Omp_num_threads windows

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Specifies the subset of available hardware resources for the hardware topology hierarchy. The subset is specified in terms of number of units per upper layer unit starting from top layer downwards. You can also specify an offset value to set which resources to use. When available, you can specify attributes to select different subsets of resources. Depending on what resources are detected, you may be able to specify additional resources, such as NUMA domains and groups of hardware resources that share certain cache levels.

The attributes available to users are:. The hardware cache can be specified as a unit, e. L2 for L2 cache, or LL for last level cache. Default: If omitted, the default value is to use all the available hardware resources. Enables true or disables false the copying of the floating-point control settings of the primary thread to the floating-point control settings of the OpenMP worker threads at the start of each parallel region.

Default: true. Selects the OpenMP run-time library execution mode. The values for this variable are serial , turnaround , or throughput.

Enables true or disables false the printing of OpenMP run-time library environment variables during program execution. Two lists of variables are printed: user-defined environment variables settings and effective values of variables used by OpenMP run-time library.

Support for group is now deprecated and will be removed in a future release. Use all instead. Enables true or disables false the printing of OpenMP run-time library version information during program execution. Enables true or disables false displaying warnings from the OpenMP run-time library during program execution. This allows the user to obtain useful runtime information as well as enable or disable certain features. A full list of supported environment variables is defined below.

The debugging output provided is intended for use by libomptarget developers. The output will be saved to the filename specified by the environment variable. For multi-threaded applications, profiling in libomp is also needed.

Any allocations larger than this threshold will not use the memory manager and be freed after the device kernel exits. The default threshold value is 8KB. This includes information about data-mappings and kernel execution. It is recommended to build your application with debugging information enabled, this will enable filenames and variable declarations in the information messages. OpenMP Debugging information is enabled at any level of debugging so a full debug runtime is not required.

For minimal debugging information compile with -gline-tables-only , or compile with -g for full debug information. Any combination of these flags can be used by setting the appropriate bits. For example, to enable printing all data active in an OpenMP target region along with CUDA information, run the following bash command.

Compiling this code targeting nvptx64 with all information enabled will provide the following output from the runtime library. The information from the OpenMP data region shows the two arrays X and Y being copied from the host to the device. This creates an entry in the host-device mapping table associating the host pointers to the newly created device data.

The data mappings in the OpenMP device kernel show the default mappings being used for all the variables used implicitly on the device. This allows for different levels of information to be enabled or disabled for certain regions of code. Using this requires declaring the function signature as an external function so it can be linked with the runtime library.

Common causes of failure could be an invalid pointer access, running out of device memory, or trying to offload when the device is busy. If the application was built with debugging symbols the error messages will additionally provide the source location of the OpenMP target region. For example, consider the following code that implements a simple parallel reduction on the GPU.

This code has a bug that causes it to fail in the offloading region. This shows that there is an illegal memory access occuring inside the OpenMP target region once execution has moved to the CUDA device, suggesting a segmentation fault. This then causes a chain reaction of failures in libomptarget. If we do this it will print the sate of the host-target pointer mappings at the time of failure. This tells us that the only data mapped between the host and the device is the sum variable that will be copied back from the device once the reduction has ended.

There is no entry mapping the host array A to the device. In this situation, the compiler cannot determine the size of the array at compile time so it will simply assume that the pointer is mapped on the device already by default.

The solution is to add an explicit map clause in the target region. This environment variable sets the stack size in bytes for the CUDA plugin. This environment variable sets the amount of memory in bytes that can be allocated using malloc and free for the CUDA plugin. This is necessary for some applications that allocate too much memory either through the user or globalization.

This environment variable sets the amount of dynamic shared memory in bytes used by the kernel once it is launched. The remote offloading plugin permits the execution of OpenMP target regions on devices in remote hosts in addition to the devices connected to the local host.

All target devices on the remote host will be exposed to the application as if they were local devices, that is, the remote host CPU or its GPUs can be offloaded to with the appropriate device number. If the server is running on the same host, each device may be identified twice: once through the device plugins and once through the device plugins that the server application has access to. This plugin consists of libomptarget.

The server application does not have to be running on a remote host, and can instead be used on the same host in order to debug memory mapping during offloading. The server must also have access to the necessary target-specific plugins in order to perform the offloading. For example, the rpc plugin is not designed to be thread-safe, the server cannot concurrently handle offloading from multiple applications at once it is synchronous and will terminate after a single execution.

Subscription added. Subscription removed. Sorry, you must verify to complete this action. Please click the verification link in your email. You may re-send via your profile. Code runs with openMP and yields a speedup of 1.

A Intel fortran compiles and links in openMP. B Portland group compiles and links in openMP. I can run the code with openMP and get a speedup of 1.

Code does not run with more than one thread.