#### Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Title Page
Dedication
Contributors
Preface
Variables, Types, and Functions
Julia Workflow
Distributed Computing
Other Books You May Enjoy
Index

## Improving code performance using @inbounds

Often, especially in performance-critical code, we want to squeeze the maximum speed out of Julia. If you are working with arrays, the @inbounds macro can be used to significantly reduce access time to the elements. The drawback is that you have to be sure that you are not trying to access an out-of-bounds location.

Inspect the inbounds.jl file that contains the following contents:

using BenchmarkTools
mode = ["normal", "@inbounds"]
i = 0
for inbounds in ["", "@inbounds"]
global i += 1
eval(Meta.parse("""function f$i(x::AbstractArray{<:Real}) y = 0$inbounds for i in eachindex(x)
y += x[i] > 0.5
end
y
end"""))
end

x = rand(10^7)
for (idx, f) in enumerate([f1, f2])
println("\n", mode[idx])
@btime $f($x)
end

### Note

In the GitHub repository for this recipe, you...