Re: [Boost-users] [boost::compute] [OS X 10.11.5] [GeForce GT 750M] Segmentation fault

Subject: Re: [Boost-users] [boost::compute] [OS X 10.11.5] [GeForce GT 750M] Segmentation fault
From: Nicholas Yue (yue.nicholas_at_[hidden])
Date: 2016-05-19 19:44:38

On 2016-05-19 4:00 PM, Prashant wrote:
> Hi,
>     Example code to sort a vector on GPU (GeForce GT 750M) generates  SEGV.
>     However, it runs fine on Iris Pro (Intel(R) Core(TM) i7-4850HQ CPU).
>
>     Having said that, it works as long as size of vector being sorted is small(<2 million).
>     The code throws an exception if the size of the vector is less 9 million.
>     Exception: boost/1_61_0/include/boost/compute/command_queue.hpp(453): Throw in
> function boost::compute::event
> boost::compute::command_queue::enqueue_write_buffer(const boost::compute::buffer
> &, size_t, size_t, const void *, const boost::compute::wait_list &)
> Dynamic exception type:
> boost::exception_detail::clone_impl<boost::exception_detail::error_info_injector
> <boost::compute::opencl_error> >
> std::exception::what: Invalid Value
>     Another observation: if the size of vector is 50 million the sorting works fine,
> though the timings are worse than Iris Pro.
>
> Regards,
> Prashant
I have encounter a different error but it might be that my GPU is too 
old. Anyway, on my MacBook (2008) OS X Yosemite 10.10.4, I build and ran 
the example code with the following results. Hopefully, this is useful 
for the developer to debug related problem.

NOTE : I have tried with very small sizes e.g. 10, 100 etc (same crash)

Nicholass-MacBook:build_BoostTutorialClang nicholas$ ./compute/sorting 
10000000
===============CPU==================
time: iteration (0) : 1267 ms
time: iteration (1) : 1273 ms
time: iteration (2) : 1275 ms
time: iteration (3) : 1276 ms
time: iteration (4) : 1273 ms
Platform 'Apple'
Ignoring non GPU devices.
====
   GPU Device: GeForce 9400M
====
/Users/nicholas/systems-clang/boost/v1.61.0/include/boost/compute/program.hpp(271): 
Throw in function void boost::compute::program::build(const std::string &)
Dynamic exception type: 
boost::exception_detail::clone_impl<boost::exception_detail::error_info_injector<boost::compute::opencl_error> 
 >
std::exception::what: Build Program Failure
====

>
> ----------------------------Cut here-------------------------------------
> #include <iostream>
> #include <vector>
> #include <algorithm>
> #include <boost/foreach.hpp>
> #include <boost/compute/core.hpp>
> #include <boost/compute/platform.hpp>
> #include <boost/compute/algorithm.hpp>
> #include <boost/compute/container/vector.hpp>
> #include <boost/compute/functional/math.hpp>
> #include <boost/compute/types/builtin.hpp>
> #include <boost/compute/function.hpp>
> #include <boost/chrono/include.hpp>
> #include <boost/exception/all.hpp>
>
> namespace compute = boost::compute;
>
> int main(int argc, char* argv[])
> {
>      if (argc != 2) {
>          std::cout << "Usage: " << argv[0] << " <size> " << std::endl;
>          return 0;
>      }
>
>      // generate random data on the host
>      std::vector<float> host_vector(atoi(argv[1]));
>      std::generate(host_vector.begin(), host_vector.end(), rand);
>
>      std::cout <<
> "===============CPU==================\n";
>      for (size_t k=0; k<5; k++)
>      {
>          std::vector<float> host_copy_vector(host_vector);
>          auto start = std::chrono::high_resolution_clock::now();
>          std::sort(host_copy_vector.begin(), host_copy_vector.end());
>
>          auto duration = std::chrono::duration_cast<std::chrono::milliseconds>
> (std::chrono::high_resolution_clock::now() - start);
>          std::cout << "time: iteration ("<< k << ") : " << duration.count() << " ms" <<
> std::endl;
>      }
>
>
>      std::vector<compute::platform> platforms = compute::system::platforms();
>
>      for(size_t i = 0; i < platforms.size(); i++){
>          const compute::platform &platform = platforms[i];
>          std::cout << "Platform '" << platform.name() << "'" << std::endl;
>          std::vector<compute::device> devices = platform.devices();
>
>          for(size_t j = 0; j < devices.size(); j++){
>              const compute::device &device = devices[j];
>
>              std::string type;
>              if(device.type() & compute::device::gpu)
>                  type = "GPU Device";
>              else if(device.type() & compute::device::cpu)
>                  type = "CPU Device";
>              else if(device.type() & compute::device::accelerator)
>                  type = "Accelerator Device";
>              else
>                  type = "Unknown Device";
>
>              if (type != "GPU Device") {
>                  std::cout << "Ignoring non GPU devices.\n";
>                  continue;
>              }
>
>              std::cout <<
> "====\n";
>              std::cout << "  " << type << ": " << device.name() << std::endl;
>              std::cout <<
> "====\n";
>              compute::context context(device);
>              compute::command_queue queue(context, device);
>
>              for (size_t k=0; k<5; k++)
>              {
>                  compute::vector<float> device_vector(host_vector.size(), context);
>
>                  // copy data from the host to the device
>                  compute::copy(
>                      host_vector.begin(), host_vector.end(), device_vector.begin(), queue
>                  );
>
>                  auto start = std::chrono::high_resolution_clock::now();
>                  try {
>                    compute::sort(device_vector.begin(), device_vector.end(), queue);
>                  } catch (boost::exception & e) {
>                    std::cerr << diagnostic_information(e);
>                    break;
>                  }
>
>                  auto duration = std::chrono::duration_cast<std::chrono::milliseconds>
> (std::chrono::high_resolution_clock::now() - start);
>                  std::cout << "time: iteration ("<< k << ") : " << duration.count() << " ms" <<
> std::endl;
>              }
>
>              std::cout <<
> "====\n";
>          }
>      }
>
>      return 0;
> }
>
>
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-- 
Nicholas Yue
Graphics - Arnold, Alembic, RenderMan, OpenGL, HDF5
Custom Dev - C++ porting, OSX, Linux, Windows
https://ca.linkedin.com/in/nicholasyue
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