1. The document describes building a source code level profiler for C++ applications. It outlines 4 milestones: logging execution time, reducing macros, tracking function hit counts, and call path profiling using a radix tree.
2. Key aspects discussed include using timers to log function durations, storing profiling data in a timed entry class, and maintaining a call tree using a radix tree with nodes representing functions and profiling data.
3. The goal is to develop a customizable profiler to identify performance bottlenecks by profiling execution times and call paths at the source code level.
This document discusses different approaches for profiling Java applications without using third-party tools. It begins by explaining the benefits of a do-it-yourself approach such as avoiding reliability and compliance concerns with tools. Various profiling types are then covered, including CPU profiling using wall clock time and calls, sampling, and memory profiling using JVM options. Bytecode manipulation is also presented as a method using ASM to add profiling code without changing sources. The document emphasizes learning the Java Virtual Machine and using its built-in capabilities for profiling purposes.
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
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Caching strategies
Slow database queries
Background processing
Profiling Ruby applications
Picking the right Ruby web server
Sharding data
Attendees will learn how to:
Gain visibility on site performance
Improve scalability and uptime
Find and fix key bottlenecks
See the on-demand replay:
http://pages.engineyard.com/6TipsforImprovingRubyApplicationPerformance.html
Machine learning is teaching the computer how to learn by itself. It is far easier to be done, especially when you have small data set and a good level of expertise in your field. Classifying objects, predicting who will buy, spotting comments in code is achieved with grassy algorithms like neural networks, genetic algorithms or ant herding. PHP is in good position to make use of such teachings, and take advantages of related technologies like fann. By the end of the session, you'll know where you want to try it.
CONFidence 2015: DTrace + OSX = Fun - Andrzej Dyjak PROIDEA
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Machine learning involves teaching a computer how to learn by itself. It is easy to do, especially when you have a small data set and a good level of expertise in your field. Predicting who will buy something or spotting comments in code is achieved with grassy algorithms such as neural networks, genetic algorithms, or ant herding. PHP is in a good position to use such teachings and take advantage of related technologies such as fann. By the end of the session, you'll know how to try it.
Yuta Iwama completed an internship where they added several new features and enhancements to Fluentd. Some of the key additions included implementing a counter API, adding data compression to buffers and forwards, creating a new simpler output plugin for secondary sections, developing a CLI tool to read dumped log data, and optimizing multiple filter calls. The internship provided valuable experience contributing to an open source middleware project and learning about aspects of design, development, and code quality.
This document discusses different approaches for profiling Java applications without using third-party tools. It begins by explaining the benefits of a do-it-yourself approach such as avoiding reliability and compliance concerns with tools. Various profiling types are then covered, including CPU profiling using wall clock time and calls, sampling, and memory profiling using JVM options. Bytecode manipulation is also presented as a method using ASM to add profiling code without changing sources. The document emphasizes learning the Java Virtual Machine and using its built-in capabilities for profiling purposes.
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
This presentation was prepared for a Webcast where John Yerhot, Engine Yard US Support Lead, and Chris Kelly, Technical Evangelist at New Relic discussed how you can scale and improve the performance of your Ruby web apps. They shared detailed guidance on issues like:
Caching strategies
Slow database queries
Background processing
Profiling Ruby applications
Picking the right Ruby web server
Sharding data
Attendees will learn how to:
Gain visibility on site performance
Improve scalability and uptime
Find and fix key bottlenecks
See the on-demand replay:
http://pages.engineyard.com/6TipsforImprovingRubyApplicationPerformance.html
Machine learning is teaching the computer how to learn by itself. It is far easier to be done, especially when you have small data set and a good level of expertise in your field. Classifying objects, predicting who will buy, spotting comments in code is achieved with grassy algorithms like neural networks, genetic algorithms or ant herding. PHP is in good position to make use of such teachings, and take advantages of related technologies like fann. By the end of the session, you'll know where you want to try it.
CONFidence 2015: DTrace + OSX = Fun - Andrzej Dyjak PROIDEA
This document summarizes a presentation about using DTrace on OS X. It introduces DTrace as a dynamic tracing tool for user and kernel space. It discusses the D programming language used for writing DTrace scripts, including data types, variables, operators, and actions. Example one-liners and scripts are provided to demonstrate syscall tracking, memory allocation snooping, and hit tracing. The presentation outlines some past security work using DTrace and similar dynamic tracing tools. It concludes with proposing future work like more kernel and USDT tracing as well as Python bindings for DTrace.
Machine learning involves teaching a computer how to learn by itself. It is easy to do, especially when you have a small data set and a good level of expertise in your field. Predicting who will buy something or spotting comments in code is achieved with grassy algorithms such as neural networks, genetic algorithms, or ant herding. PHP is in a good position to use such teachings and take advantage of related technologies such as fann. By the end of the session, you'll know how to try it.
Yuta Iwama completed an internship where they added several new features and enhancements to Fluentd. Some of the key additions included implementing a counter API, adding data compression to buffers and forwards, creating a new simpler output plugin for secondary sections, developing a CLI tool to read dumped log data, and optimizing multiple filter calls. The internship provided valuable experience contributing to an open source middleware project and learning about aspects of design, development, and code quality.
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Get to know the two stateful programming models of Azure Serverless compute: workflows and actors and how these models can simplify development and how they enable stateful and long-running application patterns within Azure’s compute environments.
Machine learning for PHP
Machine learning is teaching the computer how to learn by itself. It is far easier to be done, especially when you have small data set and a good level of expertise in your field. Classifying objects, predicting who will buy, spotting comments in code is achieved with grassy algorithms like neural networks, genetic algorithms or ant herding. PHP is in good position to make use of such teachings, and take advantages of related technologies like fann. By the end of the session, you’ll know where you want to try it.
PyCon AU 2012 - Debugging Live Python Web ApplicationsGraham Dumpleton
Monitoring tools record the result of what happened to your web application when a problem arises, but for some classes of problems, monitoring systems are only a starting point. Sometimes it is necessary to take more intrusive steps to plan for the unexpected by embedding mechanisms that will allow you to interact with a live deployed web application and extract even more detailed information.
Guider: An Integrated Runtime Performance Analyzer on AGLPeace Lee
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2) It describes the challenges of integrating hardware and validating software early in the design cycle. The methodology used emulation to parallelize hardware and software design activities.
3) Key benefits of the emulation approach included enabling more complex software testing earlier, building confidence in the device design, and having boot and operating system code ready before tape out. This allowed issues to be found and addressed early.
Title: Sista: Improving Cog’s JIT performance
Speaker: Clément Béra
Thu, August 21, 9:45am – 10:30am
Video Part1
https://www.youtube.com/watch?v=X4E_FoLysJg
Video Part2
https://www.youtube.com/watch?v=gZOk3qojoVE
Description
Abstract: Although recent improvements of the Cog VM performance made it one of the fastest available Smalltalk virtual machine, the overhead compared to optimized C code remains important. Efficient industrial object oriented virtual machine, such as Javascript V8's engine for Google Chrome and Oracle Java Hotspot can reach on many benchs the performance of optimized C code thanks to adaptive optimizations performed their JIT compilers. The VM becomes then cleverer, and after executing numerous times the same portion of codes, it stops the code execution, looks at what it is doing and recompiles critical portion of codes in code faster to run based on the current environment and previous executions.
Bio: Clément Béra and Eliot Miranda has been working together on Cog's JIT performance for the last year. Clément Béra is a young engineer and has been working in the Pharo team for the past two years. Eliot Miranda is a Smalltalk VM expert who, among others, has implemented Cog's JIT and the Spur Memory Manager for Cog.
O'Reilly Velocity New York 2016 presentation on modern Linux tracing tools and technology. Highlights the available tracing data sources on Linux (ftrace, perf_events, BPF) and demonstrates some tools that can be used to obtain traces, including DebugFS, the perf front-end, and most importantly, the BCC/BPF tool collection.
This document provides guidance on troubleshooting Citrix issues. It discusses identifying the issue, common StoreFront errors, and techniques for tracing including using PowerShell commands, modifying configuration files, and analyzing log files. Troubleshooting tools mentioned include Fiddler, Service Configuration Editor, Service Trace Viewer, and ILSpy decompiler. Specific errors addressed include unhandled exceptions when browsing applications and folder redirection issues.
The document discusses proposals for implementing profiling middleware and tools for Swift. It outlines a profiling architecture that would inject profiling at the system, process, code and line level granularity. Profiling data would be collected and stored in a multi-dimensional model covering dimensions like time, workload, system and code metrics. Open source profiling tools could then be used to analyze and visualize the data to identify performance bottlenecks.
Profilers find performance bottlenecks in your app but provide confusing information. Let's give you insights into how your profiler and your app are really interacting. What profiling APIs are available, how they work, and what their implementation on the JVM (OpenJDK) side looks like:
Stack sampling profilers: stop motion view of your app
GetCallTrace(JVisualVM case study): The official stack sampling API
Safepoints and safepoint sampling bias
AsyncGetCallTrace(Honest Profiler Case Study): The unofficial API
JVM Profilers vs System Profilers: No API needed?
This document summarizes an advanced Python programming course, covering topics like performance tuning, garbage collection, and extending Python. It discusses profiling Python code to find bottlenecks, using more efficient algorithms and data structures, optimizing code through techniques like reducing temporary objects and inline functions, leveraging faster tools like NumPy, writing extension modules in C, and parallelizing computation across CPUs and clusters. It also explains basic garbage collection algorithms like reference counting and mark-and-sweep used in CPython.
Using Jenkins in nower days you have to learn all about using Pipelines. This presentation shows how to user Jenkins Pipelines inside Oracle Projects.
The Presentation was held on the DOAG Conference 2019 in nuremberg.
Monitoring as Code: Getting to Monitoring-Driven Development - DEV314 - re:In...Amazon Web Services
“Infrastructure as Code” has changed not only how we think about configuring infrastructure, but about the infrastructure itself. AWS has been at the core of this movement, enabling your infrastructure teams to benefit from software engineering best practices such as CI/CD, automated testing, and repeatable deployments. Now that you have mastered the art of managing your infrastructure as code, it’s time to leverage these same lessons for monitoring and metrics. In this session, we dive into how you can leverage tooling such as AWS, Terraform, and Datadog to programmatically define your monitoring so that you that you can scale your organizational observability along with your infrastructure, and attain consistency from local development all the way through production.
Session sponsored by Datadog, Inc.
Here are some ways to optimize the code:
1. Use strtr() instead of preg_replace() since it avoids the overhead of regular expressions.
2. Define the replacement array outside the loop to avoid redefining it on each iteration.
3. Use direct string concatenation instead of sprintf() for better performance.
4. Avoid function calls inside the loop like sizeof(). Define the length before the loop for better performance.
5. Consider using string replacement/manipulation functions like str_replace() instead of redefining/reconcatenating strings on each loop iteration.
So in summary, the optimized code would be:
$rep = ['-' => '*', '.' => '*
This document discusses application profiling and analysis techniques. It provides an overview of profiling, which records summary information during program execution to expose performance bottlenecks. Profiling can be done through sampling or instrumentation. It also discusses tracing, which records time-stamped events during execution. The document compares profiling and tracing and describes tools like PAPI and HPCToolkit that can be used for profiling applications.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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Machine learning is teaching the computer how to learn by itself. It is far easier to be done, especially when you have small data set and a good level of expertise in your field. Classifying objects, predicting who will buy, spotting comments in code is achieved with grassy algorithms like neural networks, genetic algorithms or ant herding. PHP is in good position to make use of such teachings, and take advantages of related technologies like fann. By the end of the session, you’ll know where you want to try it.
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Monitoring tools record the result of what happened to your web application when a problem arises, but for some classes of problems, monitoring systems are only a starting point. Sometimes it is necessary to take more intrusive steps to plan for the unexpected by embedding mechanisms that will allow you to interact with a live deployed web application and extract even more detailed information.
Guider: An Integrated Runtime Performance Analyzer on AGLPeace Lee
Guider is an integrated runtime performance analyzer for Linux that collects system resource and task data in real-time. It traces numerous system operations and visualizes complex performance data. Guider provides highly readable reports and debugging features to help optimize performance. It is open source, system-wide, easy to use, accurate, and light on system resources. Guider can monitor and collect system stats, trace threads, functions, and system calls, and control tasks for testing. Future work includes real-time user-level function tracing and a GUI client for remote control and visualization.
Early Software Development through Palladium EmulationRaghav Nayak
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2) It describes the challenges of integrating hardware and validating software early in the design cycle. The methodology used emulation to parallelize hardware and software design activities.
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Title: Sista: Improving Cog’s JIT performance
Speaker: Clément Béra
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Video Part1
https://www.youtube.com/watch?v=X4E_FoLysJg
Video Part2
https://www.youtube.com/watch?v=gZOk3qojoVE
Description
Abstract: Although recent improvements of the Cog VM performance made it one of the fastest available Smalltalk virtual machine, the overhead compared to optimized C code remains important. Efficient industrial object oriented virtual machine, such as Javascript V8's engine for Google Chrome and Oracle Java Hotspot can reach on many benchs the performance of optimized C code thanks to adaptive optimizations performed their JIT compilers. The VM becomes then cleverer, and after executing numerous times the same portion of codes, it stops the code execution, looks at what it is doing and recompiles critical portion of codes in code faster to run based on the current environment and previous executions.
Bio: Clément Béra and Eliot Miranda has been working together on Cog's JIT performance for the last year. Clément Béra is a young engineer and has been working in the Pharo team for the past two years. Eliot Miranda is a Smalltalk VM expert who, among others, has implemented Cog's JIT and the Spur Memory Manager for Cog.
O'Reilly Velocity New York 2016 presentation on modern Linux tracing tools and technology. Highlights the available tracing data sources on Linux (ftrace, perf_events, BPF) and demonstrates some tools that can be used to obtain traces, including DebugFS, the perf front-end, and most importantly, the BCC/BPF tool collection.
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The document discusses proposals for implementing profiling middleware and tools for Swift. It outlines a profiling architecture that would inject profiling at the system, process, code and line level granularity. Profiling data would be collected and stored in a multi-dimensional model covering dimensions like time, workload, system and code metrics. Open source profiling tools could then be used to analyze and visualize the data to identify performance bottlenecks.
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Stack sampling profilers: stop motion view of your app
GetCallTrace(JVisualVM case study): The official stack sampling API
Safepoints and safepoint sampling bias
AsyncGetCallTrace(Honest Profiler Case Study): The unofficial API
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“Infrastructure as Code” has changed not only how we think about configuring infrastructure, but about the infrastructure itself. AWS has been at the core of this movement, enabling your infrastructure teams to benefit from software engineering best practices such as CI/CD, automated testing, and repeatable deployments. Now that you have mastered the art of managing your infrastructure as code, it’s time to leverage these same lessons for monitoring and metrics. In this session, we dive into how you can leverage tooling such as AWS, Terraform, and Datadog to programmatically define your monitoring so that you that you can scale your organizational observability along with your infrastructure, and attain consistency from local development all the way through production.
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Here are some ways to optimize the code:
1. Use strtr() instead of preg_replace() since it avoids the overhead of regular expressions.
2. Define the replacement array outside the loop to avoid redefining it on each iteration.
3. Use direct string concatenation instead of sprintf() for better performance.
4. Avoid function calls inside the loop like sizeof(). Define the length before the loop for better performance.
5. Consider using string replacement/manipulation functions like str_replace() instead of redefining/reconcatenating strings on each loop iteration.
So in summary, the optimized code would be:
$rep = ['-' => '*', '.' => '*
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
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3. When my code is running slowly
Check Resource usage
• I/O
• Memory
• CPU usage
3
4. When my code is running slowly
Check Resource usage
• I/O
• Memory
• CPU usage
4
5. When my code is running slowly
Check Resource usage
• I/O
• Memory
• CPU usage
Identify the bottleneck
5
• Nested loops
• Excessive function calls
• Inefficient algorithm
• Improper data structure
6. When my code is running slowly
Check Resource usage
• I/O
• Memory
• CPU usage
Identify the bottleneck
6
Optimize the code
• Parallelization
• Memory Optimization
• Algorithm time complexity
• Nested loops
• Excessive function calls
• Inefficient algorithm
• Improper data structure
7. When my code is running slowly
Check Resource usage
• I/O
• Memory
• CPU usage
Identify the bottleneck
7
Optimize the code
• Parallelization
• Memory Optimization
• Algorithm time complexity
• Nested loops
• Excessive function calls
• Inefficient algorithm
• Improper data structure
But how to find the bottleneck?
8. Which part of my code runs slowly?
8
#include <iostream>
#include <ctime>
int main() {
// Record the start time
clock_t start = clock();
do_something();
// Record the stop time
clock_t stop = clock();
// Calculate the elapsed time
double elapsed_time = static_cast<double>(stop - start) /
CLOCKS_PER_SEC;
// Output the time taken
std::cout << "Time taken by do_something: " << elapsed_time << "
seconds" << std::endl;
return 0;
}
Measure each function respectively?
12. Sampling profiling
• Attach to program, periodically interrupt and record the on-CPU function
12
Time
Function c
Function d
13. Sampling profiling
• Attach to program, periodically interrupt and record the on-CPU function
13
Time
Function c x6
Function d
14. Sampling profiling
• Attach to program, periodically interrupt and record the on-CPU function
14
Time
Function c x6
Function d x3
15. Sampling profiling
• Attach to program, periodically interrupt and record the on-CPU function
15
Time
Function c x6
Function d x3 Focus on optimizing function c?
16. Sampling profiling
• Attach to program, periodically interrupt and record the on-CPU function
16
• For each sample, record stack trace
Time
Function c
Function d
17. Sampling profiling
• Attach to program, periodically interrupt and record the on-CPU function
17
• For each sample, record stack trace
Time
Function c
Function d
main
a
b
c
main
a
b
c
d
18. Instrumentation profiling
• Insert code to the program to record performance metric
• Manually inserted by programmers
• Automatically inserted via some tools
18
19. Sampling VS Instrumentation
Sampling
• Non-Intrusive
• Low Overhead
Instrumentation
• Inline functions are invisible
• only approximations and not accurate
19
Pros Cons
• Inline function visible
• More accurate
• More customizable
• Significant overhead
• Require source code / binary rewriting
22. 22
Milestone 1: Log execution time
#include <iostream>
#include <chrono>
#define START_TIMER auto start_time = std::chrono::high_resolution_clock::now();
#define STOP_TIMER(functionName)
do {
auto end_time = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
std::cout << functionName << " took " << duration.count() << " microseconds.n";
} while (false);
• Define macros
• START_TIMER: get current time
• STOP_TIMER: calculate elapsed time
• Insert macro at function entry and exit
23. 23
Milestone 1 : Log execution time
void function1() {
START_TIMER;
for (int i = 0; i < 1000000; ++i) {}
STOP_TIMER("function1");
}
void function2() {
START_TIMER;
for (int i = 0; i < 500000; ++i) {}
STOP_TIMER("function2");
}
int main() {
function1();
function2();
return 0;
}
❯ ./a.out
function1 took 607 microseconds.
function2 took 291 microseconds.
24. 24
Milestone 2: Insert less macros
class ExecutionTimer {
public:
ExecutionTimer(const char* functionName) : functionName(functionName) {
start = std::chrono::high_resolution_clock::now();
}
~ExecutionTimer() {
auto end = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::microseconds>(end - m_start);
std::cout << m_name << " took " << duration.count() << " microseconds.n";
}
private:
const char* m_name;
std::chrono::high_resolution_clock::time_point m_start;
};
• Make use of constructor and destructor
• Constructor: get current time
• Destructor: calculate duration
25. 25
Milestone 2: Insert less macros
void function1() {
ExecutionTimer timer("function1");
for (int i = 0; i < 1000000; ++i) {}
}
void function2() {
ExecutionTimer timer("function2");
for (int i = 0; i < 500000; ++i) {}
}
int main() {
function1();
function2();
return 0;
}
❯ ./a.out
function1 took 607 microseconds.
function2 took 291 microseconds.
26. 26
Milestone 3: hit count of each function
class TimedEntry
{
public:
size_t count() const { return m_count; }
double time() const { return m_time; }
TimedEntry & add_time(double time)
{
++m_count;
m_time += time;
return *this;
}
private:
size_t m_count = 0;
double m_time = 0.0;
};
Create another class to hold each function’s
• execution time
• hit count
27. 27
Milestone 3: hit count of each function
class TimedEntry
{
public:
size_t count() const { return m_count; }
double time() const { return m_time; }
TimedEntry & add_time(double time)
{
++m_count;
m_time += time;
return *this;
}
private:
size_t m_count = 0;
double m_time = 0.0;
};
Create another class to hold each function’s
• execution time
• hit count
std::map<std::string, TimedEntry> m_map; Use a dictionary to hold the record
28. 28
Milestone 3: hit count of each function
void function1() {
ExecutionTimer timer =
Profiler::getInstance().startTimer("function1");
for (int i = 0; i < 1000000; ++i) {}
}
void function2() {
ExecutionTimer timer =
Profiler::getInstance().startTimer("function2");
for (int i = 0; i < 500000; ++i) {}
}
int main() {
function1();
function2();
function2();
return 0;
}
❯ ./a.out
Profiler started.
Function1, hit = 1, time = 320 microseconds.
Function2, hit = 2, time = 314 microseconds.
29. 29
Milestone 4: Call Path Profiling
• A function may have different caller
• Knowing which call path is frequently executed is important
• But how to maintain call tree during profiling?
a -> b -> c -> d -> e
a -> e
30. 30
Milestone 4: Call Path Profiling – Radix Tree
Radix Tree
• Each node acts like a function
• The child node acts like a callee
• The profiling data could be stored within the node
https://static.lwn.net/images/ns/kernel/radix-tree-2.png
31. 31
Milestone 4: Call Path Profiling - Radix Tree
Function calls
1 main
2 main -> a
3 main -> a -> b
4 main -> a -> b -> c
5 main -> a -> b
6 main -> a
7 main -> a -> c
main
a
b
c
c
• Dynamically grow the tree when profiling
32. 32
Milestone 4: Call Path Profiling - RadixTreeNode
template <typename T>
class RadixTreeNode
{
public:
using child_list_type =
std::list<std::unique_ptr<RadixTreeNode<T>>>;
using key_type = int32_t;
RadixTreeNode(std::string const & name, key_type key)
: m_name(name)
, m_key(key)
, m_prev(nullptr)
{
}
private:
key_type m_key = -1;
std::string m_name;
T m_data;
child_list_type m_children;
RadixTreeNode<T> * m_prev = nullptr;
}
• A node has
• a function name
• Profiling data
• Execution time
• Hit count
• a list of children (callee)
• a pointer point back to parent (caller)
33. 33
template <typename T>
class RadixTree
{
public:
using key_type = typename RadixTreeNode<T>::key_type;
RadixTree()
: m_root(std::make_unique<RadixTreeNode<T>>())
, m_current_node(m_root.get())
{
}
private:
key_type get_id(const std::string & name)
{
auto [it, inserted] = m_id_map.try_emplace(name,
m_unique_id++);
return it->second;
}
std::unique_ptr<RadixTreeNode<T>> m_root;
RadixTreeNode<T> * m_current_node;
std::unordered_map<std::string, key_type> m_id_map;
key_type m_unique_id = 0;
};
A tree has
• a root pointer
• a current pointer (on CPU function)
Milestone 4: Call Path Profiling - RadixTree
34. 34
T & entry(const std::string & name)
{
key_type id = get_id(name);
RadixTreeNode<T> * child = m_current_node-
>get_child(id);
if (!child)
{
m_current_node = m_current_node->add_child(name,
id);
}
else
{
m_current_node = child;
}
return m_current_node->data();
}
Milestone 4: Call Path Profiling - RadixTree
When entering a function
• Map the function name to ID
• For faster int comparison
• Check if the current node has such child
• Create a child if not exists
• Increment the hit count
• Change the current pointer
36. 36
void add_time(double time)
{
m_tree.get_current_node()->data().add_time(time);
m_tree.move_current_to_parent();
}
Milestone 4: Call Path Profiling - RadixTree
Function calls
1 main
2 main -> a
3 main -> a -> b
4 main -> a -> b -> c
5 main -> a -> b
6 main -> a -> c
main()
a() : hit = 1, time = 680 microseconds
b() : hit = 1, time = 470 microseconds
c() : hit = 1, time = 120 microseconds
c() : hit = 1, time = 124 microseconds
When leaving a function
• Update the execution time
• Change current pointer to caller
37. SUMMARY
1. Sampling based profiler can quickly deliver performance metric
2. Intrusive based profiler can capture the program’s detailed behavior
3. Developing our own source code level profiler enables us to customize the
performance Metric in the future.
4. It’s more fun to craft the profiler rather than using the existing tool
37