
-      "print naps\n",
-      "\n",
-      "%timeit dv.map_sync(snooze, naps) \n",
-      "%timeit lv.map_sync(snooze, naps) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[7 6 2 9 7 3 5 5 2 4 8 1 3 6 6 1 2 4 9 2]\n",
-        "1 loops, best of 3: 31 s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1 loops, best of 3: 28 s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext version_information\n",
-      "\n",
-      "%version_information numpy, multiprocessing, cython"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)]</td></tr><tr><td>IPython</td><td>2.1.0</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>numpy</td><td>1.8.1</td></tr><tr><td>multiprocessing</td><td>0.70a1</td></tr><tr><td>cython</td><td>0.20.1</td></tr><tr><td colspan='2'>Fri Jun 13 12:45:00 2014 EDT</td></tr></table>"
-       ],
-       "json": [
-        "{\"Software versions\": [{\"version\": \"2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)]\", \"module\": \"Python\"}, {\"version\": \"2.1.0\", \"module\": \"IPython\"}, {\"version\": \"posix [darwin]\", \"module\": \"OS\"}, {\"version\": \"1.8.1\", \"module\": \"numpy\"}, {\"version\": \"0.70a1\", \"module\": \"multiprocessing\"}, {\"version\": \"0.20.1\", \"module\": \"cython\"}]}"
-       ],
-       "latex": [
-        "\\begin{tabular}{|l|l|}\\hline\n",
-        "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
-        "Python & 2.7.7 |Anaconda 2.0.1 (x86\\letterunderscore{}64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)] \\\\ \\hline\n",
-        "IPython & 2.1.0 \\\\ \\hline\n",
-        "OS & posix [darwin] \\\\ \\hline\n",
-        "numpy & 1.8.1 \\\\ \\hline\n",
-        "multiprocessing & 0.70a1 \\\\ \\hline\n",
-        "cython & 0.20.1 \\\\ \\hline\n",
-        "\\hline \\multicolumn{2}{|l|}{Fri Jun 13 12:45:00 2014 EDT} \\\\ \\hline\n",
-        "\\end{tabular}\n"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "Software versions\n",
-        "Python 2.7.7 |Anaconda 2.0.1 (x86_64)| (default, Jun  2 2014, 12:48:16) [GCC 4.0.1 (Apple Inc. build 5493)]\n",
-        "IPython 2.1.0\n",
-        "OS posix [darwin]\n",
-        "numpy 1.8.1\n",
-        "multiprocessing 0.70a1\n",
-        "cython 0.20.1\n",
-        "<tr><td colspan='2'>Fri Jun 13 12:45:00 2014 EDT</td></tr>"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb" "b/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb"
deleted file mode 100644
index 6c86a06..0000000
--- "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Topic20_Parallel_Programming/Mandel.ipynb"
+++ /dev/null
@@ -1,971 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:df783db5cf9426e7d4f59959be7fe8e4c8cabfc2f19d005357300ed18289c637"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import division\n",
-      "import os\n",
-      "import sys\n",
-      "import glob\n",
-      "import matplotlib.pyplot as plt\n",
-      "import numpy as np\n",
-      "import pandas as pd\n",
-      "%matplotlib inline\n",
-      "%precision 4\n",
-      "plt.style.use('ggplot')\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from numba import jit"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def mandel_python(xs, ys, max_iters):\n",
-      "    c = np.zeros((len(xs), len(ys)), np.int)\n",
-      "    for i, x in enumerate(xs):\n",
-      "        for j, y in enumerate(ys):\n",
-      "            a = complex(x, y)\n",
-      "            z = 0.0j\n",
-      "            for k in range(max_iters):\n",
-      "                z = z*z + a\n",
-      "                if z.real*z.real + z.imag*z.imag >= 4:\n",
-      "                    c[i,j] = k\n",
-      "    return c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xmin, xmax, ymin, ymax = -2, 1, -1, 1\n",
-      "xs = np.linspace(xmin, xmax, 200)\n",
-      "ys = np.linspace(ymin, ymax, 200)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit mandel_python(xs, ys, 200)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 4.37 s per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = mandel_python(xs, ys, 200)\n",
-      "plt.imshow(np.log1p(c.T), extent=[xmin, xmax, ymin, ymax]);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ZrdzzrN9HuS3h8J6uo3iN+J3OZN8HYlAsJX8Ja71wukfJkyBTymD2o4AiE/IN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\nPDzWOf8PtumsAl7CL5gAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1161bd350>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "@jit\n",
-      "def mandel_numba(xs, ys, max_iters):\n",
-      "    c = np.zeros((len(xs), len(ys)), np.int)\n",
-      "    for i, x in enumerate(xs):\n",
-      "        for j, y in enumerate(ys):\n",
-      "            a = complex(x, y)\n",
-      "            z = 0.0j\n",
-      "            for k in range(max_iters):\n",
-      "                z = z*z + a\n",
-      "                if z.real*z.real + z.imag*z.imag >= 4:\n",
-      "                    c[i,j] = k\n",
-      "    return c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit mandel_numba(xs, ys, 200)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 43.2 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = mandel_numba(xs, ys, 200)\n",
-      "plt.imshow(np.log1p(c.T), extent=[xmin, xmax, ymin, ymax]);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ZrdzzrN9HuS3h8J6uo3iN+J3OZN8HYlAsJX8Ja71wukfJkyBTymD2o4AiE/IN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\nl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-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython -a\n",
-      "\n",
-      "import numpy as np\n",
-      "cimport numpy as np\n",
-      "cimport cython\n",
-      "\n",
-      "@cython.boundscheck(False)\n",
-      "@cython.wraparound(False)\n",
-      "def mandel_cython(double[::1] xs, double[::1] ys, int max_iters):\n",
-      "\n",
-      "    m = xs.shape[0]\n",
-      "    n = ys.shape[0]\n",
-      "\n",
-      "    cdef int[:,::1] c = np.zeros((m, n), np.int32)\n",
-      "    cdef int i, j, k\n",
-      "    cdef double complex a, z\n",
-      "\n",
-      "    for i in range(m):\n",
-      "        for j in range(n):\n",
-      "            a.real = xs[i]\n",
-      "            a.imag = ys[j]\n",
-      "            z = 0.0j\n",
-      "            for k in range(max_iters):\n",
-      "                z = z*z + a\n",
-      "                if z.real*z.real + z.imag*z.imag >= 4:\n",
-      "                    c[i,j] = k\n",
-      "    return c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
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-        "<pre class='cython line score-0'>&#xA0;07: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
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-        "static PyMethodDef __pyx_mdef_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython = {\"mandel_cython\", (PyCFunction)__pyx_pw_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython, METH_VARARGS|METH_KEYWORDS, 0};\n",
-        "static PyObject *__pyx_pw_46_cython_magic_7a800035f7e4d9e8e098ba584e858edb_1mandel_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
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-        "<pre class='cython line score-0' onclick='toggleDiv(this)'>+10:     <span class=\"n\">m</span> <span class=\"o\">=</span> <span class=\"n\">xs</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
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-        "<pre class='cython code score-0'>  __pyx_v_n = (__pyx_v_ys.shape[0]);\n",
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-        "<pre class='cython code score-0'>      __pyx_v_z = __pyx_t_double_complex_from_parts(0, 0.0);\n",
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-        "      for (__pyx_t_15 = 0; __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {\n",
-        "        __pyx_v_k = __pyx_t_15;\n",
-        "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+23:                 <span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"n\">z</span><span class=\"o\">*</span><span class=\"n\">z</span> <span class=\"o\">+</span> <span class=\"n\">a</span></pre>\n",
-        "<pre class='cython code score-0'>        __pyx_v_z = __Pyx_c_sum(__Pyx_c_prod(__pyx_v_z, __pyx_v_z), __pyx_v_a);\n",
-        "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+24:                 <span class=\"k\">if</span> <span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">real</span><span class=\"o\">*</span><span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">real</span> <span class=\"o\">+</span> <span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">imag</span><span class=\"o\">*</span><span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">imag</span> <span class=\"o\">&gt;=</span> <span class=\"mf\">4</span><span class=\"p\">:</span></pre>\n",
-        "<pre class='cython code score-4'>        __pyx_t_16 = ((((<span class='pyx_macro_api'>__Pyx_CREAL</span>(__pyx_v_z) * <span class='pyx_macro_api'>__Pyx_CREAL</span>(__pyx_v_z)) + (<span class='pyx_macro_api'>__Pyx_CIMAG</span>(__pyx_v_z) * <span class='pyx_macro_api'>__Pyx_CIMAG</span>(__pyx_v_z))) &gt;= 4.0) != 0);\n",
-        "        if (__pyx_t_16) {\n",
-        "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+25:                     <span class=\"n\">c</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">,</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">k</span></pre>\n",
-        "<pre class='cython code score-0'>          __pyx_t_17 = __pyx_v_i;\n",
-        "          __pyx_t_18 = __pyx_v_j;\n",
-        "          *((int *) ( /* dim=1 */ ((char *) (((int *) ( /* dim=0 */ (__pyx_v_c.data + __pyx_t_17 * __pyx_v_c.strides[0]) )) + __pyx_t_18)) )) = __pyx_v_k;\n",
-        "          goto __pyx_L9;\n",
-        "        }\n",
-        "        __pyx_L9:;\n",
-        "      }\n",
-        "    }\n",
-        "  }\n",
-        "</pre><pre class='cython line score-1' onclick='toggleDiv(this)'>+26:     <span class=\"k\">return</span> <span class=\"n\">c</span></pre>\n",
-        "<pre class='cython code score-1'>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
-        "  __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_c, 2, (PyObject *(*)(char *)) __pyx_memview_get_int, (int (*)(char *, PyObject *)) __pyx_memview_set_int, 0);;<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n",
-        "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
-        "  __pyx_r = __pyx_t_1;\n",
-        "  __pyx_t_1 = 0;\n",
-        "  goto __pyx_L0;\n",
-        "</pre></div></body></html>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "<IPython.core.display.HTML at 0x1161f7590>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit mandel_cython(xs, ys, 200)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 44.6 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = mandel_cython(xs, ys, 200)\n",
-      "plt.imshow(np.log1p(c.T), extent=[xmin, xmax, ymin, ymax]);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ZrdzzrN9HuS3h8J6uo3iN+J3OZN8HYlAsJX8Ja71wukfJkyBTymD2o4AiE/IN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\nl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\ No newline at end of file
diff --git "a/\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Matices.ipynb" "b/\350\213\261\346\226\207\345\216\237\347\211\210\350\257\276\344\273\266\350\265\204\346\226\231/Matices.ipynb"
similarity index 100%
rename from "\346\255\243\345\234\250\347\277\273\350\257\221\347\232\204\350\256\262\345\272\247\345\206\205\345\256\271/Matices.ipynb"
rename to "\350\213\261\346\226\207\345\216\237\347\211\210\350\257\276\344\273\266\350\265\204\346\226\231/Matices.ipynb"