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10_intro_julia.ipynb

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"The core design to make Julia fast is type-stability through specialization via multiple-dispatch. A single function in Julia can take different types of input and compile the function in specialized ways. This \"specialization\" brings huge performance gains to Julia. Let's first go over a simple example, namely scalar multiplication, and use the macro `@code_llvm` to check what Julia compiles it to (LLVM is a type of portable assembly language)."
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"The core design to make Julia fast is type-stability through specialization via multiple-dispatch. A single function in Julia can take different types of input and compile the function in specialized ways. This \"specialization\" brings huge performance gains to Julia. Let's first go over a simple example, namely scalar multiplication, and use the macro `@which` to check the method being used"
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"project": "4qWjnDwuuPUd70Df3LsC",
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"In this example, we want to compute the multiplication of 2 integers. As we can see, the underlying assembly code is running the multiplication of integers."
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"In this example, we want to compute the multiplication of 2 integers. As we can see, the underlying method is running the multiplication of integers."
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"source": [
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"If we multiply 2 floating point numbers, we can see that a different (specialized) assembly code is used, which is designed specifically for the type `double` (`Float64` in Julia)."
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"If we multiply 2 floating point numbers, we can see that a different method is used, which is designed specifically for the type `double` (`Float64` in Julia)."
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"kernelspec": {

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