![vega conflict hack tool vega conflict hack tool](https://vegaconflicthack2015.files.wordpress.com/2015/07/vega-conflict-banner.png)
Much of the profiling overhead affects the start time thus, it is visible in the launch time. Enabling profiling on a command queue adds approximately 10 μs to 40 μs overhead to all clEnqueue calls. For CPU devices, the kernel launch time is fast (tens of 1’s), but for discrete GPU devices it can be several hundred μs. The kernel launch time includes both the time spent in the user application (after enqueuing the command, but before it is submitted to the device), as well as the time spent in the runtime to launch the kernel. KernelExecTimeNs calculation shown above.Īnother interesting metric to track is the kernel launch time (Start - Queue). The Kernel Time metric reported in the Profiler output uses the built-in OpenCL timing capability and reports the same result as the The CodeXL GPU Profiler also can record the execution time for a kernel automatically. ``clFinish`` (m圜ommandQ) // wait for all events to finishĬlGetEventProfilingInfo(myEvent, CL_PROFILING_COMMAND_START, sizeof(cl_ulong), &startTime, NULL) ĬlGetEventProfilingInfo(myEvent, CL_PROFILING_COMMAND_END, sizeof(cl_ulong), &endTimeNs, NULL) Ĭl_ulong kernelExecTimeNs = endTime-startTime
VEGA CONFLICT HACK TOOL HOW TO
The sample code below shows how to compute the kernel execution time (End- Start):ĬlCreateCommandQueue (., CL_QUEUE_PROFILING_ENABLE, NULL)
![vega conflict hack tool vega conflict hack tool](https://static.getjar.com/ss/3c/960890_4.jpg)
For AMD GPU devices, this time is only approximately defined and is not detailed in this section.ĬL_PROFILING_COMMAND_START - Indicates when the command starts execution on the requested device.ĬL_PROFILING_COMMAND_END - Indicates when the command finishes execution on the requested device. This is set by the OpenCL runtime when the user calls an clEnqueue* function.ĬL_PROFILING_COMMAND_SUBMIT - Indicates when the command is submitted to the device. Once profiling is enabled, the OpenCL runtime automatically records timestamp information for every kernel and memory operation submitted to the queue.ĬL_PROFILING_COMMAND_QUEUED - Indicates when the command is enqueued into a command-queue on the host. The OpenCL runtime provides a built-in mechanism for timing the execution of kernels by setting the CL_QUEUE_PROFILING_ENABLE flag when the queue is created. The Analyze Mode in CodeXL provides the Statistics View, which can be used to gather useful statistics regarding the GPU usage of kernels. Once the trace data has been used to discover which kernel is most in need of optimization, you can collect the GPU performance counters to drill down into the kernel execution on a GPU device. You can find the list of performance counters supported by AMD Radeon™ GPUs in the CodeXL documentation. This information can be used to find possible bottlenecks in the kernel execution. You can confirm that the application has been using the hardware efficiently.įor example, the timeline should show that non-dependent kernel executions and data transfer operations occurred simultaneously.ĬodeXL also provides information about GPU kernel performance counters. It can be hard to find this type of synchronization error using traditional debugging techniques. For example, if kernel A execution is dependent on a buffer operation and outputs from kernel B execution, then kernel A execution must appear after the completion of the buffer execution and kernel B execution in the time grid. You can confirm that synchronization has been performed properly in the application. The Timeline View lets you easily confirm that the high-level structure of your application is correct by verifying that the number of queues and contexts created match your expectations for the application. The Timeline View can be useful for debugging your OpenCL application. For information about how to use CodeXL to gather performance data about your OpenCL application, such as application traces and timeline views, see the CodeXL home page. This information is used to discover bottlenecks in the application and find ways to optimize the application’s performance for AMD platforms.ĬodeXL 1.7, the latest version as of this writing, is available as an extension to Microsoft® Visual Studio®, a stand-alone version for Windows, and a stand-alone version for Linux.įor a high-level summary of CodeXL features, see Chapter 4 in the AMD OpenCL User Guide.
![vega conflict hack tool vega conflict hack tool](https://www.softpaz.com/screenshots/vega-conflict-player-lookup-tool-phenomite/1.png)
HIP-Supported CUDA API Reference Guide v4.5ĪMD’s CodeXL is an OpenCL kernel debugging and memory and performance analysis tool that gathers data from the OpenCL run-time and OpenCL devices during the execution of an OpenCL application.AMD Instinct™ High Performance Computing and Tuning Guide.ROCm™ Learning Center and Knowledge Base - NEW!!.
VEGA CONFLICT HACK TOOL SOFTWARE
Hardware and Software Support Information.