3.2. Setup and Walk-through¶

3.2.1. Linux vs Bare-Metal¶

Fig. 3.1 An 8-core machine with 2 hyper-threads¶

We will divide the computation into threads that run on different processors. Fig. 3.1 shows a machine on Biglab. Some nodes in Biglab have 4 cores with 2 hyper-threads each and others have 8 cores with 1 thread each. We will be running these threads on the Linux OS and hence, all the heavy lifting of sharing main memory global address is taken care of by the OS.

However, in a bare-metal system:

we would have to map the main memory (DRAM) into the address spaces of each processors. Only then can the processors distribute and coordinate the work (which involves communicating pointers to shared memory areas and synchronization).
in order to share data, the processors in the bare-metal system must agree on the location and organization of the data.
we must make sure that the processors respect each other’s private memory areas. We can do that in a bare-metal system by mapping private code and data of individual processors at different locations.
Sharing DRAM is complicated by the fact that DRAM is cached in L1 and L2 caches. Data that one processor attempts to write to DRAM may not have been written to shared DRAM yet, but instead remain in the private L1 cache of the processor. When another processor reads the same memory location, it may observe an old value. Fortunately, our x86 processors (and the ARM on the Zynq we will later use) have a Snoop Control Unit, which bypasses data directly between processors as needed to maintain a consistent view of the DRAM. Therefore, this is no concern.

Another problem that we face when we communicate via shared memory is that the reading processor should not start reading the memory until the writing processor has completed writing the data. In other words, we need a form of synchronization between the cores. Design of synchronization functions is a rather complex subject, which is dealt with in other courses such as CIS 471 or CIS 505. In this assignment, we will use the APIs of std::thread to accomplish synchronization between cores. We will show you exactly how to use these APIs in the following sections, but if you would like to learn about std::threads, here are some useful links:

If you prefer a book, refer to C++ Concurrency in Action by Anthony D. Williams.

3.2.2. Obtaining and Running the Code¶

In the previous homework, we dealt with a streaming application that compressed only one picture. For this homework, we will use the same application, except that it will take a video stream instead of a single picture.

We will use machines in Biglab/Detkin/Ketterer. Biglab nodes are shared by multiple users—meaning your processes are not the only ones running on a core. Hence, you might not see full performance scaling as you use more cores. Detkin machines should give you dedicated access to the cores.

Login to Biglab and clone the ese532_code repository using the following command:

git clone https://github.com/icgrp/ese532_code.git

If you already have it cloned, pull in the latest changes using:

cd ese532_code/
git pull origin master

The code you will use for homework submission is in the hw3 directory. The directory structure looks like this:

hw3/
    assignment/
        Makefile
        Walkthrough.cpp
        common/
            App.h
            Constants.h
            Stopwatch.h
            Utilities.h
            Utilities.cpp
        baseline/
            App.cpp
            Compress.cpp
            Differentiate.cpp
            Filter.cpp
            Scale.cpp
        coarse_grain/
            ...
        pipeline_2_cores/
            ...
        cdc_parallel/
            ...
    data/
        Input.bin
        Golden.bin

There are four parts to the homework. You can build all of them by executing make all in the hw3/assignment directory. You can build separately by:
- make base and run ./base to run the baseline project.
- make coarse and run ./coarse to run the coarse-grain project.
- make pipeline2 and run ./pipeline2 to run the pipeline project on 2 cores.
- make cdc and run ./cdc to run the data-parallel CDC you will implement on 4 cores.
The data folder contains the input data, Input.bin, which has 100 frames of size $960$ by $540$ pixels, where each pixel is a byte. Golden.bin contains the expected output. base, coarse and pipeline2 uses this file to see if there is a mismatch between your program’s output and the expected output. cdc uses prince.txt from the data folder as an input. golden.txt has the expected output cdc will produce.
The assignment/common folder has header files and helper functions used by the four parts.
You will mostly be working with the code in the rest of the folders.

3.2.3. Working with Threads¶

3.2.3.1. Basics¶

Consider the following code:

#include <iostream>
#include <thread>

void my_function(int a, int b, int&c) {
    c = a + b;
}

int main() {
    int a = 2;
    int b = 3;
    int c;
    my_function(2, 3, c);
    std::cout << "a+b=" << c << std::endl;
}

What thread do you think this code is running on? Let’s find out. Adding a little bit more to the code:

#include <iostream>
#include <thread>

// gets the thread id of the main thread
std::thread::id main_thread_id = std::this_thread::get_id();

// checks if running on main thread using the id
void is_main_thread() {
  if ( main_thread_id == std::this_thread::get_id() )
    std::cout << "This is the main thread." << std::endl;
  else
    std::cout << "This is not the main thread." << std::endl;
}

void my_function(int a, int b, int&c) {
    c = a + b;
}

int main() {
    int a = 2;
    int b = 3;
    int c;
    my_function(2, 3, c);
    std::cout << "a+b=" << c << std::endl;
    is_main_thread();
}

The output is:

a+b=5
This is the main thread.

Can we be really sure? Let’s add a little bit more:

#include <iostream>
#include <thread>

// gets the thread id of the main thread
std::thread::id main_thread_id = std::this_thread::get_id();

// checks if running on main thread using the id
void is_main_thread() {
  if ( main_thread_id == std::this_thread::get_id() )
    std::cout << "This is the main thread." << std::endl;
  else
    std::cout << "This is not the main thread." << std::endl;
}

void my_function(int a, int b, int&c) {
    c = a + b;
}

int main() {
    int a = 2;
    int b = 3;
    int c;
    my_function(a, b, c);
    std::cout << "a+b=" << c << std::endl;
    is_main_thread();

    // create a new thread, note it's not running
    // anything yet.
    std::thread th;

    // construct the thread to run is_main_thread
    // note, as soon as you construct it, the thread
    // starts running.
    // You could create and run at the same time
    // by writing: std::thread th(is_main_thread);
    th = std::thread(is_main_thread);

    // wait for the thread to finish.
    th.join();
}

The output is:

a+b=5
This is the main thread.
This is not the main thread.

From the above, we learned:

#include <thread> to use thread.
don’t construct a thread if you don’t want to run it immediately, i.e. just declare it.
thread starts running as soon as we construct it, i.e. give it a function to run.
th.join() is a blocking call and waits for the thread to finish at the point of the program where it’s called.
we are running on the main thread by default.

We have multiple cores in Biglab. By default, the linux scheduler will schedule our threads into one of these cores. What if we know what we are doing and want full control over assigning a specific thread to run on a specific core? Let’s learn how to do that.

We have given you two functions:

void pin_thread_to_cpu(std::thread &t, int cpu_num);
void pin_main_thread_to_cpu0();

They are declared and defined in common/Utilities.h and common/Utilities.cpp. Adding to our previous example:

#include <iostream>
#include <thread>
#include "Utilities.h"

// gets the thread id of the main thread
std::thread::id main_thread_id = std::this_thread::get_id();

// checks if running on main thread using the id
void is_main_thread() {
  if ( main_thread_id == std::this_thread::get_id() )
    std::cout << "This is the main thread." << std::endl;
  else
    std::cout << "This is not the main thread." << std::endl;
}

void my_function(int a, int b, int&c) {
    c = a + b;
}

int main() {
    // Assign main thread to cpu 0
    pin_main_thread_to_cpu0();

    int a = 2;
    int b = 3;
    int c;
    my_function(a, b, c);
    std::cout << "a+b=" << c << std::endl;
    is_main_thread();

    // create a new thread, note it's not running
    // anything yet.
    std::thread th;

    // construct the thread to run is_main_thread
    // note, as soon as you construct it, the thread
    // starts running.
    // You could create and run at the same time
    // by writing: std::thread th(is_main_thread);
    th = std::thread(is_main_thread);

    // Assign our thread to core 1.
    pin_thread_to_cpu(th, 2);  // threads 0 and 1 on most machines (2 hyperthreads per core) are the same core

    // wait for the thread to finish.
    th.join();
}

Note

The pin_thread_to_cpu APIs we have given you, only works on Linux. For MacOS and Windows, we let the scheduler choose the core. So if you are prototyping on your local machine, keep it in mind.
Also note how in Fig. 3.1 there are 2 hyper-threads per core. The cpu_num argument in pin_thread_to_cpu refers to the index number of the hyper-thread. Hence, for instance, if you want to run a thread on core 0 and one on core 1, you should pin the threads to either 0 and 2, or 1 and 3. This will ensure that each thread is run on a separate core. Otherwise, multiple threads on the same core will share resources and may affect performance. To see the CPU topology in Biglab, use the following commands:
```
export PATH=/home1/e/ese532/software/usr/bin/:$PATH
lstopo
```

Last thing we need to know is how to pass function and their arguments to threads? Modifying our example:

#include <iostream>
#include <thread>
#include "Utilities.h"

// gets the thread id of the main thread
std::thread::id main_thread_id = std::this_thread::get_id();

// checks if running on main thread using the id
void is_main_thread() {
  if ( main_thread_id == std::this_thread::get_id() )
    std::cout << "This is the main thread." << std::endl;
  else
    std::cout << "This is not the main thread." << std::endl;
}

void my_function(int a, int b, int&c) {
    c = a + b;
    std::cout << "From thread id:"
            << std::this_thread::get_id()
            << " a+b=" << c << std::endl;
}

int main() {
    // Assign main thread to cpu 0
    pin_main_thread_to_cpu0();

    int a = 2;
    int b = 3;
    int c;
    my_function(a, b, c);
    is_main_thread();

    // create a new thread, note it's not running
    // anything yet.
    std::thread th;

    // construct the thread to run is_main_thread
    // note, as soon as you construct it, the thread
    // starts running.
    // You could create and run at the same time
    // by writing: std::thread th(is_main_thread);
    th = std::thread(is_main_thread);

    // Assign our thread to core 1.
    pin_thread_to_cpu(th, 2); 

    // wait for the thread to finish.
    th.join();

    std::thread th2(&my_function, a, b, std::ref(c));
    th2.join();
}

The output is:
From thread id:0x114275dc0 a+b=5
This is the main thread.
This is not the main thread.
From thread id:0x700001055000 a+b=5

From the above, we learned:

the first argument to constructing a thread is a callback function. This callback can be a function object (as we see in th), a function pointer (as we see in th2) or a lambda function.
the rest of the arguments are the inputs to the function. They are passed-by-value by default (i.e. a and b are copied). Hence, if you need to pass something by reference (as we see int& c in my_function), you have to wrap it in std::ref.

This concludes everything you need to know about std::threads to complete this homework. You can run the full walk-through by make walkthrough and ./walkthrough.

3.2.3.2. Coarse-grain¶

The coarse-grain part of the homework shows you how you can process a data parallel function with threads. We show how you change the Scale function to process it with two threads:

void Scale_coarse(const unsigned char *Input, unsigned char *Output, int Y_Start_Idx, int Y_End_Idx)
{
  for (int Y = Y_Start_Idx; Y < Y_End_Idx; Y += 2)
  {
    for (int X = 0; X < INPUT_WIDTH_SCALE; X += 2)
    {
      Output[(Y / 2) * INPUT_WIDTH_SCALE / 2 + (X / 2)] = Input[Y * INPUT_WIDTH_SCALE + X];
    }
  }
}

From the code, you can see that we added two additional arguments at the function signature, which is then used in the for loop. This helps us realize the data parallel behavior of the function and let multiple threads work on it:

...
for (int Frame = 0; Frame < FRAMES; Frame++)
  {
    std::vector<std::thread> ths;
    ths.push_back(std::thread(&Scale_coarse, Input_data + Frame * FRAME_SIZE, Temp_data[0], 0, INPUT_HEIGHT_SCALE / 2));
    ths.push_back(std::thread(&Scale_coarse, Input_data + Frame * FRAME_SIZE, Temp_data[0], INPUT_HEIGHT_SCALE / 2, INPUT_HEIGHT_SCALE));

    pin_thread_to_cpu(ths[0], 0);
    pin_thread_to_cpu(ths[1], 2);

    for (auto &th : ths)
    {
      th.join();
    }
    ...

As we can see from the code above, two threads are launched in parallel. One processes indices [0, 270) and the other processes [270, 540). If you wanted to use three threads, you can split the indices as [0,180), [180, 360) and [360, 540) and invoke another thread and pin it to cpu 3.

3.2.3.3. Pipeline¶

The pipeline part of the homework shows you how you can orchestrate the launching of threads and achieve pipeline parallelism. Start reading from the main function, where we launch a process on cpu 0:

for (int Frame = 0; Frame < FRAMES + 2; Frame++)
  {
    core_0_process(std::ref(Size), Frame, Input_data, Temp_data, Output_data);
  }

Following a top-down approach, look into core_0_process function:

void core_0_process(int &Size,
                    int Frame,
                    unsigned char *Input_data,
                    unsigned char **Temp_data,
                    unsigned char *Output_data)
{
  static unsigned char temp_core_0[FRAME_SIZE];
  static unsigned char *Input_data_core_0 = temp_core_0;
  std::thread core_1_thread;
  if (Frame < FRAMES + 1)
  {
    // current core (core 0) spins up process on core 1
    core_1_thread = std::thread(&core_1_process,
                                Frame,
                                Input_data,
                                Temp_data);
    pin_thread_to_cpu(core_1_thread, 2);
  }

  // core 0 does its job
  if (Frame > 1)
  {
    Filter_vertical(Input_data_core_0, Temp_data[2]);
    Differentiate(Temp_data[2], Temp_data[3]);
    Size = Compress(Temp_data[3], Output_data);
  }
  // waits for core 1 to finish
  if (Frame < FRAMES + 1)
  {
    core_1_thread.join();
  }

  unsigned char *Temp = Temp_data[1];
  Temp_data[1] = Input_data_core_0;
  Input_data_core_0 = Temp;
}

Pay special attention to the guards—if (Frame < FRAMES + 1) and if (Frame > 1), and figure out if a code executes or not or is waiting on another core to finish. Keep following the code like this and you will realize how we mapped the functions for the pipelining on 2 cores part of the homework. In summary:

for pipelining on 2 cores, we map Scale and parts of Filter on core 1 and then the rest of Filter, Differentiate and Compress on core 0.
if you wanted to map on 3 cores, you could map Scale on core 2, Filter_horizontal on core 1, and Filter_vertical, Differentiate and Compress on core 0.

You will also realize how the data flows and how the pipeline fills and drains. Lastly, pay special attention to the static in static unsigned char of the processes in the pipeline code. Remember that static keyword in a block scope changes the storage class of a variable, i.e. the lifetime of the variable is until the program stops executing. This is especially important since being able to use old data while new data is being produced is key to achieving the pipeline parallelism.

3.2.3.4. Monitoring Processes using `htop`¶

htop tool in Linux lets you monitor the processes running on your system. Since there can be multiple users in Biglab at a given time, you would want to monitor htop and see if the CPUs are looking idle; in which case you should do some profiling of you program and get some clean results. You can also use htop to see where your threads are pinned to.

Once logged into Biglab, type htop in the terminal, and you’ll see a screen like Fig. 3.2:

Fig. 3.2 Initial htop window¶

This gives you an idea if the CPU are being utilized and if the machine is being used heavily.

Let’s configure some of the view items. Press F2 (Fn+F2 in laptops) to enter the Setup screen. From the setup screen, select Display Options and check Tree view. This will let us see the threads we spawn. Also uncheck Count CPUs from 1 instead of 0, so that we are referring to the same indexing in this walk-through. Now from the same setup screen, select Columns and select PROCESSOR from the Available Columns. This is will let us see the CPU ID of the thread.’ Press Esc to get back to the main htop window.

While keeping htop open in one terminal, open a different terminal to your Biglab node and compile the following code by pasting it your Walkthrough.cpp and do make walkthrough && ./walkthrough.

#include <iostream>
#include <thread>
#include "Utilities.h"
#include <unistd.h>
#include <array>

void my_function(int a, int b) {
    int c = a + b;
    std::cout << "From thread id:"
            << std::this_thread::get_id()
            << " a+b=" << c << std::endl;
    sleep(60);
}

int main() {
    // pin main thread to cpu 0
    pin_main_thread_to_cpu0();

    int a = 2;
    int b = 3;
    

    // create an array of threads
    std::array<std::thread, 4> threads;
    
    // spawn some threads and pin them to specific cpus
    for(int i = 0; i < 4; i++) {
      threads[i] = std::thread(&my_function, a, b);
      pin_thread_to_cpu(threads[i], 2*i+1); 
    }

    // wait for threads to finish
    for(std::thread& th : threads) {
      th.join();
    }
}

The program waits for a minute. During this time, go back to the terminal with htop. Press F4 (Fn+F4 in laptops) and type walkthrough to filter our process. You’ll see the following:

Fig. 3.3 htop showing threads and cpu assignment¶

You can see that we are able to see our threads and how they are mapped to different CPU IDs—1, 3, 5, 7—indicating that we want to use separate cores instead of hyper-threads in the same core.

ESE532 Handouts Fall 2021