About this book

Julia is a high performance, high-level dynamic language designed to address the requirements of high-level numerical and scientific computing. Julia brings solutions to the complexities faced by developers while developing elegant and high performing code.

Julia High Performance will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia.

You will learn to analyze and measure the performance of Julia code, understand how to avoid bottlenecks, and design your program for the highest possible performance. In this book, you will also see how Julia uses type information to achieve its performance goals, and how to use multuple dispatch to help the compiler to emit high performance machine code. Numbers and their arrays are obviously the key structures in scientific computing – you will see how Julia’s design makes them fast. The last chapter will give you a taste of Julia’s distributed computing capabilities.

Publication date:
April 2016


Chapter 1. Julia is Fast

In many ways, the history of programming languages has often been driven by, and certainly intertwined, with the needs of numerical and scientific computing. The first high-level programming language, Fortran, was created with scientific computing in mind, and continues to be important in the field even to this day. In recent years, the rise of data science as a specialty has brought additional focus to scientific computing, particularly for statistical uses. In this area, somewhat counterintuitively, both specialized languages such as R and general-purpose languages such as Python are in widespread use. The rise of Hadoop and Spark has spread the use of Java and Scala respectively among this community. In the midst of all this, Matlab has had a strong niche within engineering and communities, while Mathematica remains unparalleled for symbolic operations.

A new language for scientific computing therefore has a very high barrier to overcome. It's been only a few short years since the Julia language was introduced into the world. In this time, it's innovative features, which make it a dynamic language, based on multiple dispatch as its defining paradigm, has created growing niche within the numerical computing world. However, it's the claim of high performance that excited its early adopters the most.

This, then, is a book that celebrates writing high-performance programs. With Julia, this is not only possible, but also reasonably straightforward, within a low-overhead, dynamic language.

As a reader of this book, you have likely already written your first few Julia programs. We will assume that you have successfully installed Julia, and have a working programming environment available. We expect you are familiar with very basic Julia syntax, but we will discuss and review many of those concepts throughout the book as we introduce them.

  • Julia – fast and dynamic

  • Designed for speed

  • How fast can Julia be?


Julia – fast and dynamic

It is a widely believed myth in programming language communities that high-performance languages and dynamic languages are completely disjoint sets. The perceived wisdom is that, if you want programmer productivity, you should use a dynamic language, such as Ruby, Python or R. On the other hand, if you want fast code execution, you should use a statically typed language such as C or Java.

There are always exceptions to this rule. However, for most mainstream programmers, this is a strongly held belief.

This usually manifests itself in what is known as the "two language" problem. This is something that is especially prominent in scientific computing. This is the situation where the performance-critical inner kernel is written in C, but is then wrapped and used from a dynamic, higher-level language. Code written in traditional, scientific computing environments such as R, Matlab or NumPy follows this paradigm.

Code written in this fashion is not without its drawbacks however. Even though it looks like this gets you the best of both worlds — fast computation, while allowing the programmer to use a high-level language — this is a path full of hidden dangers. For one, someone will have to write the low-level kernel. So, you need two different skillsets. If you are lucky to find the low level code in C for your project, you are fine. However, if you are doing anything new or original, or even slightly different from the norm, you will find yourself writing both C and a high-level language. This severely limits the number of contributors that your projects or research will get: to be really productive, they have to be familiar with two languages.

Secondly, when running code routinely written in two languages, there can be severe and unforeseen performance pitfalls. When you can drop down to C code quickly, everything is fine. However, if, for whatever reason, your code cannot call into a C routine, you'll find your program taking hundreds or even thousands of times more longer than you expected.

Julia is the first modern language to make a reasonable effort to solve the "two language" problem. It is a high-level, dynamic, language with powerful features that make for a very productive programmer. At the same time, code written in Julia usually runs very fast, almost as fast as code written in statically typed languages.

The rest of this chapter describes some of the underlying design decisions that make Julia such a fast language. We also see some evidence of the performance claims for Julia.

The rest of the book shows you how to write your Julia programs in a way that optimizes its time and memory usage to the maximum. We will discuss how to measure and reason performance in Julia, and how to avoid potential performance pitfalls.

For all the content in this book, we will illustrate our point individually with small and simple programs. We hope that this will enable you grasp the crux of the issue, without getting distracted by unnecessary elements of a larger program. We expect that this methodology will therefore provide you with an instinctive intuition about Julia's performance profile.

Julia has a refreshingly simple performance model – and thus writing fast Julia code is a matter of understanding a few key elements of computer architecture, and how the Julia compiler interacts with it. We hope that, by the end of this book, your instincts are well developed to design and write your own Julia code with the fastest possible performance.


Versions of Julia

Julia is a fast moving project, with an open development process. All the code and examples in this book are targeted at version 0.4 of the language, which is the currently released version at the time of publication. Check Packt's website for changes and errata for future versions of Julia.


Designed for speed

When the creators of Julia launched the language into the world, they said the following in a blog post entitled Why We Created Julia, which was published in early 2012:

"We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.

(Did we mention it should be as fast as C?)"

High performance, indeed nearly C-level performance, has therefore been one of the founding principles of the language. It's built from the ground up to enable a fast execution of code.

In addition to being a core design principle, it has also been a necessity from the early stages of its development. A very large part of Julia's standard library, including very basic low-level operations, is written in Julia itself. For example, the + operation to add two integers is defined in Julia itself. (Refer to: https://github.com/JuliaLang/julia/blob/1986c5024db36b4c921130351597f5b4f9f81691/base/int.jl#L8). Similarly, the basic for loop uses the standard iteration mechanism available to all user-defined types. This means that the implementation had to be very fast from the very beginning to create a usable language. The creators of Julia did not have the luxury of escaping to C for even the core elements of the library.

We will note throughout the book many design decisions that have been made with an eye to high performance. But there are three main elements that create the basis for Julia's speed.


Julia is a Just In Time (JIT) compiled language, rather than an interpreted one. This allows Julia to be dynamic, without having the overhead of interpretation. This compilation infrastructure is build on top of Low Level Virtual Machine (LLVM) (http://llvm.org).

The LLVM compiler without infrastructure project originated at University of Illinois. It now has contributions from a very large number of corporate as well as independent developers. As a result of all this work, it is now a very high-quality, yet modular, system for many different compilation and code generation activities.

Julia uses LLVM for its JIT compilation needs. The Julia runtime generates LLVM Intermediate Representation (IR) and hands it over to LLVM's JIT compiler, which in turn generates machine code that is executed on the CPU. As a result, sophisticated compilation techniques that are built into LLVM are ready and available to Julia, from the simple (such as Loop Unrolling or Loop Deletion) to state-of-the-art (such as SIMD Vectorization) ones. These compiler optimizations form a very large body of work, and in this sense, the existence is of LLVM is very much a pre-requisite to the existence of Julia. It would have been an almost impossible task for a small team of developers to build this infrastructure from scratch.


Just-In-Time compilation

Just-in-Time compilation is a technique in which the code in a high-level language is converted to machine code for execution on the CPU at runtime. This is in contrast to interpreted languages, whose runtime executes the source language directly. This usually has a significantly higher overhead. On the other hand, Ahead of Time (AOT) compilation refers to the technique of converting source language into machine code as a separate step prior to running the code. In this case, the converted machine code can usually be saved to disk as an executable file.


We will have much more to say about types in Julia throughout this book. At this stage, suffice it to say that Julia's concept of types is a key ingredient in its performance.

The Julia compiler tries to infer the type of all data used in a program, and compiles different versions of functions specialized to particular types of its arguments. To take a simple example, consider the sqrt function. This function can be called with integer or floating-point arguments. Julia will compile two versions of the code, one for integer arguments, and one for floating point arguments. This means that, at runtime, fast, straight-line code without any type checks will be executed on the CPU.

The ability of the compiler to reason about types is due to the combination of a sophisticated dataflow-based algorithm, and careful language design that allows this information to be inferred from most programs before execution begins. Put in another way, the language is designed to make it easy to statically analyze.

If there is a single reason for Julia is being such a high-performance language, this is it. This is why Julia is able to run at C-like speeds while still being a dynamic language. Type inference and code specialization are as close to a secret sauce as Julia gets. It is notable that, outside this type inference mechanism, the Julia compiler is quite simple. It does not include many advanced Just in Time optimizations that Java and JavaScript compilers are known to use. When the compiler has enough information about the types within the code, it can generate optimized, straight-line, code without many of these advanced techniques.

It is useful to note here that unlike some other optionally typed dynamic languages, simply adding type annotations to your code does not usually make Julia go any faster. Type inference means that the compiler is, in most cases, able to figure out the types of variables when necessary. Hence you can usually write high-level code without fighting with the compiler about types, and still achieve superior performance.


How fast can Julia be?

The best evidence of Julia's performance claims is when you write your own code. However, we can provide an indication of how fast Julia can be by comparing a similar algorithm over multiple languages.

As an example, let's consider a very simple routine to calculate the power sum for a series, as follows:

The following code runs this computation in Julia 500 times:

function pisum()
    sum = 0.0
    for j = 1:500
        sum = 0.0
        for k = 1:10000
            sum += 1.0/(k*k)

You will notice that this code contains no type annotations. It should look quite familiar to any modern dynamic language. The same algorithm implemented in C would look something similar to this:

double pisum() {
    double sum = 0.0;
    for (int j=0; j<500; ++j) {
        sum = 0.0;
        for (int k=1; k<=10000; ++k) {
            sum += 1.0/(k*k);
    return sum;


Downloading the example code

You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

You can download the code files by following these steps:

  • Log in or register to our website using your e-mail address and password

  • Let the mouse pointer hover on the SUPPORT tab at the top

  • Click on Code Downloads & Errata

  • Enter the name of the book in the Search box

  • Select the book for which you're looking to download the code files

  • Choose from the drop-down menu where you purchased this book from

  • Click on Code Download

You can also download the code files by clicking on the Code Files button on the book's webpage at the Packt Publishing website. This page can be accessed by entering the book's name in the Search box. Please note that you need to be logged in to your Packt account.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows

  • Zipeg/iZip/UnRarX for Mac

  • 7-Zip/PeaZip for Linux

By timing this code, and its re-implementation in many other languages (all of which are available at https://github.com/JuliaLang/julia/tree/master/test/perf/micro), we can note that Julia's performance claims are certainly borne out in this limited test. Julia can perform at a level similar to C and other statically typed and compiled languages.

This is of course a micro benchmark, and should therefore not be extrapolated too much. However, I hope you will agree that it is possible to achieve excellent performance in Julia. The rest of the book will attempt to show how you can achieve performance close to this standard in your code.



In this chapter, you noted that Julia is a language that is built from the ground up for high performance. Its design and implementation have always been focused on providing the highest possible performance on the modern CPU.

The rest of the book will show you how to use the power of Julia to the maximum, to write the fastest possible code in this language. In the next chapter, we will discuss how to measure the speed of Julia code, and identify performance bottlenecks. You will learn some of the tools that are built into Julia for this purpose.

About the Author

  • Avik Sengupta

    Avik Sengupta is the Vice President of engineering at Julia Computing, contributor to open source Julia and maintainer of several Julia packages. Avik is the co-founder of two startups in the financial services and AI sectors and creator of large complex trading systems for the world's leading investment banks. Prior to Julia Computing, Avik was co-founder and CTO at AlgoCircle and at Itellix, director at Lab49 and head of algorithmic solutions at Decimal Point Analytics. Avik earned his MS in Computational Finance at Carnegie Mellon and MBA Finance at the Indian Institute of Management in Bangalore.

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Latest Reviews

(4 reviews total)
Y really love Julia language, this book cover many key aspects of high performance development with Julia, thank you very much!
Well - I thought I was purchacing a lot more - that the book would be bigger! But that's my fault for not really looking
Full marks for this book. This is an excellent book for someone who has reached the stage of being able to write and debug Julia programs, and would like to improve code to take advantage of Julia's performance. It provides descriptions of how to measure performance, and then provides information about aspects of Julia's compilation and language structure in order that the reader can understand ways that performance can be improved. The technical aspects of the book provide something I will certainly be able to use.The writing style adds to this, as it is clear and concise. It doesn't make the mistake of describing in detail things that a reader who has advanced beyond the beginner stage should already know. That is often just a way of padding the content.
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