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

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...