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F# High Performance
F# High Performance

F# High Performance: Increase your F# programming productivity and focus on performance optimization with best practices, expert techniques, and more

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F# High Performance

Chapter 2. Performance Measurement

Performance measurement is often subject to many debates, but we should approach the ways of solving performance problems as straightforwardly as possible while maintaining objective processes. The results must be as objective as they can. To correctly define that a performance optimization is needed or not, we must be able to measure the running code objectively. To ensure the objectiveness of the performance measurement, the result must be visible as quantitative (in numbers) and qualitative by analyzing how the code behaves when it runs, how fast it runs, and how big the code is in memory.

As a rule of thumb, it is easier to analyze quantitatively as data can be seen and compared more directly than when analyzed qualitatively. Understanding how to measure and how to interpret the measurement result can be used as a foundation for deducing the cause of any performance bottlenecks and can be further used in combination with qualitative analytics...

Introduction to the nature of F# code compilation

Before we measure .NET, we need to understand the nature of F# code and the nature of compiling the F# code. Of course, we can just analyze and look at the source code. We can also try to search for any patterns of non-performing code of known bottlenecks, which will be explained in Chapter 3,Optimizing Data Structures and Chapter 7, Language Features and Constructs Optimization.

General overview of F# compiler processes and results

All of the code in F# is compiled into byte codes of the IL assembly (as it's also an assembly language) either by the F# compiler or by using F# in interactive mode (by using FSI). This IL is then run in either Debug mode as Just In Time (JIT) code is further optimized by compiling it in Release mode. Running it in Release mode will yield compiled code to be optimized for .NET native by the Ahead Of Time (AOT) method.

For more information about .NET native, here is the official explanation in the MSDN Library...

Using CLR Profiler 4.5

CLR Profiler is a profiler tool used to profile any compiled PE of .NET. It is free and open source, although it is not available by default in the current installation of .NET and Visual Studio 2015.

A profiler is basically a helper tool to analyze the elements of a running program that relates to performances, such as:

  • Memory consumptions
  • Execution time allocations in terms of classes and modules into smaller elements such as functions and subroutines
  • Examining garbage collections (if the platform has support for garbage collection)

Based on the previous definitions, the CLR Profiler is simply a profiler to examine (or to profile) any application that runs on top of .NET CLR.

There are profilers toolings for .NET, not just CLR Profiler, out there, unfortunately not all the toolings are free. Fortunately, CLR Profiler has complete features to profile .NET application, and it is also free and open source. We can also build our own profiler based on the existing CLR Profiler...

Ways to measure performance quantitatively

There are many ways to measure performances of any running .NET code on top of CLR, including F#. So, any tool used to measure F# code is essentially usable for other programming languages on .NET CLR as well.

Again, the easiest way to objectively measure performance is by using quantitative measurements. The following are some common ways of measuring running codes:

  • Using .NET timers
  • Using native Win32 timer
  • Using Visual Studio Diagnostic
  • Running functions inside unit tests

The previous numbers are ordered from the subtlest and difficult to the quickest way of having statistical timing as performance measurements.

Using .NET timers

There are many timer classes in the .NET Framework but not all of them have a general purpose from the perspective of implementation details and in the context of execution environments.

In the context of execution environments, .NET timers are divided as follows:

  • UI timers (timers that are run on an UI thread)
  • Non-UI timer (timers...

Summary

You learned that you can measure performance quantitatively and qualitatively. Although it is easier to have quantitative measurement than qualitative measurement, it is quite straightforward and common to have quantified data as a foundation to have qualitative analysis.

With the help of .NET tooling ecosystem and libraries available at our disposal, we can ensure the objectiveness of our performance measurement. It is also recommended to have a basic understanding of .NET CLR memory management as our foundation to have performance measurement from the perspective of qualitative analysis.

We will start exploring the aspects of performance optimizations with the knowledge and concept in this chapter, starting from F# data structures in Chapter 3, Optimizing Data Structures.

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Key benefits

  • Explore the advanced concurrency support in F# and .NET TPL
  • Covers major optimization techniques in F# to improve the performance of applications
  • Use Struct, Class and Record model, Interop with C# and VB without sacrificing performance.

Description

F# is a functional programming language and is used in enterprise applications that demand high performance. It has its own unique trait: it is a functional programming language and has OOP support at the same time. This book will help you make F# applications run faster with examples you can easily break down and take into your own work. You will be able to assess the performance of the program and identify bottlenecks. Beginning with a gentle overview of concurrency features in F#, you will get to know the advanced topics of concurrency optimizations in F#, such as F# message passing agent of MailboxProcessor and further interoperation with .NET TPL. Based on this knowledge, you will be able to enhance the performance optimizations when implementing and using other F# language features. The book also covers optimization techniques by using F# best practices and F# libraries. You will learn how the concepts of concurrency and parallel programming will help in improving the performance. With this, you would be able to take advantage of multi-core processors and track memory leaks, root causes, and CPU issues. Finally, you will be able to test their applications to achieve scalability.

Who is this book for?

This book is for F# developers who want to build high-performance applications. Knowledge of functional programming would be helpful.

What you will learn

  • Understand how the execution of functions in F# works
  • Identify common performance bottlenecks
  • Implement best practices to optimize performance
  • Use the available tooling to help measure performance
  • Combine the best practice of asynchronous and synchronous
  • Optimize further using various F# language constructs
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Publication date : Jan 18, 2017
Length: 338 pages
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Language : English
ISBN-13 : 9781786468079
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ISBN-13 : 9781786468079
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Microsoft
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Table of Contents

8 Chapters
1. Performing Common Optimizations in F# Chevron down icon Chevron up icon
2. Performance Measurement Chevron down icon Chevron up icon
3. Optimizing Data Structures Chevron down icon Chevron up icon
4. Introduction to Concurrency in F# Chevron down icon Chevron up icon
5. Advanced Concurrency Support in F# Chevron down icon Chevron up icon
6. Optimizing Type Provider Chevron down icon Chevron up icon
7. Language Features and Constructs Optimization Chevron down icon Chevron up icon
8. Optimizing Computation Expressions Chevron down icon Chevron up icon

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Musa Mar 12, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Steps into Assembly language exposure to abstract functional language F#. Steps up to illustrate by building Message Queues, Computation Expressions, Type Providers,, with examples. CE example builds WPF window with panel & buttons etc from within F# syntax . F# pronounced F Sharp has a shorter cleaner syntax to other functional languages such as Haskel, Scala, functional-Java or Python today ~ an order shorter & faster approx. This may be 1 of 5 better books on f# others may be by Scott Wlaschin,... other are terse but tackled Hilbert curves, hardware BDD topics as Hansen-Rischel, Don Syme,
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