Write Great Code, Volume 2, 2nd Edition: Thinking Low-Level, Writing High-Level
656
Write Great Code, Volume 2, 2nd Edition: Thinking Low-Level, Writing High-Level
656Paperback
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Overview
Today's programming languages offer productivity and portability, but also make it easy to write sloppy code that isn't optimized for a compiler. Thinking Low-Level, Writing High-Level will teach you to craft source code that results in good machine code once it's run through a compiler.
You'll learn:
With an understanding of how compilers work, you'll be able to write source code that they can translate into elegant machine code.
NEW TO THIS EDITION, COVERAGE OF:
Product Details
| ISBN-13: | 9781718500389 |
|---|---|
| Publisher: | No Starch Press |
| Publication date: | 08/11/2020 |
| Pages: | 656 |
| Sales rank: | 1,049,993 |
| Product dimensions: | 7.00(w) x 9.20(h) x 1.60(d) |
About the Author
Read an Excerpt
INTRODUCTION
There are many aspects of great code—far too many to describe properly in a single book. Therefore, this second volume of the Write Great Code series concentrates on one important part of great code: performance. As computer systems have increased in performance from MHz, to hundreds of MHz, to GHz, the performance of computer software has taken a back seat to other concerns. Today, it is not at all uncommon for software engineers to exclaim, “You should never optimize your code!” Funny, you don’t hear too many computer application users making such statements.
Although this book describes how to write efficient code, it is not a book about optimization. Optimization is a phase near the end of the software development cycle in which software engineers determine why their code does not meet performance specifications and then massage the code to achieve those specifications. But unfortunately, if no thought is put into the performance of the application until the optimization phase, it’s unlikely that optimization will prove practical. The time to ensure that an application has reasonable performance characteristics is at the beginning, during the design and implementation phases. Optimization can fine-tune the performance of a system, but it can rarely deliver a miracle.
Although the quote is often attributed to Donald Knuth, who popularized it, it was Tony Hoare who originally said, “Premature optimization is the root of all evil.” This statement has long been the rallying cry of software engineers who avoid any thought of application performance until the very end of the software-development cycle—at which point the optimization phase is typically ignored for economic or time-to-market reasons. However, Hoare did not say, “Concern about application performance during the early stages of an application’s development is the root of all evil.” He specifically said premature optimization, which, back then, meant counting cycles and instructions in assembly language code—not the type of coding you want to do during initial program design, when the code base is rather fluid. So, Hoare’s comments were on the mark. The following excerpt from a short essay by Charles Cook (www.cookcomputing.com/blog/archives/ 000084.html) describes the problem with reading too much into this statement:
I’ve always thought this quote has all too often led software designers into serious mistakes because it has been applied to a different problem domain to what was intended.
The full version of the quote is “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.” and I agree with this. It’s usually not worth spending a lot of time micro-optimizing code before it’s obvious where the performance bottlenecks are. But, conversely, when designing software at a system level, performance issues should always be considered from the beginning. A good software developer will do this automatically, having developed a feel for where performance issues will cause problems. An inexperienced developer will not bother, misguidedly believing that a bit of fine tuning at a later stage will fix any problems.
Hoare was really saying that software engineers should worry about other issues, like good algorithm design and good implementations of those algorithms, before they worry about traditional optimizations, like how many CPU cycles a particular statement requires for execution.
Although you could certainly apply many of this book’s concepts during an optimization phase, most of the techniques here really need to be done during initial coding. If you put them off until you reach “code complete,” it’s unlikely they will ever find their way into your software. It’s just too much work to implement these ideas after the fact.
This book will teach you how to choose appropriate high-level language (HLL) statements that translate into efficient machine code with a modern optimizing compiler. With most HLLs, using different statements provides many ways to achieve a given result; and, at the machine level, some of these ways are naturally more efficient than others. Though there may be a very good reason for choosing a less-efficient statement sequence over a more efficient one (e.g., for readability purposes), the truth is that most software engineers have no idea about the runtime costs of HLL statements. Without such knowledge, they are unable to make an educated choice concerning statement selection. The goal of this book is to change that.
An experienced software engineer may argue that the implementation of these individual techniques produces only minor improvements in performance. In some cases, this evaluation is correct; but we must keep in mind that these minor effects accumulate. While one can certainly abuse the techniques this book suggests, producing less readable and less maintainable code, it only makes sense that, when presented with two otherwise equivalent code sequences (from a system design point of view), you should choose the more efficient one. Unfortunately, many of today’s software engineers don’t know which of two implementations actually produces the more efficient code.
Though you don’t need to be an expert assembly language programmer in order to write efficient code, if you’re going to study compiler output (as you will do in this book), you’ll need at least a reading knowledge of it. Chapters 3 and 4 provide a quick primer for 80x86 and PowerPC assembly language.
In Chapters 5 and 6, you’ll learn about determining the quality of your HLL statements by examining compiler output. These chapters describe disassemblers, object code dump tools, debuggers, various HLL compiler options for displaying assembly language code, and other useful software tools.
The remainder of the book, Chapters 7 through 15, describes how compilers generate machine code for different HLL statements and data types. Armed with this knowledge, you will be able to choose the most appropriate data types, constants, variables, and control structures to produce efficient applications.
While you read, keep Dr. Hoare’s quote in mind: “Premature optimization is the root of all evil.” It is certainly possible to misapply the information in this book and produce code that is difficult to read and maintain. This would be especially disastrous during the early stages of your project’s design and implementation, when the code is fluid and subject to change. But remember: This book is not about choosing the most efficient statement sequence, regardless of the consequences; it is about understanding the cost of various HLL constructs so that, when you have a choice, you can make an educated decision concerning which sequence to use. Sometimes, there are legitimate reasons to choose a less efficient sequence. However, if you do not understand the cost of a given statement, there is no way for you to choose a more efficient alternative.
Table of Contents
IntroductionChapter 1: Thinking Low-Level, Writing High-Level
Chapter 2: Shouldn’t You Learn Assembly Language?
Chapter 3: 80x86 Assembly for the HLL Programmer
Chapter 4: Compiler Operation and Code Generation
Chapter 5: Tools for Analyzing Compiler Output
Chapter 6: Constants and High-Level Languages
Chapter 7: Variables in a High-Level Language
Chapter 8: Array Data Types
Chapter 9: Pointer Data Types
Chapter 10: String Data Types
Chapter 11: Record, Union, and Class Data Types
Chapter 12: Arithmetic and Logical Expressions
Chapter 13: Control Structures and Programmatic Decisions
Chapter 14: Iterative Control Structures
Chapter 15: Functions and Procedures
Afterword: Engineering Software
Glossary