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1   package net.bmahe.genetics4j.gpu.opencl;
2   
3   import java.util.Objects;
4   
5   import org.jocl.CL;
6   import org.jocl.Pointer;
7   import org.jocl.Sizeof;
8   import org.jocl.cl_device_id;
9   import org.jocl.cl_kernel;
10  
11  /**
12   * Utility class providing convenient methods for querying OpenCL kernel work group information.
13   * 
14   * <p>KernelInfoUtils encapsulates the low-level OpenCL API calls required for retrieving kernel-specific execution
15   * characteristics on target devices. This information is essential for optimizing kernel launch parameters and ensuring
16   * efficient resource utilization in GPU-accelerated evolutionary algorithms.
17   * 
18   * <p>Key functionality includes:
19   * <ul>
20   * <li><strong>Work group queries</strong>: Retrieve kernel-specific work group size limits and preferences</li>
21   * <li><strong>Memory usage queries</strong>: Query local and private memory requirements per work-item</li>
22   * <li><strong>Performance optimization</strong>: Access preferred work group size multiples for optimal execution</li>
23   * <li><strong>Resource validation</strong>: Obtain kernel resource requirements for launch parameter validation</li>
24   * </ul>
25   * 
26   * <p>Common usage patterns:
27   * 
28   * <pre>{@code
29   * // Query kernel work group characteristics
30   * long maxWorkGroupSize = KernelInfoUtils.getKernelWorkGroupInfoLong(deviceId, kernel, CL.CL_KERNEL_WORK_GROUP_SIZE);
31   * 
32   * long preferredMultiple = KernelInfoUtils
33   * 		.getKernelWorkGroupInfoLong(deviceId, kernel, CL.CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE);
34   * 
35   * // Query memory requirements
36   * long localMemSize = KernelInfoUtils.getKernelWorkGroupInfoLong(deviceId, kernel, CL.CL_KERNEL_LOCAL_MEM_SIZE);
37   * 
38   * long privateMemSize = KernelInfoUtils.getKernelWorkGroupInfoLong(deviceId, kernel, CL.CL_KERNEL_PRIVATE_MEM_SIZE);
39   * 
40   * // Optimize work group size based on kernel characteristics
41   * long optimalWorkGroupSize = (maxWorkGroupSize / preferredMultiple) * preferredMultiple;
42   * }</pre>
43   * 
44   * <p>Kernel optimization workflow:
45   * <ol>
46   * <li><strong>Kernel compilation</strong>: Compile kernel for target device</li>
47   * <li><strong>Characteristic query</strong>: Retrieve kernel-specific execution parameters</li>
48   * <li><strong>Launch optimization</strong>: Configure work group sizes based on kernel requirements</li>
49   * <li><strong>Resource validation</strong>: Ensure memory requirements don't exceed device limits</li>
50   * </ol>
51   * 
52   * <p>Error handling:
53   * <ul>
54   * <li><strong>Parameter validation</strong>: Validates all input parameters</li>
55   * <li><strong>OpenCL error propagation</strong>: OpenCL errors are propagated as runtime exceptions</li>
56   * <li><strong>Memory management</strong>: Automatically handles buffer allocation and cleanup</li>
57   * </ul>
58   * 
59   * @see KernelInfo
60   * @see KernelInfoReader
61   * @see net.bmahe.genetics4j.gpu.opencl.model.Device
62   */
63  public class KernelInfoUtils {
64  
65  	private KernelInfoUtils() {
66  
67  	}
68  
69  	/**
70  	 * Queries and returns a long value for kernel work group information on the specified device.
71  	 * 
72  	 * <p>This method retrieves kernel-specific execution characteristics that vary by device, such as maximum work group
73  	 * size, preferred work group size multiples, and memory usage requirements. This information is essential for
74  	 * optimizing kernel launch parameters.
75  	 * 
76  	 * @param deviceId  the OpenCL device to query
77  	 * @param kernel    the compiled OpenCL kernel
78  	 * @param parameter the OpenCL parameter constant (e.g., CL_KERNEL_WORK_GROUP_SIZE, CL_KERNEL_LOCAL_MEM_SIZE)
79  	 * @return the long value of the requested kernel work group property
80  	 * @throws IllegalArgumentException if deviceId or kernel is null
81  	 */
82  	public static long getKernelWorkGroupInfoLong(final cl_device_id deviceId, final cl_kernel kernel,
83  			final int parameter) {
84  		Objects.requireNonNull(deviceId);
85  		Objects.requireNonNull(kernel);
86  
87  		final long[] values = new long[1];
88  		CL.clGetKernelWorkGroupInfo(kernel, deviceId, parameter, Sizeof.cl_long, Pointer.to(values), null);
89  
90  		return values[0];
91  	}
92  }