Book Image

Hands-On High Performance with Go

By : Bob Strecansky
Book Image

Hands-On High Performance with Go

By: Bob Strecansky

Overview of this book

Go is an easy-to-write language that is popular among developers thanks to its features such as concurrency, portability, and ability to reduce complexity. This Golang book will teach you how to construct idiomatic Go code that is reusable and highly performant. Starting with an introduction to performance concepts, you’ll understand the ideology behind Go’s performance. You’ll then learn how to effectively implement Go data structures and algorithms along with exploring data manipulation and organization to write programs for scalable software. This book covers channels and goroutines for parallelism and concurrency to write high-performance code for distributed systems. As you advance, you’ll learn how to manage memory effectively. You’ll explore the compute unified device architecture (CUDA) application programming interface (API), use containers to build Go code, and work with the Go build cache for quicker compilation. You’ll also get to grips with profiling and tracing Go code for detecting bottlenecks in your system. Finally, you’ll evaluate clusters and job queues for performance optimization and monitor the application for performance regression. By the end of this Go programming book, you’ll be able to improve existing code and fulfill customer requirements by writing efficient programs.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Learning about Performance in Go
Section 1: Learning about Performance in Go
Free Chapter
2
Introduction to Performance in Go
Introduction to Performance in Go
Technical requirements
Understanding performance in computer science
A brief history of Go
The ideology behind Go performance
Summary
3
Data Structures and Algorithms
Data Structures and Algorithms
Understanding benchmarking
Introducing Big O notation
Understanding sort algorithms
Understanding search algorithms
Exploring trees
Exploring queues
Summary
4
Understanding Concurrency
Understanding Concurrency
Understanding closures
Exploring goroutines
Introducing channels
Introducing semaphores
Understanding WaitGroups
Iterators and the process of iteration
Briefing on generators
Summary
5
STL Algorithm Equivalents in Go
STL Algorithm Equivalents in Go
Understanding algorithms in the STL
Understanding containers
Understanding function objects
Summary
6
Matrix and Vector Computation in Go
Matrix and Vector Computation in Go
Introducing Gonum and the Sparse library
Introducing BLAS
Introducing vectors
Introducing matrices
Matrix operations
Understanding matrix structures
Summary
7
Section 2: Applying Performance Concepts in Go
Section 2: Applying Performance Concepts in Go
8
Composing Readable Go Code
Composing Readable Go Code
Maintaining simplicity in Go
Maintaining readability in Go
Exploring packaging in Go
Understanding naming in Go
Understanding formatting in Go
Briefing on interfaces in Go
Comprehending methods in Go
Comprehending inheritance in Go
Exploring reflection in Go
Summary
9
Template Programming in Go
Template Programming in Go
Understanding Go generate
The link toolchain
Introducing Cobra and Viper for configuration programming
Text templating
HTML templating
Exploring Sprig
Summary
10
Memory Management in Go
Memory Management in Go
Understanding Modern Computer Memory - A Primer
Allocating memory
Understanding memory utilization
Briefing on limited memory situations
Summary
11
GPU Parallelization in Go
GPU Parallelization in Go
Cgo – writing C in Go
GPU-accelerated computing – utilizing the hardware
CUDA on GCP
CUDA – powering the program
Summary
12
Compile Time Evaluations in Go
Compile Time Evaluations in Go
Exploring the Go runtime
Understanding functions
Summary
13
Section 3: Deploying, Monitoring, and Iterating on Go Programs with Performance in Mind
Section 3: Deploying, Monitoring, and Iterating on Go Programs with Performance in Mind
14
Building and Deploying Go Code
Building and Deploying Go Code
Building Go binaries
Go build – building your Go code
Go clean – cleaning your build directory
Retrieving package dependencies with go get and go mod
Go list
Go run – executing your packages
Go install – installing your binaries
Building Go binaries with Docker
Summary
15
Profiling Go Code
Profiling Go Code
Understanding profiling
Exploring instrumentation methodologies
Briefing on CPU profiling
Briefing on memory profiling
Extended capabilities with upstream pprof
Comparing multiple profiles
Interpreting flame graphs within pprof
Detecting memory leaks in Go
Summary
16
Tracing Go Code
Tracing Go Code
Implementing tracing instrumentation
Understanding the tracing format
Understanding trace collection
Exploring pprof-like traces
Go distributed tracing
Implementing OpenCensus for your application
Summary
17
Clusters and Job Queues
Clusters and Job Queues
Clustering in Go
Exploring job queues in Go
Summary
18
Comparing Code Quality Across Versions
Comparing Code Quality Across Versions
Go Prometheus exporter – exporting data from your Go application
APM – watching your distributed system performance
SLIs and SLOs – setting goals
Logging – keeping track of your data
Summary
19
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Understanding matrix structures

Matrices are usually classified into two different structures: dense matrices and sparse matrices. A dense matrix is composed of mostly non-zero elements. A sparse matrix is a matrix that is mostly composed of elements with a 0 value. The sparsity of a matrix is calculated as the number of elements with a zero value divided by the total count of elements.

If the result of this equation is greater than 0.5, the matrix is sparse. This distinction is important as it helps us to determine the best method for matrix manipulation. If a matrix is sparse, we may be able to use some optimizations to make the matrix manipulation more efficient. Inversely, if we have a dense matrix, we know that we will most likely be performing actions on the whole matrix.

It is important to remember that operations on matrices are most likely going to be memory bound with...

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