pthread is slower than the "default" version - c

SITUATION
I want to see the advantage of using pthread. If I'm not wrong: threads allow me to execute given parts of program in parallel.
so here is what I try to accomplish: I want to make a program that takes a number(let's say n) and outputs the sum of [0..n].
code
#define MAX 1000000000
int
main() {
long long n = 0;
for (long long i = 1; i < MAX; ++i)
n += i;
printf("\nn: %lld\n", n);
return 0;
}
time: 0m2.723s
to my understanding I could simply take that number MAX and divide by 2 and let 2 threads
do the job.
code
#define MAX 1000000000
#define MAX_THREADS 2
#define STRIDE MAX / MAX_THREADS
typedef struct {
long long off;
long long res;
} arg_t;
void*
callback(void *args) {
arg_t *arg = (arg_t*)args;
for (long long i = arg->off; i < arg->off + STRIDE; ++i)
arg->res += i;
pthread_exit(0);
}
int
main() {
pthread_t threads[MAX_THREADS];
arg_t results[MAX_THREADS];
for (int i = 0; i < MAX_THREADS; ++i) {
results[i].off = i * STRIDE;
results[i].res = 0;
pthread_create(&threads[i], NULL, callback, (void*)&results[i]);
}
for (int i = 0; i < MAX_THREADS; ++i)
pthread_join(threads[i], NULL);
long long result;
result = results[0].res;
for (int i = 1; i < MAX_THREADS; ++i)
result += results[i].res;
printf("\nn: %lld\n", result);
return 0;
}
time: 0m8.530s
PROBLEM
The version with pthread runs slower. Logically this version should run faster, but maybe creation of threads is more expensive.
Can someone suggest a solution or show what I'm doing/understanding wrong here?

Your problem is cache thrashing combined with a lack of optimization (I bet you're compiling without it on).
The naive (-O0) code for
for (long long i = arg->off; i < arg->off + STRIDE; ++i)
arg->res += i;
will access the memory of *arg. With your results array being defined the way it is, that memory is very close to the memory of the next arg and the two threads will fight for the same cache-line, making RAM caching very ineffective.
If you compile with -O1, the loop should use a register instead and only write to memory at the end. Then, you should get better performance with threads (higher optimization levels on gcc seem to optimize the loop out completely)
Another (better) option is to align arg_t on a cache line:
typedef struct {
_Alignas(64) /*typical cache line size*/ long long off;
long long res;
} arg_t;
Then you should get better performance with threads regardless of whether or not you turn optimization on.
Good cache utilization is generally very important in multithreaded programming (and Ulrich Drepper has much to say on that topic in his infamous What Every Programmer Should Know About Memory).

Creating a whole bunch of threads is very unlikely to be quicker than simply adding numbers. The CPU can add an awfully large number of integers in the time it takes the kernel to set up and tear down a thread. To see the benefit of multithreading, you really need each thread to be doing a significant task -- significant compared to the overhead in creating the thread, anyway. Alternatively, you need to keep a pool of threads running, and assign them work according to some allocation strategy.
Multi-threading works best when an application consists of tasks that are somewhat independent, that would otherwise be waiting on one another to complete. It isn't a magic way to get more throughput.

Related

False sharing in multi threads

The following code runs slower as I increase the NTHREADS. Why use more threads make the program run slower? Is there any way to fix it? Someone said it is about false sharing but I do not really understand that concept.
The program basicly calculate the sum from 1 to 100000000. The idea to use multithread is to seperate the number list into several chuncks, and calculate the sum of each chunck parallelly to make the calculation faster.
#include <stdio.h>
#include <stdlib.h>
#include <pthread.h>
#define LENGTH 100000000
#define NTHREADS 2
#define NREPEATS 10
#define CHUNCK (LENGTH / NTHREADS)
typedef struct {
size_t id;
long *array;
long result;
} worker_args;
void *worker(void *args) {
worker_args *wargs = (worker_args*) args;
const size_t start = wargs->id * CHUNCK;
const size_t end = wargs->id == NTHREADS - 1 ? LENGTH : (wargs->id+1) * CHUNCK;
for (size_t i = start; i < end; ++i) {
wargs->result += wargs->array[i];
}
return NULL;
}
int main(void) {
long* numbers = malloc(sizeof(long) * LENGTH);
for (size_t i = 0; i < LENGTH; ++i) {
numbers[i] = i + 1;
}
worker_args *args = malloc(sizeof(worker_args) * NTHREADS);
for (size_t i = 0; i < NTHREADS; ++i) {
args[i] = (worker_args) {
.id = i,
.array = numbers,
.result = 0
};
}
pthread_t thread_ids[NTHREADS];
for (size_t i = 0; i < NTHREADS; ++i) {
pthread_create(thread_ids+i, NULL, worker, args+i);
}
for (size_t i = 0; i < NTHREADS; ++i) {
pthread_join(thread_ids[i], NULL);
}
long sum = 0;
for (size_t i = 0; i < NTHREADS; ++i) {
sum += args[i].result;
}
printf("Run %2zu: total sum is %ld\n", n, sum);
free(args);
free(numbers);
}
Why use more threads make the program run slower?
There is an overhead creating and joining threads. If the threads hasn't much to do then this overhead may be more expensive than the actual work.
Your threads are only doing a simple sum which isn't that expensive. Also consider that going from e.g. 10 to 11 threads doesn't change the work load per thread a lot.
10 threads --> 10000000 sums per thread
11 threads --> 9090909 sums per thread
The overhead of creating an extra thread may exceed the "work load saved" per thread.
On my PC the program runs in less than 100 milliseconds. Multi-threading isn't worth the trouble.
You need a more processing intensive task before multi-threading is worth doing.
Also notice that it seldom make sense to create more threads than the number of cores (incl hyper thread) your computer has.
false sharing
yes, "false sharing" can impact the performance of a multi-threaded program but I doubt it's the real problem in your case.
"false sharing" is something that happens in (some) cache systems when two threads (or rather two cores) writes to two different variables that belongs to the same cache line. In such cases the two threads/cores competes to own the cache line (for writing) and consequently, they'll have to refresh the memory and the cache again and again. That's bad for performance.
As I said - I doubt that is your problem. A clever compiler will do your loop solely be using CPU registers and only write to memory at the end. You can check the disassemble of your code to see if that is the case.
You can avoid "false sharing" by increasing the sizeof of your struct so that each struct fits the size of a cache line on your system.

MPI with pthreads slow performance [duplicate]

I'm trying around on the new C++11 threads, but my simple test has abysmal multicore performance. As a simple example, this program adds up some squared random numbers.
#include <iostream>
#include <thread>
#include <vector>
#include <cstdlib>
#include <chrono>
#include <cmath>
double add_single(int N) {
double sum=0;
for (int i = 0; i < N; ++i){
sum+= sqrt(1.0*rand()/RAND_MAX);
}
return sum/N;
}
void add_multi(int N, double& result) {
double sum=0;
for (int i = 0; i < N; ++i){
sum+= sqrt(1.0*rand()/RAND_MAX);
}
result = sum/N;
}
int main() {
srand (time(NULL));
int N = 1000000;
// single-threaded
auto t1 = std::chrono::high_resolution_clock::now();
double result1 = add_single(N);
auto t2 = std::chrono::high_resolution_clock::now();
auto time_elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(t2-t1).count();
std::cout << "time single: " << time_elapsed << std::endl;
// multi-threaded
std::vector<std::thread> th;
int nr_threads = 3;
double partual_results[] = {0,0,0};
t1 = std::chrono::high_resolution_clock::now();
for (int i = 0; i < nr_threads; ++i)
th.push_back(std::thread(add_multi, N/nr_threads, std::ref(partual_results[i]) ));
for(auto &a : th)
a.join();
double result_multicore = 0;
for(double result:partual_results)
result_multicore += result;
result_multicore /= nr_threads;
t2 = std::chrono::high_resolution_clock::now();
time_elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(t2-t1).count();
std::cout << "time multi: " << time_elapsed << std::endl;
return 0;
}
Compiled with 'g++ -std=c++11 -pthread test.cpp' on Linux and a 3core machine, a typical result is
time single: 33
time multi: 565
So the multi threaded version is more than an order of magnitude slower. I've used random numbers and a sqrt to make the example less trivial and prone to compiler optimizations, so I'm out of ideas.
edit:
This problem scales for larger N, so the problem is not the short runtime
The time for creating the threads is not the problem. Excluding it does not change the result significantly
Wow I found the problem. It was indeed rand(). I replaced it with an C++11 equivalent and now the runtime scales perfectly. Thanks everyone!
On my system the behavior is same, but as Maxim mentioned, rand is not thread safe. When I change rand to rand_r, then the multi threaded code is faster as expected.
void add_multi(int N, double& result) {
double sum=0;
unsigned int seed = time(NULL);
for (int i = 0; i < N; ++i){
sum+= sqrt(1.0*rand_r(&seed)/RAND_MAX);
}
result = sum/N;
}
As you discovered, rand is the culprit here.
For those who are curious, it's possible that this behavior comes from your implementation of rand using a mutex for thread safety.
For example, eglibc defines rand in terms of __random, which is defined as:
long int
__random ()
{
int32_t retval;
__libc_lock_lock (lock);
(void) __random_r (&unsafe_state, &retval);
__libc_lock_unlock (lock);
return retval;
}
This kind of locking would force multiple threads to run serially, resulting in lower performance.
The time needed to execute the program is very small (33msec). This means that the overhead to create and handle several threads may be more than the real benefit. Try using programs that need longer times for the execution (e.g., 10 sec).
To make this faster, use a thread pool pattern.
This will let you enqueue tasks in other threads without the overhead of creating a std::thread each time you want to use more than one thread.
Don't count the overhead of setting up the queue in your performance metrics, just the time to enqueue and extract the results.
Create a set of threads and a queue of tasks (a structure containing a std::function<void()>) to feed them. The threads wait on the queue for new tasks to do, do them, then wait on new tasks.
The tasks are responsible for communicating their "done-ness" back to the calling context, such as via a std::future<>. The code that lets you enqueue functions into the task queue might do this wrapping for you, ie this signature:
template<typename R=void>
std::future<R> enqueue( std::function<R()> f ) {
std::packaged_task<R()> task(f);
std::future<R> retval = task.get_future();
this->add_to_queue( std::move( task ) ); // if we had move semantics, could be easier
return retval;
}
which turns a naked std::function returning R into a nullary packaged_task, then adds that to the tasks queue. Note that the tasks queue needs be move-aware, because packaged_task is move-only.
Note 1: I am not all that familiar with std::future, so the above could be in error.
Note 2: If tasks put into the above described queue are dependent on each other for intermediate results, the queue could deadlock, because no provision to "reclaim" threads that are blocked and execute new code is described. However, "naked computation" non-blocking tasks should work fine with the above model.

Why is the multithreaded version of this program slower?

I am trying to learn pthreads and I have been experimenting with a program that tries to detect the changes on an array. Function array_modifier() picks a random element and toggles it's value (1 to 0 and vice versa) and then sleeps for some time (big enough so race conditions do not appear, I know this is bad practice). change_detector() scans the array and when an element doesn't match it's prior value and it is equal to 1, the change is detected and diff array is updated with the detection delay.
When there is one change_detector() thread (NTHREADS==1) it has to scan the whole array. When there are more threads each is assigned a portion of the array. Each detector thread will only catch the modifications in its part of the array, so you need to sum the catch times of all 4 threads to get the total time to catch all changes.
Here is the code:
#include <pthread.h>
#include <stdio.h>
#include <unistd.h>
#include <stdlib.h>
#include <sys/time.h>
#include <time.h>
#define TIME_INTERVAL 100
#define CHANGES 5000
#define UNUSED(x) ((void) x)
typedef struct {
unsigned int tid;
} parm;
static volatile unsigned int* my_array;
static unsigned int* old_value;
static struct timeval* time_array;
static unsigned int N;
static unsigned long int diff[NTHREADS] = {0};
void* array_modifier(void* args);
void* change_detector(void* arg);
int main(int argc, char** argv) {
if (argc < 2) {
exit(1);
}
N = (unsigned int)strtoul(argv[1], NULL, 0);
my_array = calloc(N, sizeof(int));
time_array = malloc(N * sizeof(struct timeval));
old_value = calloc(N, sizeof(int));
parm* p = malloc(NTHREADS * sizeof(parm));
pthread_t generator_thread;
pthread_t* detector_thread = malloc(NTHREADS * sizeof(pthread_t));
for (unsigned int i = 0; i < NTHREADS; i++) {
p[i].tid = i;
pthread_create(&detector_thread[i], NULL, change_detector, (void*) &p[i]);
}
pthread_create(&generator_thread, NULL, array_modifier, NULL);
pthread_join(generator_thread, NULL);
usleep(500);
for (unsigned int i = 0; i < NTHREADS; i++) {
pthread_cancel(detector_thread[i]);
}
for (unsigned int i = 0; i < NTHREADS; i++) fprintf(stderr, "%lu ", diff[i]);
fprintf(stderr, "\n");
_exit(0);
}
void* array_modifier(void* arg) {
UNUSED(arg);
srand(time(NULL));
unsigned int changing_signals = CHANGES;
while (changing_signals--) {
usleep(TIME_INTERVAL);
const unsigned int r = rand() % N;
gettimeofday(&time_array[r], NULL);
my_array[r] ^= 1;
}
pthread_exit(NULL);
}
void* change_detector(void* arg) {
const parm* p = (parm*) arg;
const unsigned int tid = p->tid;
const unsigned int start = tid * (N / NTHREADS) +
(tid < N % NTHREADS ? tid : N % NTHREADS);
const unsigned int end = start + (N / NTHREADS) +
(tid < N % NTHREADS);
unsigned int r = start;
while (1) {
unsigned int tmp;
while ((tmp = my_array[r]) == old_value[r]) {
r = (r < end - 1) ? r + 1 : start;
}
old_value[r] = tmp;
if (tmp) {
struct timeval tv;
gettimeofday(&tv, NULL);
// detection time in usec
diff[tid] += (tv.tv_sec - time_array[r].tv_sec) * 1000000 + (tv.tv_usec - time_array[r].tv_usec);
}
}
}
when I compile & run like this:
gcc -Wall -Wextra -O3 -DNTHREADS=1 file.c -pthread && ./a.out 100
I get:
665
but when I compile & run like this:
gcc -Wall -Wextra -O3 -DNTHREADS=4 file.c -pthread && ./a.out 100
I get:
152 190 164 242
(this sums up to 748).
So, the delay for the multithreaded program is larger.
My cpu has 6 cores.
Short Answer
You are sharing memory between thread and sharing memory between threads is slow.
Long Answer
Your program is using a number of thread to write to my_array and another thread to read from my_array. Effectively my_array is shared by a number of threads.
Now lets assume you are benchmarking on a multicore machine, you probably are hoping that the OS will assign different cores to each thread.
Bear in mind that on modern processors writing to RAM is really expensive (hundreds of CPU cycles). To improve performance CPUs have multi-level caches. The fastest Cache is the small L1 cache. A core can write to its L1 cache in the order of 2-3 cycles. The L2 cache may take on the order of 20 - 30 cycles.
Now in lots of CPU architectures each core has its own L1 cache but the L2 cache is shared. This means any data that is shared between thread (cores) has to go through the L2 cache which is much slower than the L1 cache. This means that shared memory access tends to be quite slow.
Bottom line is that if you want your multithreaded programs to perform well you need to ensure that threads do not share memory. Sharing memory is slow.
Aside
Never rely on volatile to do the correct thing when sharing memory between thread, either use your library atomic operations or use mutexes. This is because some CPUs allow out of order reads and writes that may do strange things if you do not know what you are doing.
It is rare that a multithreaded program scales perfectly with the number of threads. In your case you measured a speed-up factor of ca 0.9 (665/748) with 4 threads. That is not so good.
Here are some factors to consider:
The overhead of starting threads and dividing the work. For small jobs the cost of starting additional threads can be considerably larger than the actual work. Not applicable to this case, since the overhead isn't included in the time measurements.
"Random" variations. Your threads varied between 152 and 242. You should run the test multiple times and use either the mean or the median values.
The size of the test. Generally you get more reliable measurements on larger tests (more data). However, you need to consider how having more data affects the caching in L1/L2/L3 cache. And if the data is too large to fit into RAM you need to factor in disk I/O. Usually, multithreaded implementations are slower, because they want to work on more data at a time but in rare instances they can be faster, a phenomenon called super-linear speedup.
Overhead caused by inter-thread communication. Maybe not a factor in your case, since you don't have much of that.
Overhead caused by resource locking. Usually has a low impact on cpu utilization but may have a large impact on the total real time used.
Hardware optimizations. Some CPUs change the clock frequency depending on how many cores you use.
The cost of the measurement itself. In your case a change will be detected within 25 (100/4) iterations of the for loop. Each iteration takes but a few clock cycles. Then you call gettimeofday which probably costs thousands of clock cycles. So what you are actually measuring is more or less the cost of calling gettimeofday.
I would increase the number of values to check and the cost to check each value. I would also consider turning off compiler optimizations, since these can cause the program to do unexpected things (or skip some things entirely).

cpu cacheline and prefetch policy

I read this article http://igoro.com/archive/gallery-of-processor-cache-effects/. The article said that because cacheline delay, the code:
int[] arr = new int[64 * 1024 * 1024];
// Loop 1
for (int i = 0; i < arr.Length; i++) arr[i] *= 3;
// Loop 2
for (int i = 0; i < arr.Length; i += 16) arr[i] *= 3;
will almost have same execute time, and I wrote some sample c code to test it. I run the code on Xeon(R) E3-1230 V2 with Ubuntu 64bit, ARMv6-compatible processor rev 7 with Debian, and also run it on Core 2 T6600. All results are not what the article said.
My code is as follows:
long int jobTime(struct timespec start, struct timespec stop) {
long int seconds = stop.tv_sec - start.tv_sec;
long int nsec = stop.tv_nsec - start.tv_nsec;
return seconds * 1000 * 1000 * 1000 + nsec;
}
int main() {
struct timespec start;
struct timespec stop;
int i = 0;
struct sched_param param;
int * arr = malloc(LENGTH * 4);
printf("---------sieofint %d\n", sizeof(int));
param.sched_priority = 0;
sched_setscheduler(0, SCHED_FIFO, &param);
//clock_gettime(CLOCK_MONOTONIC, &start);
//for (i = 0; i < LENGTH; i++) arr[i] *= 5;
//clock_gettime(CLOCK_MONOTONIC, &stop);
//printf("step %d : time %ld\n", 1, jobTime(start, stop));
clock_gettime(CLOCK_MONOTONIC, &start);
for (i = 0; i < LENGTH; i += 2) arr[i] *= 5;
clock_gettime(CLOCK_MONOTONIC, &stop);
printf("step %d : time %ld\n", 2, jobTime(start, stop));
}
Each time I choose one piece to compile and run (comment one and uncomment another).
compile with:
gcc -O0 -o cache cache.c -lrt
On Xeon I get this:
step 1 : 258791478
step 2 : 97875746
I want to know whether or not what the article said was correct? Alternatively, do the newest cpus have more advanced prefetch policies?
Short Answer (TL;DR): you're accessing uninitialized data, your first loop has to allocate new physical pages for the entire array within the timed loop.
When I run your code and comment each of the sections in turn, I get almost the same timing for the two loops. However, I do get the same results you report when I uncomment both sections and run them one after the other. This makes me suspect you also did that, and suffered from cold start effect when comparing the first loop with the second. It's easy to check - just replace the order of the loops and see if the first is still slower.
To avoid, either pick a large enough LENGTH (depending on your system) so that you dont get any cache benefits from the first loop helping the second, or just add a single traversal of the entire array that's not timed.
Note that the second option wouldn't exactly prove what the blog wanted to say - that memory latency masks the execution latency, so it doesn't matter how many elements of a cache line you use, you're still bottlenecked by the memory access time (or more accurately - the bandwidth)
Also - benchmarking code with -O0 is a really bad practice
Edit:
Here's what i'm getting (removed the scheduling as it's not related).
This code:
for (i = 0; i < LENGTH; i++) arr[i] = 1; // warmup!
clock_gettime(CLOCK_MONOTONIC, &start);
for (i = 0; i < LENGTH; i++) arr[i] *= 5;
clock_gettime(CLOCK_MONOTONIC, &stop);
printf("step %d : time %ld\n", 1, jobTime(start, stop));
clock_gettime(CLOCK_MONOTONIC, &start);
for (i = 0; i < LENGTH; i+=16) arr[i] *= 5;
clock_gettime(CLOCK_MONOTONIC, &stop);
Gives :
---------sieofint 4
step 1 : time 58862552
step 16 : time 50215446
While commenting the warmup line gives the same advantage as you reported on the second loop:
---------sieofint 4
step 1 : time 279772411
step 16 : time 50615420
Replacing the order of the loops (warmup is still commented) shows it's indeed not related to the step size but to the ordering:
---------sieofint 4
step 16 : time 250033980
step 1 : time 59168310
(gcc version 4.6.3, on Opteron 6272)
Now a note about what's going on here - in theory, you'd expect warmup to be meaningful only when the array is small enough to sit in some cache - in this case the LENGTH you used is too big even for the L3 on most machines. However, you're forgetting the pagemap - you didn't just skip warming the data itself - you avoided initializing it in the first place. This can never give you meaningful results in real life, but since this a benchmark you didn't notice that, you're just multiplying junk data for the latency of it.
This means that each new page you access on the first loop doesn't only go to memory, it would probably get a page fault and have to call the OS to map a new physical page for it. This is a lengthy process, multiplies by the number of 4K pages you use - accumulating to a very long time. At this array size you can't even benefit from TLBs (you have 16k different physical 4k pages, way more than most TLBs can support even with 2 levels), so it's just the question of the fault flows. This can probably be measures by any profiling tool.
The second iteration on the same array won't have this effect and would be much faster - even though is still has to do a full pagewalk on each new page (that's done purely in HW), and then fetch the data from memory.
By the way, this is also the reason when you benchmark some behavior, you repeat the same thing multiple times (in this case it would have solved your problem if you had run over the array several time with the same stride, and ignored the first few rounds).

What could be some possible problems with this use of OpenMP?

I was trying to figure out how to parallelize a segment of code in OpenMP, where the inside of the for loop is independent from the rest of it.
Basically the project is dealing with particle systems, but I don't think that should relevant to the parallelization of the code. Is it a caching problem where the for loop divides the threads in a way such that the particles are not cached in each core in an efficient manner?
Edit: As mentioned by an answer below, I'm wondering why I'm not getting speedup.
#pragma omp parallel for
for (unsigned i = 0; i < psize-n_dead; ++i)
{
s->particles[i].pos = s->particles[i].pos + dt * s->particles[i].vel;
s->particles[i].vel = (1 - dt*.1) * s->particles[i].vel + dt*s->force;
// printf("%d", omp_get_thread_num());
}
If you're asking whether it's parallelized correctly, it looks fine. I don't see any data-races or loop-dependencies that could break it.
But I think you're wondering on why you aren't getting any speedup with parallelism.
Since you mentioned that the trip count, psize-n_dead will be on the order of 4000. I'd say that's actually pretty small given the amount of work in the loop.
In other words, you don't have much total work to be worth parallelizing. So threading overhead is probably eating up any speedup that you should be gaining. If possible, you should try parallelizing at a higher level.
EDIT: You updated your comment to include up to 200000.
For larger values, it's likely that you'll be memory bound in some way. Your loop merely iterates through all the data doing very little work. So using more threads probably won't help much (if at all).
There is no correctness issues such as data races in this piece of code.
Assuming that the number of particles to process is big enough to warrant parallelism, I do not see OpenMP related performance issues in this code. By default, OpenMP will split the loop iterations statically in equal portions across all threads, so any cache conflicts may only occur at the boundaries of these portions, i.e. just in a few iterations of the loop.
Unrelated to OpenMP (and so to the parallel speedup problem), possibly performance improvement can be achieved by switching from array-of-structs to struct-of-arrays, as this might help compiler to vectorize the code (i.e. use SIMD instructions of a target processor):
#pragma omp parallel for
for (unsigned i = 0; i < psize-n_dead; ++i)
{
s->particles.pos[i] = s->particles.pos[i] + dt * s->particles.vel[i];
s->particles.vel[i] = (1 - dt*.1) * s->particles.vel[i] + dt*s->force;
}
Such reorganization assumes that most time all particles are processed in a loop like this one. Working with an individual particle requires more cache lines to be loaded, but if you process them all in a loop, the net amount of cache lines loaded is nearly the same.
How sure are you that you're not getting speedup?
Trying it both ways - array of structs and struct of arrays, compiled with gcc -O3 (gcc 4.6), on a dual quad-core nehalem, I get for psize-n_dead = 200000, running 100 iterations for better timer accuracy:
Struct of arrays (reported time are in milliseconds)
$ for t in 1 2 4 8; do export OMP_NUM_THREADS=$t; time ./foo; done
Took time 90.984000
Took time 45.992000
Took time 22.996000
Took time 11.998000
Array of structs:
$ for t in 1 2 4 8; do export OMP_NUM_THREADS=$t; time ./foo; done
Took time 58.989000
Took time 28.995000
Took time 14.997000
Took time 8.999000
However, I because the operation is so short (sub-ms) I didn't see any speedup without doing 100 iterations because of timer accuracy. Also, you'd have to have a machine with good memory bandwidth to to get this sort of behaviour; you're only doing ~3 FMAs and another multiplication for every two pieces of data you read in.
Code for array-of-structs follows.
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
typedef struct particle_struct {
double pos;
double vel;
} particle;
typedef struct simulation_struct {
particle *particles;
double force;
} simulation;
void tick(struct timeval *t) {
gettimeofday(t, NULL);
}
/* returns time in seconds from now to time described by t */
double tock(struct timeval *t) {
struct timeval now;
gettimeofday(&now, NULL);
return (double)(now.tv_sec - t->tv_sec) + ((double)(now.tv_usec - t->tv_usec)/1000000.);
}
void update(simulation *s, unsigned psize, double dt) {
#pragma omp parallel for
for (unsigned i = 0; i < psize; ++i)
{
s->particles[i].pos = s->particles[i].pos+ dt * s->particles[i].vel;
s->particles[i].vel = (1 - dt*.1) * s->particles[i].vel + dt*s->force;
}
}
void init(simulation *s, unsigned np) {
s->force = 1.;
s->particles = malloc(np*sizeof(particle));
for (unsigned i=0; i<np; i++) {
s->particles[i].pos = 1.;
s->particles[i].vel = 1.;
}
int main(void)
{
const unsigned np=200000;
simulation s;
struct timeval clock;
init(&s, np);
tick(&clock);
for (int iter=0;iter< 100; iter++)
update(&s, np, 0.75);
double elapsed=tock(&clock)*1000.;
printf("Took time %lf\n", elapsed);
free(s.particles);
}

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