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    "# Parallel Computing with Julia\n",
    "\n",
    "This course will cover:\n",
    "\n",
    "* Introduction to parallelism\n",
    "    * What is happening to our computers?\n",
    "\n",
    "* Parallelism strategies\n",
    "    * SIMD\n",
    "    * Multi-threading\n",
    "    * Tasks\n",
    "    * Multi-process\n",
    "        * Shared memory\n",
    "        * Distributed memory\n",
    "    * GPU programming\n",
    "\n",
    "* Challenges of parallel computing\n",
    "    * Order of execution\n",
    "        * execution of out order of Possibility\n",
    "        * simultaneous access and mutation\n",
    "    * Data access and movement\n",
    "    * Code access and movement\n",
    "    * Appropriately matching the parallelism strategy to your machine capabilities\n",
    "    * Appropriately matching the parallelism strategy with the problem at hand"
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   "source": [
    "## What is happening to our computers!?\n",
    "\n",
    "![](https://raw.githubusercontent.com/JuliaComputing/JuliaAcademyData.jl/master/courses/Parallel_Computing/images/40-years-processor-trend.png)\n",
    "\n",
    "Not only have we gained multiple cores, but processors have become extremely\n",
    "complex, with multiple levels of caches, pipelines, predictions, speculations...\n",
    "\n",
    "## What is hard about parallel computing\n",
    "  * We don't think in parallel\n",
    "  * We learn to write and reason about programs serially\n",
    "  * The desire for parallelism often comes _after_ you've written your algorithm (and found it too slow!)\n",
    "\n",
    "## Summary:\n",
    "  * Current computer archetectures push us towards parallel programming for peak performance — even if we're not on a cluster!\n",
    "  * But it's hard to design good parallel algorithms\n",
    "  * And it's hard to express and reason about those algorithms"
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