Closed rkaminsk closed 2 years ago
The problem seems to be somehow related to splitting. For example, competition + recording works. Splitting with either backtracking or recording fails randomly. This happens in configs using splitting right from the start or ones that switch from competition to splitting for enumeration. One gets relatively short failing runs using
#include <clasp/clasp_facade.h>
#include <clasp/solver.h>
#include <iostream>
#include <fstream>
int main() {
while (true) {
Clasp::ClaspConfig config;
config.solve.enumMode = Clasp::EnumOptions::enum_record;
config.solve.numModels = 0;
config.solve.setSolvers(4);
config.solve.algorithm.mode = Clasp::mt::ParallelSolveOptions::Algorithm::mode_split;
Clasp::ClaspFacade libclasp;
std::ifstream input("queens.aspif");
libclasp.start(config, input);
size_t num_models[] = { 1, 0, 0, 2, 10, 4, 40, 92, 352, 724 };
for (size_t i = 0; libclasp.read() && i < 6; ++i) {
libclasp.prepare();
for (size_t j = 0; j < 1; ++j) {
libclasp.solve();
size_t n = libclasp.summary().numEnum;
if (num_models[i] != n) {
std::cerr
<< "error expected " << num_models[i]
<< " models in call " << (i + 1)
<< " but got " << n << std::endl;
}
assert(num_models[i] == n);
}
}
}
}
I added a few tracing commands and it seems like there is a terminate event send even though there are still solvers not having exhausted their search:
thread[1]: basic solve!!!
thread[1]: splitting work from solver!
thread[1]: split off/continuing to work on: 123 87 ~5 ~38 ~18 ~4 6 39/~39
thread[0]: got work of size: 123 87 ~5 ~38 ~18 ~4 6 39
thread[1]: finished with result false
thread[3]: no work left
thread[1]: no work left
thread[0]: got a terminate event
thread[4]: stopping
thread[2]: splitting work from solver!
thread[2]: split off/continuing to work on: 123 87 ~5 ~38 ~18 ~58 ~4 ~6 ~2 ~17 20/~20
thread[2]: got a terminate event
thread[2]: finished with result false
Let's see if I can find the reason. :smile:
@rkaminsk Thanks for looking into this. Unfortunately, I don't have time this and next week to investigate further :( But I'll look into this asap.
@rkaminsk Thanks for looking into this. Unfortunately, I don't have time this and next week to investigate further :( But I'll look into this asap.
No worries. I'll continue looking a bit. And post here if I find something.
I dug a little further and it seems like in the ParallelSolve::SharedData::requestWork
function the counter of the BarrierSemaphore
gets decreased by calling down
more than once for the same solver within one request. This results in a terminate event even though there is still a solver that is working.
Question: Could spurious wake ups in BarrierSemaphore::unsafe_wait
be a problem?
It seems like the problem is indeed caused by spurious wake ups. With the patched condition variable below, all models are enumerated. I opened #82 to handle this problem right in the BarrierSemaphore
class.
class condition_variable {
public:
void wait(std::unique_lock<std::mutex> &lock) {
{
std::lock_guard<std::mutex> guard_{mut_};
wait_ += 1;
}
var_.wait(lock, [this]() {
std::lock_guard<std::mutex> guard_{mut_};
if (notify_ > 0) {
--notify_;
--wait_;
return true;
}
return false;
});
}
bool wait_for(unique_lock<mutex>& lock, double timeInSecs) {
// TODO: spurious wakeups should be suppressed here, too.
return var_.wait_for(lock, std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::duration<double>(timeInSecs)))
== std::cv_status::no_timeout;
}
void notify_one() noexcept {
std::lock_guard<std::mutex> guard_{mut_};
if (notify_ < wait_) {
notify_ += 1;
}
var_.notify_one();
}
void notify_all() noexcept {
std::lock_guard<std::mutex> guard_{mut_};
notify_ = wait_;
var_.notify_all();
}
private:
std::mutex mut_;
std::condition_variable var_;
size_t notify_ = 0;
size_t wait_ = 0;
};
Many thanks for your help. Hopefully fixed in https://github.com/potassco/clasp/tree/dev
The following program run with queens.aspif.gz sometimes omits models. Unfortunately, it's a bit hard to reproduce. It happens quite frequently in my Ubuntu 20.04 virtual machine with 2 cores. I managed to trigger the problem using between 3 and 64 threads. The higher the number of threads the higher the chance that solutions are omitted.
I'll try to dig a bit deeper. Just wanted to put the example somewhere safe.