Julia High Performance电子书

Julia High Performance

Table of Contents

Julia High Performance

Credits

About the Author

About the Reviewer

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Julia is Fast

Julia – fast and dynamic

Designed for speed

JIT and LLVM

Types

How fast can Julia be?

Summary

2. Analyzing Julia Performance

Timing Julia code

Tic and Toc

The @time macro

The @timev macro

The Julia profiler

Using the profiler

ProfileView

Analyzing memory allocation

Using the memory allocation tracker

Statistically accurate benchmarking

Using Benchmarks.jl

Summary

3. Types in Julia

The Julia type system

Using types

Multiple dispatch

Abstract types

Julia's type hierarchy

Composite and immutable types

Type parameters

Type inference

Type-stability

Definitions

Fixing type-instability

Performance pitfalls

Identifying type-stability

Loop variables

Kernel methods

Types in storage locations

Arrays

Composite types

Parametric composite types

Summary

4. Functions and Macros – Structuring Julia Code for High Performance

Using globals

The trouble with globals

Fixing performance issues with globals

Inlining

Default inlining

Controlling inlining

Disabling inlining

Closures and anonymous functions

FastAnonymous

Using macros for performance

The Julia compilation process

Using macros

Evaluating a polynomial

Horner's method

The Horner macro

Generated functions

Using generated functions

Using generated functions for performance

Using named parameters

Summary

5. Fast Numbers

Numbers in Julia

Integers

Integer overflow

BigInt

The floating point

Unchecked conversions for unsigned integers

Trading performance for accuracy

The fastmath macro

The K-B-N summation

Subnormal numbers

Subnormal numbers to zero

Summary

6. Fast Arrays

Array internals in Julia

Array representation and storage

Column-wise storage

Bound checking

Removing the cost of bound checking

Configuring bound checks at startup

Allocations and in-place operations

Preallocating function output

Mutating versions

Array views

SIMD parallelization

Yeppp!

Writing generic library functions with arrays

Summary

7. Beyond the Single Processor

Parallelism in Julia

Starting a cluster

Communication between Julia processes

Programming parallel tasks

@everywhere

@spawn

Parallel for

Parallel map

Distributed arrays

Shared arrays

Threading

Summary

Index