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Hands-On High Performance with Go

You're reading from Hands-On High Performance with Go Boost and optimize the performance of your Golang applications at scale with resilience

Product type Paperback

Published in Mar 2020

Publisher Packt

ISBN-13 9781789805789

Length 406 pages

Edition 1st Edition

Languages

Go

Tools

Boost

Concepts

High Performance Programming

Author (1):

Bob Strecansky

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Table of Contents (20) Chapters

Preface

1. Section 1: Learning about Performance in Go

2. Introduction to Performance in Go FREE CHAPTER

3. Data Structures and Algorithms

4. Understanding Concurrency

5. STL Algorithm Equivalents in Go

6. Matrix and Vector Computation in Go

7. Section 2: Applying Performance Concepts in Go

8. Composing Readable Go Code

9. Template Programming in Go

10. Memory Management in Go

11. GPU Parallelization in Go

12. Compile Time Evaluations in Go

13. Section 3: Deploying, Monitoring, and Iterating on Go Programs with Performance in Mind

14. Building and Deploying Go Code

15. Profiling Go Code

16. Tracing Go Code

17. Clusters and Job Queues

18. Comparing Code Quality Across Versions

19. Other Books You May Enjoy

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Who this book is for

This Go book is a must for developers and professionals who have an intermediate-to-advanced understanding of Go programming and are interested in improving their speed of code execution.

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Authors (1)

Strecansky

Strecansky

Bob Strecansky is a senior site reliability engineer. He graduated with a computer engineering degree from Clemson University with a focus on networking. He has worked in both consulting and industry jobs since graduation. He has worked with large telecom companies and much of the Alexa top 500. He currently works at Mailchimp, working to improve web performance, security, and reliability for one of the world's largest email service providers. He has also written articles for web publications and currently maintains the OpenTelemetry PHP project. In his free time, Bob enjoys tennis, cooking, and restoring old cars. You can follow Bob on the internet to hear musings about performance analysis: Twitter: @bobstrecansky GitHub: @bobstrecansky

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