Windows7 memory management - how to prevent concurrent threads from blocking - c++

I'm working on a program consisting of two concurrent threads. One (here "Clock") is performing some computation on a regular basis (10 Hz) and is quite memory-intensive. The other one (here "hugeList") uses even more RAM but is not as time critical as the first one. So I decided to reduce its priority to THREAD_PRIORITY_LOWEST. Yet, when the thread frees most of the memory it has used the critical one doesn't manage to keep its timing.
I was able to condense down the problem to this bit of code (make sure optimizations are turned off!):
while Clock tries to keep a 10Hz-timing the hugeList-thread allocates and frees more and more memory not organized in any sort of chunks.
#include "stdafx.h"
#include <stdio.h>
#include <forward_list>
#include <time.h>
#include <windows.h>
#include <vector>
void wait_ms(double _ms)
{
clock_t endwait;
endwait = clock () + _ms * CLOCKS_PER_SEC/1000;
while (clock () < endwait) {} // active wait
}
void hugeList(void)
{
SetThreadPriority(GetCurrentThread(), THREAD_PRIORITY_LOWEST);
unsigned int loglimit = 3;
unsigned int limit = 1000;
while(true)
{
for(signed int cnt=loglimit; cnt>0; cnt--)
{
printf(" Countdown %d...\n", cnt);
wait_ms(1000.0);
}
printf(" Filling list...\n");
std::forward_list<double> list;
for(unsigned int cnt=0; cnt<limit; cnt++)
list.push_front(42.0);
loglimit++;
limit *= 10;
printf(" Clearing list...\n");
while(!list.empty())
list.pop_front();
}
}
void Clock()
{
clock_t start = clock()-CLOCKS_PER_SEC*100/1000;
while(true)
{
std::vector<double> dummyData(100000, 42.0); // just get some memory
printf("delta: %d ms\n", (clock()-start)*1000/CLOCKS_PER_SEC);
start = clock();
wait_ms(100.0);
}
}
int main()
{
DWORD dwThreadId;
if (CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)&Clock, (LPVOID) NULL, 0, &dwThreadId) == NULL)
printf("Thread could not be created");
if (CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)&hugeList, (LPVOID) NULL, 0, &dwThreadId) == NULL)
printf("Thread could not be created");
while(true) {;}
return 0;
}
First of all I noticed that allocating memory for the linked list is way faster than freeing it.
On my machine (Windows7) at around the 4th iteration of the "hugeList"-method the Clock-Thread gets significantly disturbed (up to 200ms). The effect disappears without the dummyData-vector "asking" for some memory in the Clock-Thread.
So,
Is there any way of increasing the priority of memory allocation for the Clock-Thread in Win7?
Or do I have to split both operations onto two contexts (processes)?
Note that my original code uses some communication via shared variables which would require for some kind of IPC if I chose the second option.
Note that my original code gets stuck for about 1sec when the equivalent to the "hugeList"-method clears a boost::unordered_map and enters ntdll.dll!RtIInitializeCriticalSection many many times.
(observed by systinernals process explorer)
Note that the effects observed are not due to swapping, I'm using 1.4GB of my 16GB (64bit win7).
edit:
just wanted to let you know that up to now I haven't been able to solve my issue. Splitting both parts of the code onto two processes does not seem to be an option since my time is rather limited and I've never worked with processes so far. I'm afraid I won't be able to get to a running version in time.
However, I managed to reduce the effects by reducing the number of memory deallocations made by the non-critical thread. This was achieved by using a fast pooling memory allocator (like the one provided in the boost library).
There does not seem to be the possibility of explicitly creating certain objects (like e.g. the huge forward list in my example) on some sort of threadprivate heap that would not require synchronisation.
For further reading:
http://bmagic.sourceforge.net/memalloc.html
Do threads have a distinct heap?
Memory Allocation/Deallocation Bottleneck?
http://software.intel.com/en-us/articles/avoiding-heap-contention-among-threads
http://www.boost.org/doc/libs/1_55_0/libs/pool/doc/html/boost_pool/pool/introduction.html

Replacing std::forward_list with a std::list, I ran your code on a corei7 4GB machine until 2GB is consumed. No disturbances at all. (In debug build)
P.S
Yes. The release build recreates the issue. I replaced the forward list with an array
double* p = new double[limit];
for(unsigned int cnt=0; cnt<limit; cnt++)
p[cnt] = 42.0;
and
for(unsigned int cnt=0; cnt<limit; cnt++)
p[cnt] = -1;
delete [] p;
It does not recreates then.
It seems thread scheduler is punishing for asking for lot of small memory chunks.

Related

32-bit malloc() return NULL when opening many threads?

I have a sample C++ program as below:
#include <windows.h>
#include <stdio.h>
int main(int argc, char* argv[])
{
void * pointerArr[20000];
int i = 0, j;
for (i = 0; i < 20000; i++) {
void * pointer = malloc(131125);
if (pointer == NULL) {
printf("i = %d, out of memory!\n", i);
getchar();
break;
}
pointerArr[i] = pointer;
}
for (j = 0; j < i; j++) {
free(pointerArr[j]);
}
getchar();
return 0;
}
When I run it with Visual Studio 32-bit Debug, it will run with following result:
The program can use nearly 2Gb of memory before out of memory.
This is normal behavior.
However, when I adding the code to start Thread inside the for loop as below:
#include <windows.h>
#include <stdio.h>
DWORD WINAPI thread_func(VOID* pInArgs)
{
Sleep(100000);
return 0;
}
int main(int argc, char* argv[])
{
void * pointerArr[20000];
int i = 0, j;
for (i = 0; i < 20000; i++) {
CreateThread(NULL, 0, thread_func, NULL, 0, NULL);
void * pointer = malloc(131125);
if (pointer == NULL) {
printf("i = %d, out of memory!\n", i);
getchar();
break;
}
pointerArr[i] = pointer;
}
for (j = 0; j < i; j++) {
free(pointerArr[j]);
}
getchar();
return 0;
}
The result is as below:
The memory is still just around 200Mb but function malloc will return NULL.
Could anyone help explain why the program cannot use the memory up to 2Gb before out of memory?
Is it mean creating many threads like above will cause memory leak?
In my real application, this error occur when I create about 800 threads, the RAM memory at the time "out of memory" is around 300Mb.
As noted in a comment by #macroland, the main thing happening here is that each thread is consuming 1 MiB for its stack (see MSDN CreateThread and Thread Stack Size). You say malloc returns NULL once the total you have directly allocated reaches 200 MB. Since you are allocating 131125 bytes at a time, that is 200 MB / 131125 B = 1525 threads. Their cumulative stack space will be around 1.5 GB. Adding the 200 MB of malloc memory is 1.7 GB, and miscellaneous overhead likely accounts for the rest.
So, why does Task Manager not show this? Because the full 1 MiB of thread stack space is not actually allocated (also called committed), rather it is reserved. See VirtualAlloc and the MEM_RESERVE flag. The address space has been reserved for expansion up to 1 MiB, but initially only 64 KiB are allocated, and Task Manager only counts the latter. But reserved memory will not be unilaterally repurposed by malloc until the reservation is lifted, so once it runs out of available address space, it has to return NULL.
What tool can show this? I don't know of anything off the shelf (even Process Explorer does not seem show a count of reserved memory). What I have done in the past is write my own little routine that uses VirtualQuery to enumerate the entire address space, including reserved ranges. I recommend you do the same; it's not much code to write, and very handy when coding for 32-bit Windows because the 2 GiB address space gets cramped very easily (DLLs are an obvious reason, but the default malloc also will leave unexpected reservations behind in response to certain allocation patterns even if you free everything).
In any case, if you want to create thousands of threads in a 32-bit Windows process, be sure to pass a non-zero value as the dwStackSize parameter to CreateThread, and also pass STACK_SIZE_PARAM_IS_A_RESERVATION as dwCreationFlags. The minimum is 64 KiB, which will be plenty if you avoid recursive algorithms in the threads.
Addendum: In a comment, #iinspectable cautions against using thousands of threads, citing Raymond Chen's 2005 blog post Does Windows have a limit of 2000 threads per process?. I agree that doing so is questionable for a variety of reasons; it is not my intent to endorse the practice, rather I'm just explaining one necessary element.

Kernel copying CoW pages after child process exit

In Linux, whenever a process is forked, the memory mappings of the parent process are cloned into the child process. In reality, for performance reasons, the pages are set to be copy-on-write -- initially they are shared and, in the event one of the two processes writing on one of them, they will then be cloned (MAP_PRIVATE).
This is a very common mechanism of getting a snapshot of the state of a running program -- you do a fork, and this gives you a (consistent) view of the memory of the process at that point in time.
I did a simple benchmark where I have two components:
A parent process that has a pool of threads writing into an array
A child process that has a pool of threads making a snapshot of the array and unmapping it
Under some circumstances (machine/architecture/memory placement/number of threads/...) I am able to make the copy finish much earlier than the threads write into the array.
However, when the child process exits, in htop I still see most of the CPU time being spent in the kernel, which is consistent to it being used to handle the copy-on-write whenever the parent process writes to a page.
In my understanding, if an anonymous page marked as copy-on-write is mapped by a single process, it should not be copied and instead should be used directly.
How can I be sure that this is indeed time being spent copying the memory?
In case I'm right, how can I avoid this overhead?
The core of the benchmark is below, in modern C++.
Define WITH_FORK to enable the snapshot; leave undefined to disable the child process.
#include <unistd.h>
#include <sys/mman.h>
#include <sys/types.h>
#include <sys/wait.h>
#include <numaif.h>
#include <numa.h>
#include <algorithm>
#include <cassert>
#include <condition_variable>
#include <mutex>
#include <iomanip>
#include <iostream>
#include <cmath>
#include <numeric>
#include <thread>
#include <vector>
#define ARRAY_SIZE 1073741824 // 1GB
#define NUM_WORKERS 28
#define NUM_CHECKPOINTERS 4
#define BATCH_SIZE 2097152 // 2MB
using inttype = uint64_t;
using timepoint = std::chrono::time_point<std::chrono::high_resolution_clock>;
constexpr uint64_t NUM_ELEMS() {
return ARRAY_SIZE / sizeof(inttype);
}
int main() {
// allocate array
std::array<inttype, NUM_ELEMS()> *arrayptr = new std::array<inttype, NUM_ELEMS()>();
std::array<inttype, NUM_ELEMS()> & array = *arrayptr;
// allocate checkpoint space
std::array<inttype, NUM_ELEMS()> *cpptr = new std::array<inttype, NUM_ELEMS()>();
std::array<inttype, NUM_ELEMS()> & cp = *cpptr;
// initialize array
std::fill(array.begin(), array.end(), 123);
#ifdef WITH_FORK
// spawn checkpointer threads
int pid = fork();
if (pid == -1) {
perror("fork");
exit(-1);
}
// child process -- do checkpoint
if (pid == 0) {
std::array<std::thread, NUM_CHECKPOINTERS> cpthreads;
for (size_t tid = 0; tid < NUM_CHECKPOINTERS; tid++) {
cpthreads[tid] = std::thread([&, tid] {
// copy array
const size_t numBatches = ARRAY_SIZE / BATCH_SIZE;
for (size_t i = tid; i < numBatches; i += NUM_CHECKPOINTERS) {
void *src = reinterpret_cast<void*>(
reinterpret_cast<intptr_t>(array.data()) + i * BATCH_SIZE);
void *dst = reinterpret_cast<void*>(
reinterpret_cast<intptr_t>(cp.data()) + i * BATCH_SIZE);
memcpy(dst, src, BATCH_SIZE);
munmap(src, BATCH_SIZE);
}
});
}
for (std::thread& thread : cpthreads) {
thread.join();
}
printf("CP finished successfully! Child exiting.\n");
exit(0);
}
#endif // #ifdef WITH_FORK
// spawn worker threads
std::array<std::thread, NUM_WORKERS> threads;
for (size_t tid = 0; tid < NUM_WORKERS; tid++) {
threads[tid] = std::thread([&, tid] {
// write to array
std::array<inttype, NUM_ELEMS()>::iterator it;
for (it = array.begin() + tid; it < array.end(); it += NUM_WORKERS) {
*it = tid;
}
});
}
timepoint tStart = std::chrono::high_resolution_clock::now();
#ifdef WITH_FORK
// allow reaping child process while workers work
std::thread childWaitThread = std::thread([&] {
if (waitpid(pid, nullptr, 0)) {
perror("waitpid");
}
timepoint tChild = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> durationChild = tChild - tStart;
printf("reunited with child after (s): %lf\n", durationChild.count());
});
#endif
// wait for workers to finish
for (std::thread& thread : threads) {
thread.join();
}
timepoint tEnd = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> duration = tEnd - tStart;
printf("duration (s): %lf\n", duration.count());
#ifdef WITH_FORK
childWaitThread.join();
#endif
}
The size of the array is 1GB, which is about 250K pages, where each page is 4KB in size. For this program, the number of page faults that occur due writing to CoW pages can be easily estimated. It can also measured using the Linux perf tool. The new operator initializes the array to zero. So the following line of code:
std::array<inttype, NUM_ELEMS()> *arrayptr = new std::array<inttype, NUM_ELEMS()>();
will cause about 250K page faults. Similarly, the following line of the code:
std::array<inttype, NUM_ELEMS()> *cpptr = new std::array<inttype, NUM_ELEMS()>();
will cause another 250K page faults. All of these page faults are minor, i.e., they can be handled without accessing the disk drive. Allocating two 1GB arrays will not cause any major faults for a system with much more physical memory.
At this point, about 500K page faults have already occurred (there will be other pages faults caused by other memory accesses from the program, of course, but they can be neglected). The execution of std::fill will not cause any minor faults but the virtual pages of the arrays have already been mapped to dedicated physical pages.
The execution of the program then proceeds to forking the child process and creating the worker threads of the parent process. The creation of the child process by itself is sufficient to make a snapshot of the array, so there is really no need to do anything in the child process. In fact, when the child process is forked, the virtual pages of both arrays are marked as copy-on-write. The child process reads from arrayptr and writes to cpptr, which results in additional 250K minor faults. The parent process also writes to arrayptr, which also results in additional 250K minor faults. So making a copy in the child process and unmapping the pages does not improve performance. On the contrary, the number of page faults is doubled and performance is significantly degraded.
You can measure the number of minor and major faults using the following command:
perf stat -r 3 -e minor-faults,major-faults ./binary
This will, by default, count minor and major faults for the whole process tree. The -r 3 option tells perf to repeat the experiment three times and report the average and standard deviation.
I noticed also that the total number of threads is 28 + 4. The optimal number of threads is approximately equal to the total number of online logical cores on your system. If the number of threads is much larger or much smaller than that, performance will be degraded due to the overhead of creating too many threads and switching between them.
Another potential issue may exist in the following loop:
for (it = array.begin() + tid; it < array.end(); it += NUM_WORKERS) {
*it = tid;
}
Different threads may try to write more than once to the same cache line at the same time, resulting in false sharing. This may not be a significant issue depending on the size of the cache line of your processor, the number of threads, and whether all cores are running at the same frequency, so it's hard to say without measuring. A better loop shape would be having the elements of each thread to be contiguous in the array.

What could cause a mutex to misbehave?

I've been busy the last couple of months debugging a rare crash caused somewhere within a very large proprietary C++ image processing library, compiled with GCC 4.7.2 for an ARM Cortex-A9 Linux target. Since a common symptom was glibc complaining about heap corruption, the first step was to employ a heap corruption checker to catch oob memory writes. I used the technique described in https://stackoverflow.com/a/17850402/3779334 to divert all calls to free/malloc to my own function, padding every allocated chunk of memory with some amount of known data to catch out-of-bounds writes - but found nothing, even when padding with as much as 1 KB before and after every single allocated block (there are hundreds of thousands of allocated blocks due to intensive use of STL containers, so I can't enlarge the padding further, plus I assume any write more than 1KB out of bounds would eventually trigger a segfault anyway). This bounds checker has found other problems in the past so I don't doubt its functionality.
(Before anyone says 'Valgrind', yes, I have tried that too with no results either.)
Now, my memory bounds checker also has a feature where it prepends every allocated block with a data struct. These structs are all linked in one long linked list, to allow me to occasionally go over all allocations and test memory integrity. For some reason, even though all manipulations of this list are mutex protected, the list was getting corrupted. When investigating the issue, it began to seem like the mutex itself was occasionally failing to do its job. Here is the pseudocode:
pthread_mutex_t alloc_mutex;
static bool boolmutex; // set to false during init. volatile has no effect.
void malloc_wrapper() {
// ...
pthread_mutex_lock(&alloc_mutex);
if (boolmutex) {
printf("mutex misbehaving\n");
__THROW_ERROR__; // this happens!
}
boolmutex = true;
// manipulate linked list here
boolmutex = false;
pthread_mutex_unlock(&alloc_mutex);
// ...
}
The code commented with "this happens!" is occasionally reached, even though this seems impossible. My first theory was that the mutex data structure was being overwritten. I placed the mutex within a struct, with large arrays before and after it, but when this problem occurred the arrays were untouched so nothing seems to be overwritten.
So.. What kind of corruption could possibly cause this to happen, and how would I find and fix the cause?
A few more notes. The test program uses 3-4 threads for processing. Running with less threads seems to make the corruptions less common, but not disappear. The test runs for about 20 seconds each time and completes successfully in the vast majority of cases (I can have 10 units repeating the test, with the first failure occurring after 5 minutes to several hours). When the problem occurs it is quite late in the test (say, 15 seconds in), so this isn't a bad initialization issue. The memory bounds checker never catches actual out of bounds writes but glibc still occasionally fails with a corrupted heap error (Can such an error be caused by something other than an oob write?). Each failure generates a core dump with plenty of trace information; there is no pattern I can see in these dumps, no particular section of code that shows up more than others. This problem seems very specific to a particular family of algorithms and does not happen in other algorithms, so I'm quite certain this isn't a sporadic hardware or memory error. I have done many more tests to check for oob heap accesses which I don't want to list to keep this post from getting any longer.
Thanks in advance for any help!
Thanks to all commenters. I've tried nearly all suggestions with no results, when I finally decided to write a simple memory allocation stress test - one that would run a thread on each of the CPU cores (my unit is a Freescale i.MX6 quad core SoC), each allocating and freeing memory in random order at high speed. The test crashed with a glibc memory corruption error within minutes or a few hours at most.
Updating the kernel from 3.0.35 to 3.0.101 solved the problem; both the stress test and the image processing algorithm now run overnight without failing. The problem does not reproduce on Intel machines with the same kernel version, so the problem is specific either to ARM in general or perhaps to some patch Freescale included with the specific BSP version that included kernel 3.0.35.
For those curious, attached is the stress test source code. Set NUM_THREADS to the number of CPU cores and build with:
<cross-compiler-prefix>g++ -O3 test_heap.cpp -lpthread -o test_heap
I hope this information helps someone. Cheers :)
// Multithreaded heap stress test. By Itay Chamiel 20151012.
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <unistd.h>
#include <assert.h>
#include <pthread.h>
#include <sys/time.h>
#define NUM_THREADS 4 // set to number of CPU cores
#define ALIVE_INDICATOR NUM_THREADS
// Each thread constantly allocates and frees memory. In each iteration of the infinite loop, decide at random whether to
// allocate or free a block of memory. A list of 500-1000 allocated blocks is maintained by each thread. When memory is allocated
// it is added to this list; when freeing, a random block is selected from this list, freed and removed from the list.
void* thr(void* arg) {
int* alive_flag = (int*)arg;
int thread_id = *alive_flag; // this is a number between 0 and (NUM_THREADS-1) given by main()
int cnt = 0;
timeval t_pre, t_post;
gettimeofday(&t_pre, NULL);
const int ALLOCATE=1, FREE=0;
const unsigned int MINSIZE=500, MAXSIZE=1000;
const int MAX_ALLOC=10000;
char* membufs[MAXSIZE];
unsigned int membufs_size = 0;
int num_allocs = 0, num_frees = 0;
while(1)
{
int action;
// Decide whether to allocate or free a memory block.
// if we have less than MINSIZE buffers, allocate.
if (membufs_size < MINSIZE) action = ALLOCATE;
// if we have MAXSIZE, free.
else if (membufs_size >= MAXSIZE) action = FREE;
// else, decide randomly.
else {
action = ((rand() & 0x1)? ALLOCATE : FREE);
}
if (action == ALLOCATE) {
// choose size to allocate, from 1 to MAX_ALLOC bytes
size_t size = (rand() % MAX_ALLOC) + 1;
// allocate and fill memory
char* buf = (char*)malloc(size);
memset(buf, 0x77, size);
// add buffer to list
membufs[membufs_size] = buf;
membufs_size++;
assert(membufs_size <= MAXSIZE);
num_allocs++;
}
else { // action == FREE
// choose a random buffer to free
size_t pos = rand() % membufs_size;
assert (pos < membufs_size);
// free and remove from list by replacing entry with last member
free(membufs[pos]);
membufs[pos] = membufs[membufs_size-1];
membufs_size--;
assert(membufs_size >= 0);
num_frees++;
}
// once in 10 seconds print a status update
gettimeofday(&t_post, NULL);
if (t_post.tv_sec - t_pre.tv_sec >= 10) {
printf("Thread %d [%d] - %d allocs %d frees. Alloced blocks %u.\n", thread_id, cnt++, num_allocs, num_frees, membufs_size);
gettimeofday(&t_pre, NULL);
}
// indicate alive to main thread
*alive_flag = ALIVE_INDICATOR;
}
return NULL;
}
int main()
{
int alive_flag[NUM_THREADS];
printf("Memory allocation stress test running on %d threads.\n", NUM_THREADS);
// start a thread for each core
for (int i=0; i<NUM_THREADS; i++) {
alive_flag[i] = i; // tell each thread its ID.
pthread_t th;
int ret = pthread_create(&th, NULL, thr, &alive_flag[i]);
assert(ret == 0);
}
while(1) {
sleep(10);
// check that all threads are alive
bool ok = true;
for (int i=0; i<NUM_THREADS; i++) {
if (alive_flag[i] != ALIVE_INDICATOR)
{
printf("Thread %d is not responding\n", i);
ok = false;
}
}
assert(ok);
for (int i=0; i<NUM_THREADS; i++)
alive_flag[i] = 0;
}
return 0;
}

Mergesort pThread implementation taking same time as single-threaded

(I have tried to simplify this as much as i could to find out where I'm doing something wrong.)
The ideea of the code is that I have a global array *v (I hope using this array isn't slowing things down, the threads should never acces the same value because they all work on different ranges) and I try to create 2 threads each one sorting the first half, respectively the second half by calling the function merge_sort() with the respective parameters.
On the threaded run, i see the process going to 80-100% cpu usage (on dual core cpu) while on the no threads run it only stays at 50% yet the run times are very close.
This is the (relevant) code:
//These are the 2 sorting functions, each thread will call merge_sort(..). Is this a problem? both threads calling same (normal) function?
void merge (int *v, int start, int middle, int end) {
//dynamically creates 2 new arrays for the v[start..middle] and v[middle+1..end]
//copies the original values into the 2 halves
//then sorts them back into the v array
}
void merge_sort (int *v, int start, int end) {
//recursively calls merge_sort(start, (start+end)/2) and merge_sort((start+end)/2+1, end) to sort them
//calls merge(start, middle, end)
}
//here i'm expecting each thread to be created and to call merge_sort on its specific range (this is a simplified version of the original code to find the bug easier)
void* mergesort_t2(void * arg) {
t_data* th_info = (t_data*)arg;
merge_sort(v, th_info->a, th_info->b);
return (void*)0;
}
//in main I simply create 2 threads calling the above function
int main (int argc, char* argv[])
{
//some stuff
//getting the clock to calculate run time
clock_t t_inceput, t_sfarsit;
t_inceput = clock();
//ignore crt_depth for this example (in the full code i'm recursively creating new threads and i need this to know when to stop)
//the a and b are the range of values the created thread will have to sort
pthread_t thread[2];
t_data next_info[2];
next_info[0].crt_depth = 1;
next_info[0].a = 0;
next_info[0].b = n/2;
next_info[1].crt_depth = 1;
next_info[1].a = n/2+1;
next_info[1].b = n-1;
for (int i=0; i<2; i++) {
if (pthread_create (&thread[i], NULL, &mergesort_t2, &next_info[i]) != 0) {
cerr<<"error\n;";
return err;
}
}
for (int i=0; i<2; i++) {
if (pthread_join(thread[i], &status) != 0) {
cerr<<"error\n;";
return err;
}
}
//now i merge the 2 sorted halves
merge(v, 0, n/2, n-1);
//calculate end time
t_sfarsit = clock();
cout<<"Sort time (s): "<<double(t_sfarsit - t_inceput)/CLOCKS_PER_SEC<<endl;
delete [] v;
}
Output (on 1 million values):
Sort time (s): 1.294
Output with direct calling of merge_sort, no threads:
Sort time (s): 1.388
Output (on 10 million values):
Sort time (s): 12.75
Output with direct calling of merge_sort, no threads:
Sort time (s): 13.838
Solution:
I'd like to thank WhozCraig and Adam too as they've hinted to this from the beginning.
I've used the inplace_merge(..) function instead of my own and the program run times are as they should now.
Here's my initial merge function (not really sure if the initial, i've probably modified it a few times since, also array indices might be wrong right now, i went back and forth between [a,b] and [a,b), this was just the last commented-out version):
void merge (int *v, int a, int m, int c) { //sorts v[a,m] - v[m+1,c] in v[a,c]
//create the 2 new arrays
int *st = new int[m-a+1];
int *dr = new int[c-m+1];
//copy the values
for (int i1 = 0; i1 <= m-a; i1++)
st[i1] = v[a+i1];
for (int i2 = 0; i2 <= c-(m+1); i2++)
dr[i2] = v[m+1+i2];
//merge them back together in sorted order
int is=0, id=0;
for (int i=0; i<=c-a; i++) {
if (id+m+1 > c || (a+is <= m && st[is] <= dr[id])) {
v[a+i] = st[is];
is++;
}
else {
v[a+i] = dr[id];
id++;
}
}
delete st, dr;
}
all this was replaced with:
inplace_merge(v+a, v+m, v+c);
Edit, some times on my 3ghz dual core cpu:
1 million values:
1 thread : 7.236 s
2 threads: 4.622 s
4 threads: 4.692 s
10 million values:
1 thread : 82.034 s
2 threads: 46.189 s
4 threads: 47.36 s
There's one thing that struck me: "dynamically creates 2 new arrays[...]". Since both threads will need memory from the system, they need to acquire a lock for that, which could well be your bottleneck. In particular the idea of doing microscopic array allocations sounds horribly inefficient. Someone suggested an in-place sort that doesn't need any additional storage, which is much better for performance.
Another thing is the often-forgotten starting half-sentence for any big-O complexity measurements: "There is an n0 so that for all n>n0...". In other words, maybe you haven't reached n0 yet? I recently saw a video (hopefully someone else will remember it) where some people tried to determine this limit for some algorithms, and their results were that these limits are surprisingly high.
Note: since OP uses Windows, my answer below (which incorrectly assumed Linux) might not apply. I left it for sake of those who might find the information useful.
clock() is a wrong interface for measuring time on Linux: it measures CPU time used by the program (see http://linux.die.net/man/3/clock), which in case of multiple threads is the sum of CPU time for all threads. You need to measure elapsed, or wallclock, time. See more details in this SO question: C: using clock() to measure time in multi-threaded programs, which also tells what API can be used instead of clock().
In the MPI-based implementation that you try to compare with, two different processes are used (that's how MPI typically enables concurrency), and the CPU time of the second process is not included - so the CPU time is close to wallclock time. Nevertheless, it's still wrong to use CPU time (and so clock()) for performance measurement, even in serial programs; for one reason, if a program waits for e.g. a network event or a message from another MPI process, it still spends time - but not CPU time.
Update: In Microsoft's implementation of C run-time library, clock() returns wall-clock time, so is OK to use for your purpose. It's unclear though if you use Microsoft's toolchain or something else, like Cygwin or MinGW.

Negative Speedup on Multithreading my Program

On my laptop with Intel Pentium dual-core processor T2370 (Acer Extensa) I ran a simple multithreading speedup test. I am using Linux. The code is pasted below. While I was expecting a speedup of 2-3 times, I was surprised to see a slowdown by a factor of 2. I tried the same with gcc optimization levels -O0 ... -O3, but everytime I got the same result. I am using pthreads. I also tried the same with only two threads (instead of 3 threads in the code), but the performance was similar.
What could be the reason? The faster version took reasonably long - about 20 secs - so it seems is not an issue of startup overhead.
NOTE: This code is a lot buggy (indeed it does not make much sense as the output of serial and parallel versions would be different). The intention was just to "get" a speedup comparison for the same number of instructions.
#include <stdio.h>
#include <time.h>
#include <unistd.h>
#include <pthread.h>
class Thread{
private:
pthread_t thread;
static void *thread_func(void *d){((Thread *)d)->run();}
public:
Thread(){}
virtual ~Thread(){}
virtual void run(){}
int start(){return pthread_create(&thread, NULL, Thread::thread_func, (void*)this);}
int wait(){return pthread_join(thread, NULL);}
};
#include <iostream>
const int ARR_SIZE = 100000000;
const int N = 20;
int arr[ARR_SIZE];
int main(void)
{
class Thread_a:public Thread{
public:
Thread_a(int* a): arr_(a) {}
void run()
{
for(int n = 0; n<N; n++)
for(int i=0; i<ARR_SIZE/3; i++){ arr_[i] += arr_[i-1];}
}
private:
int* arr_;
};
class Thread_b:public Thread{
public:
Thread_b(int* a): arr_(a) {}
void run()
{
for(int n = 0; n<N; n++)
for(int i=ARR_SIZE/3; i<2*ARR_SIZE/3; i++){ arr_[i] += arr_[i-1];}
}
private:
int* arr_;
};
class Thread_c:public Thread{
public:
Thread_c(int* a): arr_(a) {}
void run()
{
for(int n = 0; n<N; n++)
for(int i=2*ARR_SIZE/3; i<ARR_SIZE; i++){ arr_[i] += arr_[i-1];}
}
private:
int* arr_;
};
{
Thread *a=new Thread_a(arr);
Thread *b=new Thread_b(arr);
Thread *c=new Thread_c(arr);
clock_t start = clock();
if (a->start() != 0) {
return 1;
}
if (b->start() != 0) {
return 1;
}
if (c->start() != 0) {
return 1;
}
if (a->wait() != 0) {
return 1;
}
if (b->wait() != 0) {
return 1;
}
if (c->wait() != 0) {
return 1;
}
clock_t end = clock();
double duration = (double)(end - start) / CLOCKS_PER_SEC;
std::cout << duration << "seconds\n";
delete a;
delete b;
}
{
clock_t start = clock();
for(int n = 0; n<N; n++)
for(int i=0; i<ARR_SIZE; i++){ arr[i] += arr[i-1];}
clock_t end = clock();
double duration = (double)(end - start) / CLOCKS_PER_SEC;
std::cout << "serial: " << duration << "seconds\n";
}
return 0;
}
See also: What can make a program run slower when using more threads?
The times you are reporting are measured using the clock function:
The clock() function returns an approximation of processor time used by the program.
$ time bin/amit_kumar_threads.cpp
6.62seconds
serial: 2.7seconds
real 0m5.247s
user 0m9.025s
sys 0m0.304s
The real time will be less for multiprocessor tasks, but the processor time will typically be greater.
When you use multiple threads, the work may be done by more than one processor, but the amount of work is the same, and in addition there may be some overhead such as contention for limited resources. clock() measures the total processor time, which will be the work + any contention overhead. So it should never be less than the processor time for doing the work in a single thread.
It's a little hard to tell from the question whether you knew this, and were surprised that the value returned by clock() was twice that for a single thread rather than being only a little more, or you were expecting it to be less.
Using clock_gettime() instead (you'll need the realtime library librt, g++ -lrt etc.) gives:
$ time bin/amit_kumar_threads.cpp
2.524 seconds
serial: 2.761 seconds
real 0m5.326s
user 0m9.057s
sys 0m0.344s
which still is less of a speed-up than one might hope for, but at least the numbers make some sense.
100000000*20/2.5s = 800Hz, the bus frequency is 1600 MHz, so I suspect with a read and a write for each iteration (assuming some caching), you're memory bandwidth limited as tstenner suggests, and the clock() value shows that most of the time some of your processors are waiting for data. (does anyone know whether clock() time includes such stalls?)
The only thing your thread does is adding some elements, so your application should be IO-bound. When you add an extra thread, you have 2 CPUs sharing the memory bus, so it won't go faster, instead, you'll have cache misses etc.
I believe that your algorithm essentially makes your cache memory useless.
Probably what you are seeing is the effect of (non)locality of reference between the three threads. Essentially because each thread is operating on a different section of data that is widely separated from the others you are causing cache misses as the data section for one thread replaces that for another thread in your cache. If your program was constructed so that the threads operated on sections of data that were smaller (so that they could all be kept in memory) or closer together (so that all threads could use the same in-cache pages), you'd see a performance boost. As it is I suspect that your slow down is because a lot of memory references are having to be satisifed from main memory instead of from your cache.
Not related to your threading issues, but there is a bounds error in your code.
You have:
for(int i=0; i<ARR_SIZE; i++){ arr[i] += arr[i-1];}
When i is zero you will be doing
arr[0] += arr[-1];
Also see herb's article on how multi cpu and cache lines interference in multithreaded code specially the section `All Sharing Is Bad -- Even of "Unshared" Objects...'
As others have pointed out, threads don't necessarily provide improvements to speed. In this particular example, the amount of time spent in each thread is significantly less than the amount of time required to perform context switches and synchronization.
tstenner has got it mostly right.
This is mainly a benchmark of your OS's "allocate and map a new page" algorithm. That array allocation allocates 800MB of virtual memory; the OS won't actually allocate real physical memory until it's needed. "Allocate and map a new page" is usually protected by a mutex, so more cores won't help.
Your benchmark also stresses the memory bus (minimum 800MB transferred; on OSs that zero memory just before they give it to you, the worst case is 800MB * 7 transfers). Adding more cores isn't really going to help if the bottleneck is the memory bus.
You have 3 threads that are trampling all over the same memory. The cache lines are being read and written to by different threads, so will be ping-ponging between the L1 caches on the two CPU cores. (A cache line that is to be written to can only be in one L1 cache, and that must be the L1 cache that is attached to the CPU code that's doing the write). This is not very efficient. The CPU cores are probably spending most of their time waiting for the cache line to be transferred, which is why this is slower with threads than if you single-threaded it.
Incidentally, the code is also buggy because the same array is read & written from different CPUs without locking. Proper locking would have an effect on performance.
Threads take you to the promised land of speed boosts(TM) when you have a proper vector implementation. Which means that you need to have:
a proper parallelization of your algorithm
a compiler that knows and can spread your algorithm out on the hardware as a parallel procedure
hardware support for parallelization
It is difficult to come up with the first. You need to be able to have redundancy and make sure that it's not eating in your performance, proper merging of data for processing the next batch of data and so on ...
But this is then only a theoretical standpoint.
Running multiple threads doesn't give you much when you have only one processor and a bad algorithm. Remember -- there is only one processor, so your threads have to wait for a time slice and essentially you are doing sequential processing.