QA:Testcase intel compute runtime

Description

This test case is designed to stress test the Intel Compute Runtime with diverse and complex OpenCL workloads to evaluate performance, stability, and functionality on Intel GPUs.

Setup

1. Follow the setup instructions from the basic "intel-compute-runtime" test case to prepare the environment.
2. Ensure additional development tools and libraries are installed for compiling complex OpenCL programs, e.g., sudo dnf install gcc-c++ ocl-icd ocl-icd-devel.
3. Obtain or develop OpenCL programs that cover a wide range of workloads, such as:

 - A program that performs heavy computations (e.g., matrix multiplication, cryptographic algorithms).
 - A program that requires high memory bandwidth (e.g., large dataset transfers between host and GPU memory).
 - A program designed to test parallel execution efficiency (e.g., particle simulations, n-body problems). 

How to test

Be as specific as required for the target audience.

1. Compile and run the heavy computation OpenCL program. Monitor the execution time and compare it against expected performance metrics.
2. Compile and run the high memory bandwidth OpenCL program. Observe the transfer rates and ensure there are no errors or excessive delays.
3. Compile and run the parallel execution efficiency OpenCL program. Evaluate how well the GPU handles parallel tasks and check for any anomalies or performance bottlenecks.
4. For each test, monitor the system's thermal and power consumption metrics to assess the runtime's efficiency under load.

Expected Results

The following must be true to consider this a successful test run. Be brief ... but explicit.

1. All OpenCL programs compile without errors and run successfully on the Intel GPU.
2. Execution times and performance metrics are within expected ranges for the given hardware, indicating proper utilization of the GPU's computational resources.
3. Data transfer rates for high memory bandwidth tasks do not show unexpected delays, indicating efficient use of memory resources.
4. Parallel execution tests demonstrate efficient workload distribution across the GPU, without significant bottlenecks or errors.
5. The system maintains thermal and power efficiency within safe limits during all tests, indicating stable performance under load.

Optional

For further testing, consider varying the workload sizes and complexity, or testing with OpenCL applications that integrate with other software libraries (e.g., OpenGL for graphics rendering). Additionally, testing on systems with different Intel GPU generations could provide insights into runtime compatibility and performance scaling.