Suppose I have a the following function which makes use of #pragma omp parallel internally.
void do_heavy_work(double * input_array);
I now want to do_heavy_work on many input_arrays thus:
void do_many_heavy_work(double ** input_arrays, int num_arrays)
{
for (int i = 0; i < num_arrays; ++i)
{
do_heavy_work(input_arrays[i]);
}
}
Let's say I have N hardware threads. The implementation above would cause num_arrays invocations of do_heavy_work to occur in a serial fashion, each using all N threads internally to do whatever parallel thing it wants.
Now assume that when num_arrays > 1 it is actually more efficient to parallelise over this outer loop than it is to parallelise internally in do_heavy_work. I now have the following options.
Put #pragma omp parallel for on the outer loop and set OMP_NESTED=1. However, by setting OMP_NUM_THREADS=N I will end up with a large total number of threads (N*num_arrays) to be spawned.
As above but turn off nested parallelism. This wastes available cores when num_arrays < N.
Ideally I want OpenMP to split its team of OMP_NUM_THREADS threads into num_arrays subteams, and then each do_heavy_work can thread over its allocated subteam if given some.
What's the easiest way to achieve this?
(For the purpose of this discussion let's assume that num_arrays is not necessarily known beforehand, and also that I cannot change the code in do_heavy_work itself. The code should work on a number of machines so N should be freely specifiable.)
OMP_NUM_THREADS can be set to a list, thus specifying the number of threads at each level of nesting. E.g. OMP_NUM_THREADS=10,4 will tell the OpenMP runtime to execute the outer parallel region with 10 threads and each nested region will execute with 4 threads for a total of up to 40 simultaneously running threads.
Alternatively, you can make your program adaptive with code similar to this one:
void do_many_heavy_work(double ** input_arrays, int num_arrays)
{
#pragma omp parallel num_threads(num_arrays)
{
int nested_team_size = omp_get_max_threads() / num_arrays;
omp_set_num_threads(nested_team_size);
#pragma omp for
for (int i = 0; i < num_arrays; ++i)
{
do_heavy_work(input_arrays[i]);
}
}
}
This code will not use all available threads if the value of OMP_NUM_THREADS is not divisible by num_arrays. If having different number of threads per nested region is fine (it could result in some arrays being processed faster than others), come up with an idea of how to distribute the threads and set nested_team_size in each thread accordingly. Calling omp_set_num_threads() from within a parallel region only affects nested regions started by the calling thread, so you can have different nested team sizes.
Related
I'm looking to multithread a for loop using OpenMP.
As I understood when you do a loop like;
#pragma omp parallel for num_threads(NTHREADS)
for (size_t i = 0; i < length; i++)
{
...
All the threads will just grab an i and move on with their work.
For my implementation, I need to have it that they work "sequentially" in parallel.
By that I mean that e.g., for a length of 800 with 8 threads, I need thread 1 to work on 0 to 99, thread 2 to work on 100-199 and so on.
Is this possible with OpenMP?
Your desired behavior is the default. The loop can be scheduled in several ways, and schedule(static) is the default: the loop gets divided in blocks, and the first thread takes the first block, et cetera.
So your initial understanding was wrong: a thread does not grab an index, but a block.
Just to note: if you want a thread to grab a smaller block, you can specify schedule(static,8) or whatever number suits you, but less than 8 runs into cache performance problems.
From OpenMP specification:
schedule([modifier [, modifier]:]kind[, chunk_size])
When kind is static, iterations are divided into chunks of size
chunk_size, and the chunks are assigned to the threads in the team in
a round-robin fashion in the order of the thread number. Each chunk
contains chunk_size iterations, except for the chunk that contains the
sequentially last iteration, which may have fewer iterations. When no
chunk_size is specified, the iteration space is divided into chunks
that are approximately equal in size, and at most one chunk is
distributed to each thread. The size of the chunks is unspecified in
this case.
When the monotonic modifier is specified then each thread executes the
chunks that it is assigned in increasing logical iteration order.
For a team of p threads and a loop of n iterations, let n∕p be the
integer q that satisfies n = p * q - r, with 0 <= r < p. One compliant
implementation of the static schedule (with no specified chunk_size)
would behave as though chunk_size had been specified with value q.
Another compliant implementation would assign q iterations to the
first p - r threads, and q - 1 iterations to the remaining r threads.
This illustrates why a conforming program must not rely on the details
of a particular implementation.
The default schedule is taken from def-sched-var and it is implementation defined, so if your program relies on it, define it explicitly:
schedule(monotonic:static,chunk_size)
In this case it is clearly defined how your program behaves and does not depend on the implementation at all. Note also that your code should not depend on the number of threads, because OpenMP does not guarantee that your parallel region gets all the requested/available threads. Note also that the monotonic modifier is the default in the case of static schedule, so you do not have to state it explicitly.
So, if the above mentioned 'approximate' chunk size or the exact number of threads is not an issue in your case, your code should be
#pragma omp parallel for schedule(static) num_threads(NTHREADS)
On the other hand, if you need a control on chunk_size and/or on the exact number of threads used, you should use something like
#pragma omp parallel num_threads(NTHREADS)
{
#pragma omp single
{
int nthreads=omp_get_num_threads();
//calculate chunk_size based on nthreads
chunk_size=.....
}
#pragma omp for schedule(static,chunk_size)
for(...)
}
I'm trying to parallelize a simulator written in C++ using OpenMP pragmas.
I have a basic understanding of it but no experience.
The code below shows the main method to parallelize:
void run(long long end) {
while (now + dt <= end) {
now += dt;
for (unsigned int i=0; i < populations.size(); i++) {
populations[i]->update(now);
}
}
}
where populations is a std::vector of instances of the class Population. Each population updates its own elements as follows:
void Population::update(long long ts) {
for (unsigned int j = 0; j < this->size(); j++) {
if (check(j,ts)) {
doit(ts, j);
}
}
}
Being each population of a different size, the loop in Population::update() takes a varying amount of time leading to suboptimal speedups. By adding #pragma omp parallel for schedule(static) in the run() method. I get a 2X speedup with 4 threads, however it drops for 8 threads.
I am aware of the schedule(dynamic) clause, allowing to balance out the computation between the threads. However, when I tried to dynamically dispatch the threads I did not observe any improvements.
Am I going in the right direction? Do you think playing with the chunck size would help? Any suggestion is appreciated!
So there is two things to distinguish:
The influence of the number of threads and the scheduling policy.
For the number of threads, having more threads than cores usually slows down performances because of the context switches. So it depends on the number of cores you have on your computer
The difference between the code generated (at least as far as I remember) for static and dynamic is that with the static scheduling, the loop iterations are divided by the number of threads equally and with the dynamic scheduling, the distribution is computed at runtime (after the end of every iteration, the omp runtime is queried with __builtin_GOMP_loop_dynamic_next).
The reason for the slowdown observed when switching to dynamic can be that the loop doesn't contain enough iterations/computations so the overhead of computing dynamically the iterations distribution is not covered by the gain in performance.
(I assumed that every population instance doesn't share data with others)
Just throwing ideas, hope this help =)
I am trying to achieve something contrived using OpenMP.
I have a multi-core system with N available processors. I want to have a vector of objects of length k*P to be populated in batches of P by a single thread (by reading a file), i.e. a single thread reads this file and writes in vecObj[0 to P-1] then vecObj[p to 2P-1] etc. To make things simple, this vector is pre-resized (i.e. inserting using = operator, no pushbacks, constant length as far as we are concerned).
After a batch is written into the vector, I want the remaining N-1 threads to work on the available data. Since every object can take different time to be worked upon, it would be good to have dynamic scheduling for the remaining threads. The below snippet works really well when all the threads are working on the data.
#pragma omp parallel for schedule(dynamic, per_thread)
for(size_t i = 0; i < dataLength(); ++i) {
threadWorkOnElement(vecObj, i);
}
Now, according to me, the the main issue I am facing in thinking up of a solution is the question as to how can I have N-1 threads dynamically scheduled over the range of available data, while another thread just keeps on reading and populating the vector with data?
I am guessing that the issue of writing new data and messaging the remaining threads can be achieved using std atomic.
I think that what I am trying to achieve is along the lines of the following pseudo code
std::atomic<size_t> freshDataEnd;
size_t dataWorkStart = 0;
size_t dataWorkEnd;
#pragma omp parallel
{
#pragma omp task
{
//increment freshDataEnd atomically upon reading every P objects
//return when end of file is reached
readData(vecObj, freshDataEnd);
}
#pragma omp task
{
omp_set_num_threads(N-1);
while(freshDataEnd <= MAX_VEC_LEN) {
if (dataWorkStart < freshDataEnd) {
dataWorkEnd = freshDataEnd;
#pragma omp parallel for schedule(dynamic, per_thread)
for(size_t i = dataWorkStart; i < dataWorkEnd; ++i) {
threadWorkOnElement(vecObj, i);
}
dataWorkStart = dataWorkEnd;
}
}
}
}
Is this the correct approach to achieve what I am trying to do? How can I handle this sort of nested parallelism? Not so important : I would have preferred to stick with openmp directives and not use std atomics, is that possible? How?
Performance wise, which of the following is more efficient?
Assigning in the master thread and copying the value to all threads:
int i = 0;
#pragma omp parallel for firstprivate(i)
for( ; i < n; i++){
...
}
Declaring and assigning the variable in each thread
#pragma omp parallel for
for(int i = 0; i < n; i++){
...
}
Declaring the variable in the master thread but assigning it in each thread.
int i;
#pragma omp parallel for private(i)
for(i = 0; i < n; i++){
...
}
It may seem a silly question and/or the performance impact may be negligible. But I'm parallelizing a loop that does a small amount of computation and is called a large number of times, so any optimization I can squeeze out of this loop is helpful.
I'm looking for a more low level explanation and how OpenMP handles this.
For example, if parallelizing for a large number of threads I assume the second implementation would be more efficient, since initializing a variable using xor is far more efficient than copying the variable to all the threads
There is not much of a difference in terms of performance among the 3 versions you presented, since each one of them is using #pragma omp parallel for. Hence, OpenMP will automatically assign each for iteration to different threads. Thus, variable i will became private to each thread, and each thread will have a different range of for iterations to work with. The variable 'i' was automatically set to private in order to avoid race conditions when updating this variable. Since, the variable 'i' will be private on the parallel for anyway, there is no need to put private(i) on the #pragma omp parallel for.
Nevertheless, your first version will produce an error since OpenMP is expecting that the loop right underneath of #pragma omp parallel for have the following format:
for(init-expr; test-expr;incr-expr)
inorder to precompute the range of work.
The for directive places restrictions on the structure of all
associated for-loops. Specifically, all associated for-loops must
have the following canonical form:
for (init-expr; test-expr;incr-expr) structured-block (OpenMP Application Program Interface pag. 39/40.)
Edit: I tested your two last versions, and inspected the generated assembly. Both version produce the same assembly, as you can see -> version 2 and version 3.
I need to parallelize this loop, I though that to use was a good idea, but I never studied them before.
#pragma omp parallel for
for(std::set<size_t>::const_iterator it=mesh->NEList[vid].begin();
it!=mesh->NEList[vid].end(); ++it){
worst_q = std::min(worst_q, mesh->element_quality(*it));
}
In this case the loop is not parallelized because it uses iterator and the compiler cannot
understand how to slit it.
Can You help me?
OpenMP requires that the controlling predicate in parallel for loops has one of the following relational operators: <, <=, > or >=. Only random access iterators provide these operators and hence OpenMP parallel loops work only with containers that provide random access iterators. std::set provides only bidirectional iterators. You may overcome that limitation using explicit tasks. Reduction can be performed by first partially reducing over private to each thread variables followed by a global reduction over the partial values.
double *t_worst_q;
// Cache size on x86/x64 in number of t_worst_q[] elements
const int cb = 64 / sizeof(*t_worst_q);
#pragma omp parallel
{
#pragma omp single
{
t_worst_q = new double[omp_get_num_threads() * cb];
for (int i = 0; i < omp_get_num_threads(); i++)
t_worst_q[i * cb] = worst_q;
}
// Perform partial min reduction using tasks
#pragma omp single
{
for(std::set<size_t>::const_iterator it=mesh->NEList[vid].begin();
it!=mesh->NEList[vid].end(); ++it) {
size_t elem = *it;
#pragma omp task
{
int tid = omp_get_thread_num();
t_worst_q[tid * cb] = std::min(t_worst_q[tid * cb],
mesh->element_quality(elem));
}
}
}
// Perform global reduction
#pragma omp critical
{
int tid = omp_get_thread_num();
worst_q = std::min(worst_q, t_worst_q[tid * cb]);
}
}
delete [] t_worst_q;
(I assume that mesh->element_quality() returns double)
Some key points:
The loop is executed serially by one thread only, but each iteration creates a new task. These are most likely queued for execution by the idle threads.
Idle threads waiting at the implicit barrier of the single construct begin consuming tasks as soon as they are created.
The value pointed by it is dereferenced before the task body. If dereferenced inside the task body, it would be firstprivate and a copy of the iterator would be created for each task (i.e. on each iteration). This is not what you want.
Each thread performs partial reduction in its private part of the t_worst_q[].
In order to prevent performance degradation due to false sharing, the elements of t_worst_q[] that each thread accesses are spaced out so to end up in separate cache lines. On x86/x64 the cache line is 64 bytes, therefore the thread number is multiplied by cb = 64 / sizeof(double).
The global min reduction is performed inside a critical construct to protect worst_q from being accessed by several threads at once. This is for illustrative purposes only since the reduction could also be performed by a loop in the main thread after the parallel region.
Note that explicit tasks require compiler which supports OpenMP 3.0 or 3.1. This rules out all versions of Microsoft C/C++ Compiler (it only supports OpenMP 2.0).
Random-Access Container
The simplest solution is to just throw everything into a random-access container (like std::vector) and use the index-based loops that are favoured by OpenMP:
// Copy elements
std::vector<size_t> neListVector(mesh->NEList[vid].begin(), mesh->NEList[vid].end());
// Process in a standard OpenMP index-based for loop
#pragma omp parallel for reduction(min : worst_q)
for (int i = 0; i < neListVector.size(); i++) {
worst_q = std::min(worst_q, complexCalc(neListVector[i]));
}
Apart from being incredibly simple, in your situation (tiny elements of type size_t that can easily be copied) this is also the solution with the best performance and scalability.
Avoiding copies
However, in a different situation than yours you may have elements that aren't copied as easily (larger elements) or cannot be copied at all. In this case you can just throw the corresponding pointers in a random-access container:
// Collect pointers
std::vector<const nonCopiableObjectType *> neListVector;
for (const auto &entry : mesh->NEList[vid]) {
neListVector.push_back(&entry);
}
// Process in a standard OpenMP index-based for loop
#pragma omp parallel for reduction(min : worst_q)
for (int i = 0; i < neListVector.size(); i++) {
worst_q = std::min(worst_q, mesh->element_quality(*neListVector[i]));
}
This is slightly more complex than the first solution, still has the same good performance on small elements and increased performance on larger elements.
Tasks and Dynamic Scheduling
Since someone else brought up OpenMP Tasks in his answer, I want to comment on that to. Tasks are a very powerful construct, but they have a huge overhead (that even increases with the number of threads) and in this case just make things more complex.
For the min reduction the use of Tasks is never justified because the creation of a Task in the main thread costs much more than just doing the std::min itself!
For the more complex operation mesh->element_quality you might think that the dynamic nature of Tasks can help you with load-balancing problems, in case that the execution time of mesh->element_quality varies greatly between iterations and you don't have enough iterations to even it out. But even in that case, there is a simpler solution: Simply use dynamic scheduling by adding the schedule(dynamic) directive to your parallel for line in one of my previous solutions. It achieves the same behaviour which far less overhead.