mirror of https://github.com/BOINC/boinc.git
519a0bcbef
- If you run the client with --run_test_app, runs "test_app" in the current directory and interacts with it (and does nothing else). It can suspend/resume it with arbitrary timing; this is controlled in run_test_app() (app_start.cpp). - example app: add --critical_section option. This lets you test the runtime system for apps that do most of their work in a critical section (like GPU apps). - Add some logging messages (conditioned by DEBUG_BOINC_API) to the runtime system. - boinc_finish() waits for the timer thread to write final messages; make sure it doesn't do anything else (like suspend the worker thread) during this period |
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INIT_DATA test files | ||
Makefile_AMD | ||
Makefile_NVIDIA | ||
Makefile_mac | ||
ReadMe.txt | ||
openclapp.cpp | ||
openclapp.hpp | ||
openclapp_kernels.cl |
ReadMe.txt
Windows projects. To build for Mac: make -f Makefile_mac To build for Linux (assuming you have installed the appropriate GPU computing SDK): For ATI/AMD: make -f Makefile_AMD For NVIDIA: make -f Makefile_NVIDIA For intel Ivy Bridge: modify one of the above make files for the appropriate paths to the OpenCL headers and libraries. Adjust the -I and -L arguments for Linux if the OpenCL headers and libraries are in non-standard locations. To run: This same sample is designed to run with AMD, NVIDIA and Intel Ivy Bridge GPUs. It is supplied with 3 minimal init_data.xml files, one for each of these 3 vendors (GPU "types".) Copy the appropriate init_data.xml file into the directory containing the openclapp executable. Then run from the Terminal: $ cd to/the/directory/containing/executable/and/init_data.xml/file $ ./openclapp [options] command line options -run_slow: sleep 1 second after each character -cpu_time N: use about N CPU seconds after copying files -early_exit: exit(10) after 30 iterations -early_crash: crash after 30 iterations -early_sleep: go into infinite sleep after 30 iterations ============================================================== Important notes about the sample code: Since a computer can have multiple GPUs, the application must use the GPU assigned by the BOINC client. To do this, it must call the following API: int boinc_get_opencl_ids( int argc, char** argv, int type, cl_device_id* device, cl_platform_id* platform ); The arguments are as follows: argc, argv: the argv and argc received by the application's main() from the BOINC client. type: may be PROC_TYPE_NVIDIA_GPU, PROC_TYPE_AMD_GPU or PROC_TYPE_INTEL_GPU. device: a pointer to the variable to receive the cl_device_id of the desired GPU. platform: a pointer to the variable to receive the cl_platform_id of the desired GPU. Currently, BOINC expects projects to provide separate production applications for each GPU vendor (GPU type), with a separate "plan class" for each. BOINC currently supports GPUs from the three major vendors: AMD (ATI). NVIDIA or Intel (Ivy Bridge or later). BOINC refers to the vendors as gpu "types." Because older clients do not write the <gpu_type> field into the init_data.xml file, your application must pass the appropriate GPU type as the third argument in the boinc_get_opencl_ids() call, or it will not be compatible with older clients. However, to avoid redundancy, this one sample is designed to work with OpenCl-capable GPUs from any of the three vendors. To accomplish this, it does not pass a valid type in the boinc_get_opencl_ids() call; it requires init_data.xml file to have a valid <gpu_type> field, and so would not be compatible with older clients. This shortcut is not acceptable for production OpenCL applications; you _must_ pass in a type of either PROC_TYPE_NVIDIA_GPU, PROC_TYPE_AMD_GPU or PROC_TYPE_INTEL_GPU. ============================================================== What is the difference between a GPU's gpu_device_num and its gpu_opencl_dev_index? In most cases, they are identical. But on Macs which have CUDA installed, Mac OpenCL does not always recognize all NVIDIA GPUs recognized by CUDA. In that case, the gpu_device_num is the device's position among all the CUDA-capable GPUs, and the gpu_opencl_dev_index is the device's position among all the OpenCL-capable GPUs.