Already posted a doubt about the same issue, but i think the answers started to go in other direction, so i will try to focus my questions :P
1) Need to fill a hughe vector with some data. To improve the speed i want to use threads to do the job, so 1 thread can write the first half of the vector and the other thread write the second half.
Since each thread is accesing different positions of the vector... Do i need to protect that acces?
In other words, can i write at the same time in 2 different positions of this structure without protecting it?
...
using namespace std;
...
main{
int n = 256x1024x1024;
vector<int> vec(n);
thread t1(fillFunction(std::ref(vector), 0, n/2);
thread t2(fillFunction(std::ref(vector), n/2, n);
t1.join;
t2.join;
}
fillFunction(vector<int> &vec, int first, int final){
int i;
for (i = first; i < final; i++){
vec[i] = some_data;
}
}
In case i have to protect the access, should i use lock_guard or unique_lock?
2) This thread solution is really going to improve the speed?
I mean, even if i protect the writings, the vector is large enough to not fit on cache. The threads are writing on very different positions, so the 'for' will generate so many cache misses.
Can these "cache misses" result in a slower execution than without threads?
Making 1 thread to fill the even numbers and the other thread the odd numbers can reduce the cache misses?
thread t1(fillFunction(std::ref(vector), 0, n/2);
thread t2(fillFunction(std::ref(vector), 1, n);
[...]
for (i = first; i < final; i = i+2){
vec[i] = some_data;
}
Thank you all :)
1) No you do not need to protect the vector if you are guaranteed to write to different addresses.
2) You will really just have to test these things yourself on your exact machine. Try single thread vs. interleaved access vs. split access and just time the results.
Related
I am using a MultiThreading class which creates the required number of threads in its own threadpool and deletes itself after use.
std::thread *m_pool; //number of threads according to available cores
std::mutex m_locker;
std::condition_variable m_condition;
std::atomic<bool> m_exit;
int m_processors
m_pool = new std::thread[m_processors + 1]
void func()
{
//code
}
for (int i = 0; i < m_processors; i++)
{
m_pool[i] = std::thread(func);
}
void reset(void)
{
{
std::lock_guard<std::mutex> lock(m_locker);
m_exit = true;
}
m_condition.notify_all();
for(int i = 0; i <= m_processors; i++)
m_pool[i].join();
delete[] m_pool;
}
After running through all tasks, the for-loop is supposed to join all running threads before delete[] is being executed.
But there seems to be one last thread still running, while the m_pool does not exist anymore.
This leads to the problem, that I can't close my program anymore.
Is there any way to check if all threads are joined or wait for all threads to be joined before deleting the threadpool?
Simple typo bug I think.
Your loop that has the condition i <= m_processors is a bug and will actually process one extra entry past the end of the array. This is an off-by-one bug. Suppose m_processors is 2. You'll have an array that contains 2 elements with indices [0] and [1]. Yet, you'll be reading past the end of the array, attempting to join with the item at index [2]. m_pool[2] is undefined memory and you're likely going to either crash or block forever there.
You likely intended i < m_processors.
The real source of the problem is addressed by Wick's answer. I will extend it with some tips that also solve your problem while improving other aspects of your code.
If you use C++11 for std::thread, then you shouldn't create your thread handles using operator new[]. There are better ways of doing that with other C++ constructs, which will make everything simpler and exception safe (you don't leak memory if an unexpected exception is thrown).
Store your thread objects in a std::vector. It will manage the memory allocation and deallocation for you (no more new and delete). You can use other more flexible containers such as std::list if you insert/delete threads dynamically.
Fill the vector in place with std::generate or similar
std::vector<std::thread> m_pool;
m_pool.reserve(n_processors);
// Fill the vector
std::generate_n( std::back_inserter(m_pool), m_processors,
[](){ return std::thread(func); } );
Join all the elements using range-for loop and delete handles using container's functions.
for( std::thread& t: m_pool ) {
t.join();
}
m_pool.clear();
I would like to use OpenMP to make my program run faster. Unfortunately, the opposite is the case. My code looks something like this:
const int max_iterations = 10000;
int num_interation = std::numeric_limits<int>::max();
#pragma omp parallel for
for(int i = 0; i < std::min(num_interation, max_iterations); i++)
{
// do sth.
// update the number of required iterations
// num_interation can only become smaller over time
num_interation = update_iterations(...);
}
For some reason, many more iterations are processed than required. Without OpenMP, it takes 500 iterations on avarage. However, even when setting the numbers of threads to one (set_num_threads(1)), it computes more than one thousand iterations. The same happens if I use mutliple threads, and also when using a writelock when updating num_iterations.
I would assume that it has something todo with memory bandwidth or a race condition. But those problems should not appear in case of set_num_threads(1).
Therefore, I assume that it could have something todo with the scheduling and the chunk size. However, I am really not sure about this.
Can somebody give me a hint?
A quick answer for the behaviour you experience is given by the OpenMP standard page 56:
The iteration count for each associated loop is computed before entry
to the outermost loop. If execution of any associated loop changes any
of the values used to compute any of the iteration counts, then the
behavior is unspecified.
In essence, this means that you cannot modify the boundaries of your loop once you entered it. Although according to the standard the behaviour is "unspecified", in your case, what happen is quite clear since as soon as you switch OpenMP on on your code, you compute the number of iterations you had specified initially.
So you have to take another approach to this problem.
This is a possible solution (amongst many other) which I hope scales OK. It has the drawback of potentially allowing more iterations to happen than the number you intended (up to OMP_NUM_THREADS-1 more iterations than expected, assuming that //do sth. is balanced, and many more if not). Also, it assumes that update_iterations(...) is thread safe and can be called in parallel without unwanted side effects... This is a very strong assumption which you'd better enforce!
num_interation = std::min(num_interation, max_iterations);
#pragma omp parallel
{
int i = omp_get_thread_num();
const int nbth = omp_get_num_threads();
while ( i < num_interation ) {
// do sth.
// update the number of required iterations
// num_interation can only become smaller over time
int new_num_interation = update_iterations(...);
#pragma omp critical
num_interation = std::min(num_interation, new_num_interation);
i += nbth;
}
}
A more synchronised solution, if the //do sth. isn't so balanced and not doing too many extra iterations is important, could be:
num_interation = std::min(num_interation, max_iterations);
int nb_it_done = 0;
#pragma omp parallel
{
int i = omp_get_thread_num();
const int nbth = omp_get_num_threads();
while ( nb_it_done < num_interation ) {
// do sth.
// update the number of required iterations
// num_interation can only become smaller over time
int new_num_interation = update_iterations(i);
#pragma omp critical
num_interation = std::min(num_interation, new_num_interation);
i += nbth;
#pragma omp single
nb_it_done += nbth;
}
}
Another weird thing here is that, since you didn't show what i is used for, it isn't clear if iterating somewhat randomly into the domain is a problem. If it isn't, the first solution should work well, even for unbalanced //do sth.. But if it is a problem, then you'd better stick with the second solution (and even potentially reinforce the synchronism).
But at the end of the day, there is now way (that I can think of and with decent parallelism) to avoid potential extra work to be done, since the number of iterations can change along the way.
I try to parse many Google protocol buffer messages from a binary file generated by calling SerializeToString. I first load all Bytes into a heap memory by calling new function. I also have two arrays to store the Bytes begin address of a message in the heap memory and the Bytes count of the message.
Then I begin to parse message by calling ParseFromString.I want to quicken the procedure by using multi-thread.
In each thread, I pass the start index and end index of address array and Byte count array.
In parent process. the main code is:
struct ParsePara
{
char* str_buffer;
size_t* buffer_offset;
size_t* binary_string_length_array;
size_t start_idx;
size_t end_idx;
Flight_Ticket_Info* ticket_info_buffer_array;
};
//Flight_Ticket_Info is class of message
//offset_size is the count of message
ticket_array = new Flight_Ticket_Info[offset_size];
const int max_thread_count = 6;
pthread_t pthread_id_vec[max_thread_count];
CTimer thread_cost;
thread_cost.start();
vector<ParsePara*> para_vec;
const size_t each_count = ceil(float(offset_size) / max_thread_count);
for (size_t k = 0;k < max_thread_count;k++)
{
size_t start_idx = each_count * k;
size_t end_idx = each_count * (k+1);
if (start_idx >= offset_size)
break;
if (end_idx >= offset_size)
end_idx = offset_size;
ParsePara* cand_para_ptr = new ParsePara();
if (!cand_para_ptr)
{
_ERROR_EXIT(0,"[Malloc memory fail.]");
}
cand_para_ptr->str_buffer = m_valdata;//heap memory for storing Bytes of message
cand_para_ptr->buffer_offset = offset_array;//begin address of each message
cand_para_ptr->start_idx = start_idx;
cand_para_ptr->end_idx = end_idx;
cand_para_ptr->ticket_info_buffer_array = ticket_array;//array to store message
cand_para_ptr->binary_string_length_array = binary_length_array;//Bytes count of each message
para_vec.push_back(cand_para_ptr);
}
for(size_t k = 0 ;k < para_vec.size();k++)
{
int ret = pthread_create(&pthread_id_vec[k],NULL,parserFlightTicketForMultiThread,para_vec[k]);
if (0 != ret)
{
_ERROR_EXIT(0,"[Error] [create thread fail]");
}
}
for (size_t k = 0;k < para_vec.size();k++)
{
pthread_join(pthread_id_vec[k],NULL);
}
In each thread the thread function is:
void* parserFlightTicketForMultiThread(void* void_para_ptr)
{
ParsePara* para_ptr = (ParsePara*) void_para_ptr;
parserFlightTicketForMany(para_ptr->str_buffer,para_ptr->ticket_info_buffer_array,para_ptr->buffer_offset,
para_ptr->start_idx,para_ptr->end_idx,para_ptr->binary_string_length_array);
}
void parserFlightTicketForMany(const char* str_buffer,Flight_Ticket_Info* ticket_info_buffer_array,
size_t* buffer_offset,const size_t start_idx,const size_t end_idx,size_t* binary_string_length_array)
{
printf("start_idx:%d,end_idx:%d\n",start_idx,end_idx);
for (size_t k = start_idx;k < end_idx;k++)
{
if (k % 100000 == 0)
cout << k << endl;
size_t cand_offset = buffer_offset[k];
size_t binary_length = binary_string_length_array[k];
ticket_info_buffer_array[k].ParseFromString(string(&str_buffer[cand_offset],binary_length-1));
}
printf("done %ld %ld\n",start_idx,end_idx);
}
But multi-thread cost is more than one thread.
one thread cost is:40455623ms
My computer is 8 core and six thread cost is:131586865ms
Anyone can help me? thank you!
Some possible problems -- you'll have to experiment to determine which:
Protobuf parsing speed is often limited by memory bandwidth rather than CPU time, especially with a large input data set. In that case, more threads won't help, since all the cores are sharing bandwidth to main memory. Indeed, having multiple cores fighting over memory bandwidth could make the overall operation slower. Note that the biggest consumer of memory is not the input bytes but rather the parsed data objects -- that is, the output of parsing -- which are many times larger than the encoded data. To improve this problem, consider writing the parsing loop so that it fully-processes each message immediately after parsing, before moving on to the text message. That way, instead of allocating k protobuf objects, you only need to allocate one protobuf object per thread, and repeatedly reuse the same object for parsing. This way the object will (probably) stay in the core's private L1 cache and avoid consuming memory bandwidth; only the input bytes will be read over the main bus.
How are you loading data into RAM? Did you read() into a large array or did you mmap()? In the latter case the data is read from disk lazily -- it won't happen until you actually attempt to parse it. Even in the read() case, it could be that the data has been swapped out, creating similar effects. Either way, your threads are now not just fighting for memory bandwidth, but disk bandwidth, which is of course much slower. Having six threads reading separate parts of a big file will definitely be slower overall than having one thread read the whole file, because the operating system optimizes for sequential access.
Protobuf allocates memory during parsing. Many memory allocators take a lock while allocating new memory. Since all your threads are allocating tons and tons of objects in a tight loop, they will contend for this lock. Make sure you are using a thread-friendly memory allocator, such as Google's tcmalloc. Note that repeatedly reusing the same protobuf object in a parse-consume loop rather than allocating lots of different objects will also help immensely here, because the protobuf object will automatically reuse memory for sub-objects.
There may be a bug in your code and it might not be doing what you expect at all when multithreaded. For example, a bug might be causing all the threads to process the same data, rather than different data, and it could be that the data they're choosing happens to be bigger. Make sure you are testing that the results of your code are exactly the same when you run single-threaded vs. multi-threaded.
In short, if you want multiple cores to make your code faster, you have to think about not just what each core is doing, but what data is going in and out of each core, and how much the cores have to talk to each other. Ideally you want each core to operate all on its own without talking to anyone or anything; then you get maximum parallelism. That's not usually possible, of course, but the closer you can get to that, the better.
BTW, a random optimization for you:
ParseFromString(string(&str_buffer[cand_offset],binary_length-1))
Replace that with:
ParseFromArray(&str_buffer[cand_offset],binary_length-1)
Creating at std::string makes a copy of the data, which wastes time (and memory bandwidth). (This doesn't explain why threading is slow, though.)
I am working on multithread programming and I am stuck on something.
In my program there are two tasks and two types of robots for carrying out the tasks:
Task 1 requires any two types of robot and
task 2 requires 2 robot1 type and 2 robot2 type.
Total number of robot1 and robot2 and pointers to these two types are given for initialization. Threads share these robots and robots are reserved until a thread is done with them.
Actual task is done in doTask1(robot **) function which takes pointer to a robot pointer as parameter so I need to pass the robots that I reserved. I want to provide concurrency. Obviously if I lock everything it will not be concurrent. robot1 is type of Robot **. Since It is used by all threads before one thread calls doTask or finish it other can overwrite robot1 so it changes things. I know it is because robot1 is shared by all threads. Could you explain how can I solve this problem? I don't want to pass any arguments to thread start routine.
rsc is my struct to hold number of robots and pointers that are given in an initialization function.
void *task1(void *arg)
{
int tid;
tid = *((int *) arg);
cout << "TASK 1 with thread id " << tid << endl;
pthread_mutex_lock (&mutexUpdateRob);
while (rsc->totalResources < 2)
{
pthread_cond_wait(&noResource, &mutexUpdateRob);
}
if (rsc->numOfRobotA > 0 && rsc->numOfRobotB > 0)
{
rsc->numOfRobotA --;
rsc->numOfRobotB--;
robot1[0] = &rsc->robotA[counterA];
robot1[1] = &rsc->robotB[counterB];
counterA ++;
counterB ++;
flag1 = true;
rsc->totalResources -= 2;
}
pthread_mutex_unlock (&mutexUpdateRob);
doTask1(robot1);
pthread_mutex_lock (&mutexUpdateRob);
if(flag1)
{
rsc->numOfRobotA ++;
rsc->numOfRobotB++;
rsc->totalResources += 2;
}
if (totalResource >= 2)
{
pthread_cond_signal(&noResource);
}
pthread_mutex_unlock (&mutexUpdateRob);
pthread_exit(NULL);
}
If robots are global resources, threads should not dispose of them. It should be the duty of the main thread exit (or cleanup) function.
Also, there sould be a way for threads to locate unambiguously the robots, and to lock their use.
The robot1 array seems to store the robots, and it seems to be a global array. However:
its access is not protected by a mutex (pthread_mutex_t), it seems now that you've taken care of that.
Also, the code in task1 is always modifying entries 0 and 1 of this array. If two threads or more execute that code, the entries will be overwritten. I don't think that it is what you want. How will that array be used afterwards?
In fact, why does this array need to be global?
The bottom line is this: as long as this array is shared by threads, they will have problems working concurrently. Think about it this way:
You have two companies using robots to work, but they're using the same truck (robot1) to move the robots around. How are these two companies supposed to function properly, and efficiently with only one truck?
So in my ilumination days, i started to think about how the hell do windows/linux implement the mutex, i've implemented this synchronizer in 100... different ways, in many diferent arquitectures but never think how it is really implemented in big ass OS, for example in the ARM world i made some of my synchronizers disabling the interrupts but i always though that it wasn't a really good way to do it.
I tried to "swim" throgh the linux kernel but just like a though i can't see nothing that satisfies my curiosity. I'm not an expert in threading, but i have solid all the basic and intermediate concepts of it.
So does anyone know how a mutex is implemented?
A quick look at code apparently from one Linux distribution seems to indicate that it is implemented using an interlocked compare and exchange. So, in some sense, the OS isn't really implementing it since the interlocked operation is probably handled at the hardware level.
Edit As Hans points out, the interlocked exchange does the compare and exchange in an atomic manner. Here is documentation for the Windows version. For fun, I just now wrote a small test to show a really simple example of creating a mutex like that. This is a simple acquire and release test.
#include <windows.h>
#include <assert.h>
#include <stdio.h>
struct homebrew {
LONG *mutex;
int *shared;
int mine;
};
#define NUM_THREADS 10
#define NUM_ACQUIRES 100000
DWORD WINAPI SomeThread( LPVOID lpParam )
{
struct homebrew *test = (struct homebrew*)lpParam;
while ( test->mine < NUM_ACQUIRES ) {
// Test and set the mutex. If it currently has value 0, then it
// is free. Setting 1 means it is owned. This interlocked function does
// the test and set as an atomic operation
if ( 0 == InterlockedCompareExchange( test->mutex, 1, 0 )) {
// this tread now owns the mutex. Increment the shared variable
// without an atomic increment (relying on mutex ownership to protect it)
(*test->shared)++;
test->mine++;
// Release the mutex (4 byte aligned assignment is atomic)
*test->mutex = 0;
}
}
return 0;
}
int main( int argc, char* argv[] )
{
LONG mymutex = 0; // zero means
int shared = 0;
HANDLE threads[NUM_THREADS];
struct homebrew test[NUM_THREADS];
int i;
// Initialize each thread's structure. All share the same mutex and a shared
// counter
for ( i = 0; i < NUM_THREADS; i++ ) {
test[i].mine = 0; test[i].shared = &shared; test[i].mutex = &mymutex;
}
// create the threads and then wait for all to finish
for ( i = 0; i < NUM_THREADS; i++ )
threads[i] = CreateThread(NULL, 0, SomeThread, &test[i], 0, NULL);
for ( i = 0; i < NUM_THREADS; i++ )
WaitForSingleObject( threads[i], INFINITE );
// Verify all increments occurred atomically
printf( "shared = %d (%s)\n", shared,
shared == NUM_THREADS * NUM_ACQUIRES ? "correct" : "wrong" );
for ( i = 0; i < NUM_THREADS; i++ ) {
if ( test[i].mine != NUM_ACQUIRES ) {
printf( "Thread %d cheated. Only %d acquires.\n", i, test[i].mine );
}
}
}
If I comment out the call to the InterlockedCompareExchange call and just let all threads run the increments in a free-for-all fashion, then the results do result in failures. Running it 10 times, for example, without the interlocked compare call:
shared = 748694 (wrong)
shared = 811522 (wrong)
shared = 796155 (wrong)
shared = 825947 (wrong)
shared = 1000000 (correct)
shared = 795036 (wrong)
shared = 801810 (wrong)
shared = 790812 (wrong)
shared = 724753 (wrong)
shared = 849444 (wrong)
The curious thing is that one time the results showed now incorrect contention. That might be because there is no "everyone start now" synchronization; maybe all threads started and finished in order in that case. But when I have the InterlockedExchangeCall in place, it runs without failure (or at least it ran 100 times without failure ... that doesn't prove I didn't write a subtle bug into the example).
Here is the discussion from the people who implemented it ... very interesting as it shows the tradeoffs ..
Several posts from Linus T ... of course
In earlier days pre-POSIX etc I used to implement synchronization by using a native mode word (e.g. 16 or 32 bit word) and the Test And Set instruction lurking on every serious processor. This instruction guarantees to test the value of a word and set it in one atomic instruction. This provides the basis for a spinlock and from that a hierarchy of synchronization functions could be built. The simplest is of course just a spinlock which performs a busy wait, not an option for more than transitory sync'ing, then a spinlock which drops the process time slice at each iteration for a lower system impact. Notional concepts like Semaphores, Mutexes, Monitors etc can be built by getting into the kernel scheduling code.
As I recall the prime usage was to implement message queues to permit multiple clients to access a database server. Another was a very early real time car race result and timing system on a quite primitive 16 bit machine and OS.
These days I use Pthreads and Semaphores and Windows Events/Mutexes (mutices?) etc and don't give a thought as to how they work, although I must admit that having been down in the engine room does give one and intuitive feel for better and more efficient multiprocessing.
In windows world.
The mutex before the windows vista mas implemented with a Compare Exchange to change the state of the mutex from Empty to BeingUsed, the other threads that entered the wait on the mutex the CAS will obvious fail and it must be added to the mutex queue for furder notification. Those operations (add/remove/check) of the queue would be protected by an common lock in windows kernel.
After Windows XP, the mutex started to use a spin lock for performance reasons being a self-suficiant object.
In unix world i didn't get much furder but probably is very similar to the windows 7.
Finally for kernels that work on a single processor the best way is to disable the interrupts when entering the critical section and re-enabling then when exiting.