C++ multithreads run time issue - c++

Question

I have been studying C++ multithreads and get a question about it.
Here is what I am understanding about multithreads.
One of the reasons we use multithreads is to reduce the run time, right?
For example, I think if we use two threads we can expect half of the execution time.
So, I tried to code to prove it.
Here is the code.
#include <vector>
#include <iostream>
#include <thread>
#include <future>
using namespace std;
#define iterationNumber 1000000
void myFunction(const int index, const int numberInThread, promise<unsigned long>&& p, const vector<int>& numberList) {
clock_t begin,end;
int firstIndex = index * numberInThread;
int lastIndex = firstIndex + numberInThread;
vector<int>::const_iterator first = numberList.cbegin() + firstIndex;
vector<int>::const_iterator last = numberList.cbegin() + lastIndex;
vector<int> numbers(first,last);
unsigned long result = 0;
begin = clock();
for(int i = 0 ; i < numbers.size(); i++) {
result += numbers.at(i);
}
end = clock();
cout << "thread" << index << " took " << ((float)(end-begin))/CLOCKS_PER_SEC << endl;
p.set_value(result);
}
int main(void)
{
vector<int> numberList;
vector<thread> t;
vector<future<unsigned long>> futures;
vector<unsigned long> result;
const int NumberOfThreads = thread::hardware_concurrency() ?: 2;
int numberInThread = iterationNumber / NumberOfThreads;
clock_t begin,end;
for(int i = 0 ; i < iterationNumber ; i++) {
int randomN = rand() % 10000 + 1;
numberList.push_back(randomN);
}
for(int j = 0 ; j < NumberOfThreads; j++){
promise<unsigned long> promises;
futures.push_back(promises.get_future());
t.push_back(thread(myFunction, j, numberInThread, std::move(promises), numberList));
}
for_each(t.begin(), t.end(), std::mem_fn(&std::thread::join));
for (int i = 0; i < futures.size(); i++) {
result.push_back(futures.at(i).get());
}
unsigned long RRR = 0;
begin = clock();
for(int i = 0 ; i < numberList.size(); i++) {
RRR += numberList.at(i);
}
end = clock();
cout << "not by thread took " << ((float)(end-begin))/CLOCKS_PER_SEC << endl;
}
Because the hardware concurrency of my laptop is 4, it will create 4 threads and each takes a quarter of numberList and sum up the numbers.
However, the result was different than I expected.
thread0 took 0.007232
thread1 took 0.007402
thread2 took 0.010035
thread3 took 0.011759
not by thread took 0.009654
Why? Why it took more time than serial version(not by thread).

Answer 1

For example, I think if we use two threads we can expect half of the
execution time.
You'd think so, but sadly, that is often not the case in practice. The ideal "N cores means 1/Nth the execution time" scenario occurs only when the N cores can execute completely in parallel, without any core's actions interfering with the performance of the other cores.
But what your threads are doing is just summing up different sub-sections of an array... surely that can benefit from being executed in parallel? The answer is that in principle it can, but on a modern CPU, simple addition is so blindingly fast that it isn't really a factor in how long it takes a loop to complete. What really does limit the execute speed of a loop is access to RAM. Compared to the speed of the CPU, RAM access is very slow -- and on most desktop computers, each CPU has only one connection to RAM, regardless of how many cores it has. That means that what you are really measuring in your program is the speed at which a big array of integers can be read in from RAM to the CPU, and that speed is roughly the same -- equal to the CPU's memory-bus bandwidth -- regardless of whether it's one core doing the reading-in of the memory, or four.
To demonstrate how much RAM access is a factor, below is a modified/simplified version of your test program. In this version of the program, I've removed the big vectors, and instead the computation is just a series of calls to the (relatively expensive) sin() function. Note that in this version, the loop is only accessing a few memory locations, rather than thousands, and thus a core that is running the computation loop will not have to periodically wait for more data to be copied in from RAM to its local cache:
#include <vector>
#include <iostream>
#include <thread>
#include <chrono>
#include <math.h>
using namespace std;
static int iterationNumber = 1000000;
unsigned long long threadElapsedTimeMicros[10];
unsigned long threadResults[10];
void myFunction(const int index, const int numberInThread)
{
unsigned long result = 666;
std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
for(int i=0; i<numberInThread; i++) result += 100*sin(result);
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
threadResults[index] = result;
threadElapsedTimeMicros[index] = std::chrono::duration_cast<std::chrono::microseconds>(end - begin).count();
// We'll print out the value of threadElapsedTimeMicros[index] later on,
// after all the threads have been join()'d.
// If we printed it out now it might affect the timing of the other threads
// that may still be executing
}
int main(void)
{
vector<thread> t;
const int NumberOfThreads = thread::hardware_concurrency();
const int numberInThread = iterationNumber / NumberOfThreads;
// Multithreaded approach
std::chrono::steady_clock::time_point allBegin = std::chrono::steady_clock::now();
for(int j = 0 ; j < NumberOfThreads; j++) t.push_back(thread(myFunction, j, numberInThread));
for(int j = 0 ; j < NumberOfThreads; j++) t[j].join();
std::chrono::steady_clock::time_point allEnd = std::chrono::steady_clock::now();
for(int j = 0 ; j < NumberOfThreads; j++) cout << " The computations in thread #" << j << ": result=" << threadResults[j] << ", took " << threadElapsedTimeMicros[j] << " microseconds" << std::endl;
cout << " Total time spent doing multithreaded computations was " << std::chrono::duration_cast<std::chrono::microseconds>(allEnd - allBegin).count() << " microseconds in total" << std::endl;
// And now, the single-threaded approach, for comparison
unsigned long result = 666;
std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
for(int i = 0 ; i < iterationNumber; i++) result += 100*sin(result);
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
cout << "result=" << result << ", single-threaded computation took " << std::chrono::duration_cast<std::chrono::microseconds>(end - begin).count() << " microseconds" << std::endl;
return 0;
}
When I run the above program on my dual-core Mac mini (i7 with hyperthreading), here are the results I get:
Jeremys-Mac-mini:~ lcsuser1$ g++ -std=c++11 -O3 ./temp.cpp
Jeremys-Mac-mini:~ lcsuser1$ ./a.out
The computations in thread #0: result=1062, took 11718 microseconds
The computations in thread #1: result=1062, took 11481 microseconds
The computations in thread #2: result=1062, took 11525 microseconds
The computations in thread #3: result=1062, took 11230 microseconds
Total time spent doing multithreaded computations was 16492 microseconds in total
result=1181, single-threaded computation took 49846 microseconds
So in this case the results are more like what you'd expect -- because memory access was not a bottleneck, each core was able to run at full speed, and complete its 25% portion of the total calculations in about 25% of the time that it took a single thread to complete 100% of the calculations... and since the four cores were running truly in parallel, the total time spent doing the calculations was about 33% of the time it took for the single-threaded routine to complete (ideally it would be 25% but there's some overhead involved in starting up and shutting down the threads, etc).

Answer 2

This is an explanation, for the beginner.
It's not technically accurate, but IMHO not that far from it that anyone takes damage from reading it.
It provides an entry into understanding the parallel processing terms.
Threads, Tasks, and Processes
It is important to know the difference between threads, and processes.
By default starting a new process, allocates a dedicated memory for that process. So they share memory with no other processes, and could (in theory) be run on separate computers.
(You can share memory with other processes, via operating system, or "shared memory", but you have to add these features, they are not by default available for your process)
Having multiple cores means that the each running process can be executed on any idle core.
So basically one program runs on one core, another program runs on a second core, and the background service doing something for you, runs on a third, (and so on and so forth)
Threads is something different.
For instance all processes will run in a main thread.
The operating system implements a scheduler, that is supposed to allocate cpu time for programs. In principle it will say:
Program A, get 0.01 seconds, than pause!
Program B, get 0.01 seconds, then pause!
Program A, get 0.01 seconds, then pause!
Program B, get 0.01 seconds, then pause!
you get the idea..
The scheduler typically can prioritize between threads, so some programs get more CPU time than others.
The scheduler can of course schedule threads on all cores, but if it does this within a process, (splits a process's threads over multiple cores) there can be a performance penalty as each core holds it's own very fast memory cache.
Since threads from the same process can access the same cache, sharing memory between threads is quite fast.
Accessing another cores cache is not as fast, (if even possible without going via RAM), so in general schedulers will not split a process over multiple cores.
The result is that all the threads belonging to a process runs on the same core.
| Core 1 | Core 2 | Core 3 |
| Process A, Thread 1 | Process C, Thread 1 | Process F, Thread 1|
| Process A, Thread 2 | Process D, Thread 1 | Process F, Thread 2|
| Process B, Thread 1 | Process E, Thread 1 | Process F, Thread 3|
| Process A, Thread 1 | Process C, Thread 1 | Process F, Thread 1|
| Process A, Thread 2 | Process D, Thread 1 | Process F, Thread 2|
| Process B, Thread 1 | Process E, Thread 1 | Process F, Thread 3|
A process can spawn multiple threads, they all share the parent threads memory area, and will normally all run on the core that the parent was running on.
It makes sense to spawn threads within a process, if you have an application that needs to respond to something that it cannot control the timing of.
I.E. the users presses on a cancel button, or attempts to move a window, while the application is running calculations that takes a long time to complete.
Responsiveness of the UI, requires the application to spend time reading, and handling what the user is attempting to do. This could be achieved in a main loop, if the program does parts of the calculation in each iteration.
However that get's complicated real fast, so instead of having the calculation code, exit in the middle of a calculation to check the UI, and update the UI, and then continue. You run the calculation code in another thread.
The scheduler then makes sure that the UI thread, and the calculation thread gets CPU time, so the UI responds to user input, while the calculation continues..
And your code stays fairly simple.
But I want to run my calculations another core to gain speed
To distribute calculations on multiple cores, you could spawn a new process for each calculation job. In this way the scheduler will know that each process get's it's own memory, and it can easily be launched on an idle core.
However you have a problem, you need to share memory with the other process, so it knows what to do.
A simple way of doing this, is sharing memory via the filesystem.
You could create a file with the data for the calculation, and then spawn a thread governing the execution (and communication) with another program, (so your UI is responsive, while we wait for the results).
The governing thread runs the other program via system commands, which starts it as another process.
The other program will be written such that it runs with the input file as input argument, so we can run it in multiple instances, on different files.
If the program self terminates when it's done, and creates an output file, it can run on any core, (or multiple) and your process can read the output file.
This actually works, and should the calculation take a long time (like many minutes) this is perhaps ok, even though we use files to communicate between our processes.
For calculations that only takes seconds, however, the file system is slow, and waiting for it will almost remove the gained performance of using processes instead of just using threads. So other more efficient memory sharing is used in real life. For instance creating a shared memory area in RAM.
The "create governing thread, and spawn subprocess, allow communication with process via governing thread, collect data when process is complete, and expose via governing thread" can be implemented in multiple ways.
Tasks
Well "tasks" is ambiguous.
In general it means "Process or thread that solves a task".
However, in certain languages like C#, it is something that implements a thread like thing, that the scheduler can treat as a process. Other languages that provide a similar feature typically dubs this either tasks or workers.
So with workers/tasks it appears to the programmer as if it was merely a thread, that you can share memory with easily, via references, and control like any other thread, by invoking methods on the thread.
But it appears to the scheduler as if it's a process that can be run on any core.
It implements the shared memory problem in a fairly efficient way, as part of the language, so the programmer won't have to re-invent this wheel for all tasks.
This is often referred to as "Hybrid threading" or simply "parallel threads"

Answer 3

Seems that you have some misconception about multi-threading. Simply using two threads cannot halve the processing time.
Multi-threading is a kind of complicated concept but you can easily find related materials on the web. You should read one of them first. But I will try to give a simple explanation with an example.
No matter how many CPUs(or cores) you have, the total handling capacity of the CPU will be always the same whether you use multi-thread or not, right? Then, where does the performance difference come from?
When a program runs on a device(computer) it uses not only CPU but also other system resources such as Networks, RAM, Hard drives, etc. If the flow of the program is serialized there will be a certain point of time when the CPU is idle waiting for other system resources to get done. But, in the case that the program runs with multiple threads(multiple flow), if a thread turns to idle(waiting some tasks done by other system resources) the other threads can use the CPU. Therefore, you can minimize the idle time of the CPU and improve the time performance. This is one of the most simple example about multi-threading.
Since your sample code is almost 'only CPU-consuming', using multi-thread could bring little improvement of performance. Sometimes it can be worse because multi-threading also comes with time cost of context-switching.
FYI, parallel processing is not the same as multi-threading.

Answer 4

This is very good to point out the problems with macs.
Provided you use a o.s. that can schedule threads in a useful manner, you have to consider if a problem is basically the product of 1 problem many times. An example is matrix multiplication. When you multiply 2 matrices there is a certain parts of it which are independent of the others. A 3x3 matrix times another 3x3 requires 9 dot products which can be computed independently of the others, which themselves require 3 multiplications and 2 additions but here the multiplications must be done first. So we see if we wanted to utilize multithreaded processor for this task we could use 9 cores or threads and given they get equal compute time or have same priority level (which is adjustable on windows) you would reduce the time to multiply a 3x3 matrices by 9. This is because we are essentially doing something 9 times which can be done at the same time by 9 people.
now for each of 9 threads we could have 3 cores perform multiplications totaling 3x9=24 cores all together now. Reducing time by t/24. But we have 18 additions and here we can get no gain from more cores. One addition must be piped into another. And the problem takes time t with one core or time t/24 ideally with 24 cores working together. Now you can see why problems are often seeked out if they are 'linear' because they can be done in parallel pretty good like graphics for example (some things like backside culling are sorting problems and inherently not linear so parallel processing has diminished performance boosts).
Then there is added overhead of starting threads and how they are scheduled by the o.s. and processor. Hope this helps.