You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Measure CPU time spent on each thread separately in C++

I know that this question sounds like an easy question and a duplicate of former ones, in which boost.timer and the chrono facility of C++11 are given as answers.
But, what I have in mind is a bit different and I found no answer to it either on StackOverflow or elsewhere:
In my (C++11) program on Ubuntu Linux, I start several threads with std::async and the std::future mechanism.
Inside every thread I measure CPU-Time with boost.timer(). If I start only one thread I get a CPU time of (in my example) ~0.39 sec and an equal WC time of ~0.39 sec.
If I start several threads I get a longer WC time for each, say 0.8 sec for 16 threads and now the CPU-time for each is about 6.4 sec, that is 8 * 0.8 sec (I have a quad-core Xeon CPU).
So the CPU-Time of each thread is seemingly multiplied by the (number of CPU cores) * 2.
Of course(?) I would like to see a CPU-time near 0.39 sec for each thread, as this is probably still the time the thread uses the CPU for its purposes. The longer CPU time shown (multiplied with the "CPU number factor") is not so much of help in gauging the true CPU consumption of each thread separately.
For illustration I append my test program and its output, first for one thread, then for 16 threads.
So my question is: What can I do, which library, function or programming technique can I use, to get the true CPU usage of each thread which should not change much with the number of threads started?
#include <iostream>
#include <fstream>

#include <vector>
#include <cmath>

#include <future>
#include <mutex>
#include <chrono>

#include <boost/timer/timer.hpp>

std::mutex mtx;

class XTimer
{
public:
    XTimer() {};

    void start();
    void stop();

    double cpu_time();
    double boost_cpu_time();
    double wc_time();

    std::chrono::time_point<std::chrono::system_clock> timestamp_wc;
    std::chrono::time_point<std::chrono::steady_clock> timestamp_cpu;

    boost::timer::cpu_timer timer_cpu;

    double wc_time_val;
    double cpu_time_val;

    double boost_cpu_time_val;

};

void XTimer::start()
{
    timestamp_wc = std::chrono::system_clock::now();
    timestamp_cpu = std::chrono::steady_clock::now();

    timer_cpu.start();

    cpu_time_val = 0;
    wc_time_val = 0;

    boost_cpu_time_val = 0;

}

void XTimer::stop()
{
    const auto ns_wc = std::chrono::system_clock::now() - timestamp_wc;
    const auto ns_cpu = std::chrono::steady_clock::now() - timestamp_cpu;

    auto elapsed_times(timer_cpu.elapsed());

    auto cpu_elapsed(elapsed_times.system + elapsed_times.user);

    //std::cout << "boost: cpu elapsed = " << cpu_elapsed << std::endl;

    wc_time_val = double(ns_wc.count())/1e9;
    cpu_time_val = double(ns_cpu.count())/1e9;

    boost_cpu_time_val = double(cpu_elapsed)/1e9;

}

double XTimer::cpu_time()
{
    return cpu_time_val;
}

double XTimer::boost_cpu_time()
{
    return boost_cpu_time_val;
}

double XTimer::wc_time()
{
    return wc_time_val;
}

template<class T>
int wait_for_all(std::vector<std::future<T>> & fuvec)
{
    std::vector<T> res;

    for(auto & fu: fuvec) {
        res.push_back(fu.get());
    }
    return res.size();

}

int test_thread(int a)
{
    const int N = 10000000;

    double x = 0;

    XTimer tt;

    do {
        std::lock_guard<std::mutex> lck {mtx}; 
        std::cout << "start thread: " << a << std::endl;
    } while (0);

    tt.start(); 

    for(int i = 0; i < N; ++i) {

        if (i % 10000 == 0) {
            //std::cout << (char((int('A') + a)));
        }

        x += sin(i);
    }

    tt.stop();

    do {
        std::lock_guard<std::mutex> lck {mtx};

        std::cout << "end thread: " << a << std::endl;
        std::cout << "boost cpu = " << tt.boost_cpu_time() << " wc = " << tt.wc_time() << std::endl;
    } while (0);

    return 0;
}

int test_threads_start(int num_threads)
{

    std::vector<std::future<int>> fivec;

    XTimer tt;

    tt.start();

    for(int i = 0; i < num_threads; ++i) {
        fivec.push_back(std::async(test_thread, i));
    }

    int sz = wait_for_all(fivec);

    tt.stop();

    std::cout << std::endl << std::endl;

    std::cout << "all threads finished: total wc time = " << tt.wc_time() << std::endl;
    std::cout << "all threads finished: total boost cpu time = " << tt.boost_cpu_time() << std::endl;

}

int main(int argc, char** argv)
{
    const int num_threads_default = 1;
    int num_threads = num_threads_default;

    //boost::timer::auto_cpu_timer ac;

    if (argc > 1) {
        num_threads = atoi(argv[1]);
    }

    std::cout << "starting " << num_threads << " threads." << std::endl;

    test_threads_start(num_threads);

    std::cout << "end." << std::endl;

    return 0;
}

I can be compiled by
g++ -o testit testit.cpp -L/usr/lib/x86_64-linux-gnu -pthread -lboost_timer -lboost_system -lboost_thread 

Sample output with 1 thread
starting 1 threads.
start thread: 0
end thread: 0
boost cpu = 0.37 wc = 0.374107

all threads finished: total wc time = 0.374374
all threads finished: total boost cpu time = 0.37

Sample output with 16 threads
starting 16 threads.
start thread: 0
start thread: 1
start thread: 2
start thread: 3
start thread: 4
start thread: 10
start thread: 5
start thread: 7
start thread: 6
start thread: 11
start thread: 8
start thread: 9
start thread: 13
start thread: 12
start thread: 14
start thread: 15
end thread: 1
boost cpu = 4.67 wc = 0.588818
end thread: 2
boost cpu = 5.29 wc = 0.66638
end thread: 0
boost cpu = 5.72 wc = 0.7206
end thread: 13
boost cpu = 5.82 wc = 0.728717
end thread: 11
boost cpu = 6.18 wc = 0.774979
end thread: 12
boost cpu = 6.17 wc = 0.773298
end thread: 6
boost cpu = 6.32 wc = 0.793143
end thread: 15
boost cpu = 6.12 wc = 0.767049
end thread: 4
boost cpu = 6.7 wc = 0.843377
end thread: 14
boost cpu = 6.74 wc = 0.84842
end thread: 3
boost cpu = 6.91 wc = 0.874065
end thread: 9
boost cpu = 6.83 wc = 0.86342
end thread: 5
boost cpu = 7 wc = 0.896873
end thread: 7
boost cpu = 7.05 wc = 0.917324
end thread: 10
boost cpu = 7.11 wc = 0.930335
end thread: 8
boost cpu = 7.03 wc = 0.940374

all threads finished: total wc time = 0.957748
all threads finished: total boost cpu time = 7.14
end.

A:

Documentation of boost::timer does not mention anything about per thread measurements. Fortunately boost::chrono contains thread_clock which gives per thread CPU usage on platforms which support it. It uses the same interface as the std::chrono clocks and measures thread wall clock.
After adding following lines to your example code:
// Includes section
#include <boost/chrono.hpp>

// XTimer
boost::chrono::thread_clock::time_point timestamp_thread_wc;
double thread_wc_time_val;

// XTimer::start()
timestamp_thread_wc = boost::chrono::thread_clock::now();

// XTimer::stop()
const auto ns_thread_wc = boost::chrono::thread_clock::now() - timestamp_thread_wc;
thread_wc_time_val = double(ns_thread_wc.count())/1e9;

// test_thread() just after for loop
sleep(1);

// test_thread() in bottom do -> while(0) loop
std::cout << "thread cpu = " << tt.thread_wc_time_val << std::endl;

and compiling with additional -lboost_chrono option I get:
starting 1 threads.
start thread: 0
end thread: 0
boost cpu = 0.16 wc = 1.16715
thread cpu = 0.166943

all threads finished: total wc time = 1.16754
all threads finished: total boost cpu time = 0.16
end.

and:
starting 2 threads.
start thread: 0
start thread: 1
end thread: 1
boost cpu = 0.28 wc = 1.14168
thread cpu = 0.141524
end thread: 0
boost cpu = 0.28 wc = 1.14417
thread cpu = 0.14401

all threads finished: total wc time = 1.14442
all threads finished: total boost cpu time = 0.28
end.