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// build with:  nvcc -O3 -std=c++17 -lcublas -arch=sm_75 100k_main.cu
// note: uses ~3GB VRAM. Took 20 min on 1080
#include <cstdio>
#include <cstdlib>
#include <cublas_v2.h>
#include <cuda_runtime.h>

using ll = long long;
constexpr ll  N    = 100'000LL;  
constexpr int BS   = 16384;       

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<?xml version="1.0" encoding="UTF-8"?>
<learning_hierarchy>
    <metadata>
        <title>Learning Path for Ferrocene Contribution</title>
        <description>Comprehensive learning hierarchy for understanding and contributing to Ferrocene, a safety-critical Rust toolchain</description>
        <target_audience>Beginners in Safety-Critical Systems and Rust</target_audience>
    </metadata>

    <learning_path>
        <phase order="1" name="safety_critical_fundamentals">

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<?xml version="1.0" encoding="UTF-8"?>
<learning_hierarchy>
    <metadata>
        <title>Learning Path for Release-plz Contribution</title>
        <description>Comprehensive learning hierarchy for contributing to Release-plz, an automated Rust package release tool</description>
        <target_audience>Beginners in Release Management and Rust</target_audience>
    </metadata>

    <learning_path>
        <phase order="1" name="version_control_fundamentals">

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<?xml version="1.0" encoding="UTF-8"?>
<learning_hierarchy>
    <metadata>
        <title>Learning Path for Postcard Contribution</title>
        <description>Comprehensive learning hierarchy for contributing to Postcard, a no_std serialization library</description>
        <target_audience>Beginners in Embedded Systems and Rust</target_audience>
    </metadata>

    <learning_path>
        <phase order="1" name="embedded_systems_fundamentals">

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<?xml version="1.0" encoding="UTF-8"?>
<learning_hierarchy>
    <metadata>
        <title>Learning Path for Sysinfo Contribution</title>
        <description>Comprehensive learning hierarchy for contributing to sysinfo, a cross-platform system information library</description>
        <target_audience>Beginners in Systems Programming and Rust</target_audience>
    </metadata>

    <learning_path>
        <phase order="1" name="operating_system_fundamentals">

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<?xml version="1.0" encoding="UTF-8"?>
<learning_hierarchy>
    <metadata>
        <title>Learning Path for Inferno Contribution</title>
        <description>Comprehensive learning hierarchy for contributing to Inferno, a Rust-based flamegraph generation toolkit</description>
        <target_audience>Beginners in Performance Analysis and Rust</target_audience>
    </metadata>

    <learning_path>
        <phase order="1" name="performance_analysis_fundamentals">

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<?xml version="1.0" encoding="UTF-8"?>
<learning_hierarchy>
    <metadata>
        <title>Learning Path for egui Contribution</title>
        <description>Comprehensive learning hierarchy for contributing to egui, starting from computer science fundamentals</description>
        <target_audience>Beginners in Rust and Computer Science</target_audience>
    </metadata>

    <learning_path>
        <phase order="1" name="computer_science_fundamentals">

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<topics>
    <topic name="Performance Analysis">
        <subtopic name="CPU Profiling">
            <tag>Stack Traces</tag>
            <tag>Call Graphs</tag>
            <tag>Sampling Methods</tag>
            <deep>
                <item>CPU Time Measurement</item>
                <item>Context Switching</item>
                <item>Interrupt Handling</item>

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import time
import board
import random
import neopixel
import digitalio
from adafruit_debouncer import Button


pixel_pin = board.D1
num_pixels = 144

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import pandas as pd
import numpy as np


"""
Method generates n random UNIX timestamps
Parameters:
 - start : The start date
 - end   : The end date
 - n     : The number of random timestamps