1 package net.bmahe.genetics4j.gpu.opencl.model;
2
3 import org.immutables.value.Value;
4
5 /**
6 * Represents kernel-specific execution characteristics and resource requirements for an OpenCL kernel on a specific device.
7 *
8 * <p>KernelInfo encapsulates the device-specific compilation and execution characteristics of an OpenCL kernel,
9 * providing essential information for optimal work group configuration and resource allocation in GPU-accelerated
10 * evolutionary algorithms. This information is determined at kernel compilation time and varies by device.
11 *
12 * <p>Key kernel characteristics include:
13 * <ul>
14 * <li><strong>Work group constraints</strong>: Maximum and preferred work group sizes for efficient execution</li>
15 * <li><strong>Memory usage</strong>: Local and private memory requirements per work-item</li>
16 * <li><strong>Performance optimization</strong>: Preferred work group size multiples for optimal resource utilization</li>
17 * <li><strong>Resource validation</strong>: Constraints for validating kernel launch parameters</li>
18 * </ul>
19 *
20 * <p>Kernel optimization considerations for evolutionary algorithms:
21 * <ul>
22 * <li><strong>Work group sizing</strong>: Configure launch parameters within device-specific limits</li>
23 * <li><strong>Memory allocation</strong>: Ensure sufficient local memory for parallel fitness evaluation</li>
24 * <li><strong>Performance tuning</strong>: Align work group sizes with preferred multiples</li>
25 * <li><strong>Resource planning</strong>: Account for per-work-item memory requirements</li>
26 * </ul>
27 *
28 * <p>Common usage patterns for kernel configuration:
29 * <pre>{@code
30 * // Query kernel information after compilation
31 * KernelInfo kernelInfo = kernelInfoReader.read(deviceId, kernel, "fitness_evaluation");
32 *
33 * // Configure work group size within device limits
34 * long maxWorkGroupSize = Math.min(kernelInfo.workGroupSize(), device.maxWorkGroupSize());
35 *
36 * // Optimize for preferred work group size multiple
37 * long preferredMultiple = kernelInfo.preferredWorkGroupSizeMultiple();
38 * long optimalWorkGroupSize = (maxWorkGroupSize / preferredMultiple) * preferredMultiple;
39 *
40 * // Validate memory requirements for population size
41 * long populationSize = 1000;
42 * long totalLocalMem = kernelInfo.localMemSize() * optimalWorkGroupSize;
43 * long totalPrivateMem = kernelInfo.privateMemSize() * populationSize;
44 *
45 * // Configure kernel execution with validated parameters
46 * clEnqueueNDRangeKernel(commandQueue, kernel, 1, null,
47 * new long[]{populationSize}, new long[]{optimalWorkGroupSize}, 0, null, null);
48 * }</pre>
49 *
50 * <p>Performance optimization workflow:
51 * <ol>
52 * <li><strong>Kernel compilation</strong>: Compile kernel for target device</li>
53 * <li><strong>Information query</strong>: Read kernel-specific execution characteristics</li>
54 * <li><strong>Work group optimization</strong>: Calculate optimal work group size based on preferences</li>
55 * <li><strong>Memory validation</strong>: Ensure memory requirements fit within device limits</li>
56 * <li><strong>Launch configuration</strong>: Configure kernel execution with optimized parameters</li>
57 * </ol>
58 *
59 * <p>Memory management considerations:
60 * <ul>
61 * <li><strong>Local memory</strong>: Shared among work-items in the same work group</li>
62 * <li><strong>Private memory</strong>: Individual memory per work-item</li>
63 * <li><strong>Total allocation</strong>: Sum of all work-items' memory requirements</li>
64 * <li><strong>Device limits</strong>: Validate against device memory constraints</li>
65 * </ul>
66 *
67 * <p>Error handling and validation:
68 * <ul>
69 * <li><strong>Work group limits</strong>: Ensure launch parameters don't exceed kernel limits</li>
70 * <li><strong>Memory constraints</strong>: Validate total memory usage against device capabilities</li>
71 * <li><strong>Performance degradation</strong>: Monitor for suboptimal work group configurations</li>
72 * <li><strong>Resource conflicts</strong>: Handle multiple kernels competing for device resources</li>
73 * </ul>
74 *
75 * @see Device
76 * @see net.bmahe.genetics4j.gpu.opencl.KernelInfoReader
77 * @see net.bmahe.genetics4j.gpu.opencl.KernelInfoUtils
78 */
79 @Value.Immutable
80 public interface KernelInfo {
81
82 /**
83 * Returns the name of the kernel function.
84 *
85 * @return the kernel function name as specified in the OpenCL program
86 */
87 String name();
88
89 /**
90 * Returns the maximum work group size that can be used when executing this kernel on the device.
91 *
92 * <p>This value represents the maximum number of work-items that can be in a work group when
93 * executing this specific kernel on the target device. It may be smaller than the device's
94 * general maximum work group size due to kernel-specific resource requirements.
95 *
96 * @return the maximum work group size for this kernel
97 */
98 long workGroupSize();
99
100 /**
101 * Returns the preferred work group size multiple for optimal kernel execution performance.
102 *
103 * <p>For optimal performance, the work group size should be a multiple of this value.
104 * This represents the native vector width or wavefront size of the device and helps
105 * achieve better resource utilization and memory coalescing.
106 *
107 * @return the preferred work group size multiple for performance optimization
108 */
109 long preferredWorkGroupSizeMultiple();
110
111 /**
112 * Returns the amount of local memory in bytes used by this kernel.
113 *
114 * <p>Local memory is shared among all work-items in a work group and includes both
115 * statically allocated local variables and dynamically allocated local memory passed
116 * as kernel arguments. This value is used to validate that the total local memory
117 * usage doesn't exceed the device's local memory capacity.
118 *
119 * @return the local memory usage in bytes per work group
120 */
121 long localMemSize();
122
123 /**
124 * Returns the minimum amount of private memory in bytes used by each work-item.
125 *
126 * <p>Private memory is individual to each work-item and includes local variables,
127 * function call stacks, and other per-work-item data. This value helps estimate
128 * the total memory footprint when launching kernels with large work group sizes.
129 *
130 * @return the private memory usage in bytes per work-item
131 */
132 long privateMemSize();
133
134 /**
135 * Creates a new builder for constructing KernelInfo instances.
136 *
137 * @return a new builder for creating kernel information objects
138 */
139 static ImmutableKernelInfo.Builder builder() {
140 return ImmutableKernelInfo.builder();
141 }
142 }