1596353640
Code generation time is a significant contributor to total build time for optimized builds. During this step of the compilation process, source code is passed through several algorithms that transform your program into an optimized version that can be executed more efficiently. In general, the optimization of a C++ function happens quickly and poses no problem. In exceptional cases, however, some functions can become large and complex enough to put pressure on the optimizer and noticeably slow down your builds. In this article, we show how you can use C++ Build Insights to determine if slow code generation is a problem for you. We demonstrate two ways to diagnose these issues: first manually with the vcperf analysis tool, and then programmatically with the C++ Build Insights SDK. Throughout the tutorial, we show these techniques being used to improve the build time of Chakra, the open-source JavaScript engine, by 7%.
How to obtain and use vcperf
The examples in this article make use of vcperf, a tool that allows you to capture a trace of your build and to view it in the Windows Performance Analyzer (WPA). The latest version is available in Visual Studio 2019.
1. Follow these steps to obtain and configure _vcperf _and WPA:
perf_msvcbuildinsights.dll
file from your Visual Studio 2019’s MSVC installation directory to your newly installed WPA directory. This file is the C++ Build Insights WPA add-in, which must be available to WPA for correctly displaying the C++ Build Insights events.C:\Program Files (x86)\Microsoft Visual Studio\2019\{Edition}\VC\Tools\MSVC\{Version}\bin\Hostx64\x64
.C:\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit
.perfcore.ini
file in your WPA installation directory, and add an entry for the perf_msvcbuildinsights.dll
file. This tells WPA to load the C++ Build Insights plugin on startup.You can also obtain the latest vcperf and WPA add-in by cloning and building the vcperf GitHub repository. Feel free to use your built copy in conjunction with Visual Studio 2019!
2. Follow these steps to collect a trace of your build:
vcperf /start MySessionName
.vcperf /stop MySessionName outputFile.etl
. This command will stop the trace, analyze all events, and save everything in the outputFile.etl trace file.Using the Functions view in WPA
C++ Build Insights has a dedicated view to help diagnose slow code generation time: the Functions view. After opening your trace in WPA, you can access the view by dragging it from the Graph Explorer pane to the Analysis window, as shown below.
The Functions view offers 3 presets that you can select from when navigating your build trace:
Click on the drop-down menu at the top of the view to select the one you need. This step is illustrated below.
In the next 3 sections, we cover each of these presets in turn.
#c++ #diagnostics #programming-c #cplusplus
1618698960
The C++ team at Visual Studio has delivered substantial build and link time improvements throughout Visual Studio 2019. This blog is Part 2 of a series of blogs showcasing real-world results of our efforts. See how the Gears 5 team benefited from iteration build time improvements in Part 1.
#c++ #performance #build throughput #build time #compile time #game development #games #gaming #iteration time #linker #video games
1596353640
Code generation time is a significant contributor to total build time for optimized builds. During this step of the compilation process, source code is passed through several algorithms that transform your program into an optimized version that can be executed more efficiently. In general, the optimization of a C++ function happens quickly and poses no problem. In exceptional cases, however, some functions can become large and complex enough to put pressure on the optimizer and noticeably slow down your builds. In this article, we show how you can use C++ Build Insights to determine if slow code generation is a problem for you. We demonstrate two ways to diagnose these issues: first manually with the vcperf analysis tool, and then programmatically with the C++ Build Insights SDK. Throughout the tutorial, we show these techniques being used to improve the build time of Chakra, the open-source JavaScript engine, by 7%.
How to obtain and use vcperf
The examples in this article make use of vcperf, a tool that allows you to capture a trace of your build and to view it in the Windows Performance Analyzer (WPA). The latest version is available in Visual Studio 2019.
1. Follow these steps to obtain and configure _vcperf _and WPA:
perf_msvcbuildinsights.dll
file from your Visual Studio 2019’s MSVC installation directory to your newly installed WPA directory. This file is the C++ Build Insights WPA add-in, which must be available to WPA for correctly displaying the C++ Build Insights events.C:\Program Files (x86)\Microsoft Visual Studio\2019\{Edition}\VC\Tools\MSVC\{Version}\bin\Hostx64\x64
.C:\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit
.perfcore.ini
file in your WPA installation directory, and add an entry for the perf_msvcbuildinsights.dll
file. This tells WPA to load the C++ Build Insights plugin on startup.You can also obtain the latest vcperf and WPA add-in by cloning and building the vcperf GitHub repository. Feel free to use your built copy in conjunction with Visual Studio 2019!
2. Follow these steps to collect a trace of your build:
vcperf /start MySessionName
.vcperf /stop MySessionName outputFile.etl
. This command will stop the trace, analyze all events, and save everything in the outputFile.etl trace file.Using the Functions view in WPA
C++ Build Insights has a dedicated view to help diagnose slow code generation time: the Functions view. After opening your trace in WPA, you can access the view by dragging it from the Graph Explorer pane to the Analysis window, as shown below.
The Functions view offers 3 presets that you can select from when navigating your build trace:
Click on the drop-down menu at the top of the view to select the one you need. This step is illustrated below.
In the next 3 sections, we cover each of these presets in turn.
#c++ #diagnostics #programming-c #cplusplus
1589835780
Source generators are a new feature of the C# compiler that enables inspecting user code using compiler-generated metadata and generating additional source files to be compiled along with the rest of program.
Loosely inspired by F# type providers, C# source generators respond to the same aim of enabling metaprogramming but in a completely different way. Indeed, while F# type providers emit types, properties, and methods in-memory, source generators emit C# code back into the compilation process.
#c# #c #c++ #programming #programming c
1604008800
Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.
Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.
“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”
We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.
We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.
Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast
module, and wide adoption of the language itself.
Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:
As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:
The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.
A token might consist of either a single character, like (
, or literals (like integers, strings, e.g., 7
, Bob
, etc.), or reserved keywords of that language (e.g, def
in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.
Python provides the tokenize
module in its standard library to let you play around with tokens:
Python
1
import io
2
import tokenize
3
4
code = b"color = input('Enter your favourite color: ')"
5
6
for token in tokenize.tokenize(io.BytesIO(code).readline):
7
print(token)
Python
1
TokenInfo(type=62 (ENCODING), string='utf-8')
2
TokenInfo(type=1 (NAME), string='color')
3
TokenInfo(type=54 (OP), string='=')
4
TokenInfo(type=1 (NAME), string='input')
5
TokenInfo(type=54 (OP), string='(')
6
TokenInfo(type=3 (STRING), string="'Enter your favourite color: '")
7
TokenInfo(type=54 (OP), string=')')
8
TokenInfo(type=4 (NEWLINE), string='')
9
TokenInfo(type=0 (ENDMARKER), string='')
(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)
#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer
1624240146
C and C++ are the most powerful programming language in the world. Most of the super fast and complex libraries and algorithms are written in C or C++. Most powerful Kernel programs are also written in C. So, there is no way to skip it.
In programming competitions, most programmers prefer to write code in C or C++. Tourist is considered the worlds top programming contestant of all ages who write code in C++.
During programming competitions, programmers prefer to use a lightweight editor to focus on coding and algorithm designing. Vim, Sublime Text, and Notepad++ are the most common editors for us. Apart from the competition, many software developers and professionals love to use Sublime Text just because of its flexibility.
I have discussed the steps we need to complete in this blog post before running a C/C++ code in Sublime Text. We will take the inputs from an input file and print outputs to an output file without using freopen
file related functions in C/C++.
#cpp #c #c-programming #sublimetext #c++ #c/c++