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.
Table of Contents (20 chapters)
Section 1: Learning about Performance in Go
Section 2: Applying Performance Concepts in Go
Section 3: Deploying, Monitoring, and Iterating on Go Programs with Performance in Mind

GPU Parallelization in Go

GPU accelerated programming is becoming more and more important in today's high-performance computing stacks. It is commonly used in fields such as Artificial Intelligence (AI) and Machine Learning (ML). GPUs are commonly used for these tasks because they tend to be excellent for parallel computation.

In this chapter, we will learn about Cgo, GPU accelerated programming, CUDA (short for Compute Unified Device Architecture), make commands, C style linking for Go programs, and executing a GPU enabled process within a Docker container. Learning all of these individual things will help us to use a GPU to power a Go backed CUDA program. Doing this will help us to determine how we can use the GPU effectively to help solve computational problems using Go:

  • Cgo – writing C in Go
  • GPU-accelerated computing – utilizing the hardware
  • CUDA on GCP...