1 package net.bmahe.genetics4j.gpu.opencl.model;
2
3 import java.util.Set;
4
5 import org.immutables.value.Value;
6 import org.jocl.cl_device_id;
7
8 /**
9 * Represents an OpenCL compute device with its capabilities and characteristics for GPU-accelerated evolutionary algorithms.
10 *
11 * <p>Device encapsulates the properties and capabilities of an OpenCL compute device (GPU, CPU, or accelerator)
12 * that can be used for fitness evaluation in evolutionary algorithms. This information is essential for
13 * device selection, kernel optimization, and workload configuration to achieve optimal performance.
14 *
15 * <p>Key device characteristics include:
16 * <ul>
17 * <li><strong>Device identification</strong>: Name, vendor, and version information</li>
18 * <li><strong>Compute capabilities</strong>: Number of compute units and maximum work group sizes</li>
19 * <li><strong>Memory hierarchy</strong>: Global, local, and constant memory sizes and characteristics</li>
20 * <li><strong>Processing features</strong>: Vector width preferences, image support, and built-in kernels</li>
21 * <li><strong>Performance metrics</strong>: Clock frequency and execution capabilities</li>
22 * </ul>
23 *
24 * <p>Device selection considerations for evolutionary algorithms:
25 * <ul>
26 * <li><strong>Device type</strong>: GPU devices typically provide highest parallelism for large populations</li>
27 * <li><strong>Compute units</strong>: More compute units allow better utilization of large populations</li>
28 * <li><strong>Work group sizes</strong>: Must accommodate the parallelism patterns of fitness kernels</li>
29 * <li><strong>Memory capacity</strong>: Must be sufficient for population data and intermediate results</li>
30 * <li><strong>Vector operations</strong>: Vector width preferences can optimize numerical computations</li>
31 * </ul>
32 *
33 * <p>Common device filtering patterns:
34 * <pre>{@code
35 * // Select GPU devices with sufficient parallel processing capability
36 * Predicate<Device> gpuFilter = device ->
37 * device.deviceType().contains(DeviceType.GPU) &&
38 * device.maxComputeUnits() >= 8;
39 *
40 * // Select devices with large work group support for population processing
41 * Predicate<Device> workGroupFilter = device ->
42 * device.maxWorkGroupSize() >= 256;
43 *
44 * // Select devices with high clock frequency for compute-intensive fitness
45 * Predicate<Device> performanceFilter = device ->
46 * device.maxClockFrequency() >= 1000; // MHz
47 *
48 * // Select devices that support floating-point vector operations
49 * Predicate<Device> vectorFilter = device ->
50 * device.preferredVectorWidthFloat() >= 4;
51 *
52 * // Comprehensive filter for evolutionary algorithm suitability
53 * Predicate<Device> eaOptimizedFilter = device ->
54 * device.deviceType().contains(DeviceType.GPU) &&
55 * device.maxComputeUnits() >= 4 &&
56 * device.maxWorkGroupSize() >= 128 &&
57 * device.preferredVectorWidthFloat() >= 2;
58 * }</pre>
59 *
60 * <p>Performance optimization using device information:
61 * <ul>
62 * <li><strong>Work group sizing</strong>: Configure kernel work groups based on {@link #maxWorkGroupSize()}</li>
63 * <li><strong>Parallel dispatch</strong>: Scale parallelism based on {@link #maxComputeUnits()}</li>
64 * <li><strong>Vector operations</strong>: Optimize data layouts for {@link #preferredVectorWidthFloat()}</li>
65 * <li><strong>Memory access patterns</strong>: Design kernels considering memory hierarchy characteristics</li>
66 * </ul>
67 *
68 * <p>Device capability assessment workflow:
69 * <ol>
70 * <li><strong>Device discovery</strong>: Enumerate devices from selected platforms</li>
71 * <li><strong>Capability query</strong>: Read device properties from OpenCL runtime</li>
72 * <li><strong>Model creation</strong>: Create device objects with discovered capabilities</li>
73 * <li><strong>Filtering</strong>: Apply user-defined predicates to select suitable devices</li>
74 * <li><strong>Context creation</strong>: Create OpenCL contexts for selected devices</li>
75 * </ol>
76 *
77 * <p>Common device types in evolutionary computation:
78 * <ul>
79 * <li><strong>GPU devices</strong>: Provide massive parallelism for large population fitness evaluation</li>
80 * <li><strong>CPU devices</strong>: Offer good sequential performance and large memory capacity</li>
81 * <li><strong>Accelerator devices</strong>: Specialized hardware for specific computational patterns</li>
82 * <li><strong>Custom devices</strong>: FPGA or other specialized compute devices</li>
83 * </ul>
84 *
85 * <p>Error handling and compatibility:
86 * <ul>
87 * <li><strong>Device availability</strong>: Devices may become unavailable during execution</li>
88 * <li><strong>Capability validation</strong>: Ensure device supports required kernel features</li>
89 * <li><strong>Memory constraints</strong>: Validate device memory is sufficient for population size</li>
90 * <li><strong>Work group limits</strong>: Ensure kernels respect device work group size limits</li>
91 * </ul>
92 *
93 * @see Platform
94 * @see DeviceType
95 * @see net.bmahe.genetics4j.gpu.spec.GPUEAExecutionContext#deviceFilters()
96 * @see net.bmahe.genetics4j.gpu.opencl.DeviceUtils
97 */
98 @Value.Immutable
99 public interface Device {
100
101 /**
102 * Returns the native OpenCL device identifier.
103 *
104 * @return the OpenCL device ID for low-level operations
105 */
106 cl_device_id deviceId();
107
108 /**
109 * Returns the device name provided by the vendor.
110 *
111 * @return the human-readable device name (e.g., "GeForce RTX 3080", "Intel Core i7")
112 */
113 String name();
114
115 /**
116 * Returns the device vendor name.
117 *
118 * @return the vendor name (e.g., "NVIDIA Corporation", "Intel", "AMD")
119 */
120 String vendor();
121
122 /**
123 * Returns the OpenCL version supported by this device.
124 *
125 * @return the device OpenCL version string (e.g., "OpenCL 2.1")
126 */
127 String deviceVersion();
128
129 /**
130 * Returns the device driver version.
131 *
132 * @return the driver version string provided by the vendor
133 */
134 String driverVersion();
135
136 /**
137 * Returns the maximum configured clock frequency of the device compute units in MHz.
138 *
139 * @return the maximum clock frequency in megahertz
140 */
141 int maxClockFrequency();
142
143 /**
144 * Returns the set of device types that classify this device.
145 *
146 * @return set of device types (e.g., GPU, CPU, ACCELERATOR)
147 */
148 Set<DeviceType> deviceType();
149
150 /**
151 * Returns the set of built-in kernel names available on this device.
152 *
153 * @return set of built-in kernel names provided by the device
154 */
155 Set<String> builtInKernels();
156
157 /**
158 * Returns the number of parallel compute units on the device.
159 *
160 * <p>Compute units represent the primary parallel processing elements and directly
161 * impact the device's ability to execute work groups concurrently.
162 *
163 * @return the number of parallel compute units available
164 */
165 int maxComputeUnits();
166
167 /**
168 * Returns the maximum number of work-item dimensions supported by the device.
169 *
170 * @return the maximum number of dimensions for work-item indexing
171 */
172 int maxWorkItemDimensions();
173
174 /**
175 * Returns the maximum number of work-items in a work group for kernel execution.
176 *
177 * <p>This limit constrains the local work group size that can be used when
178 * launching kernels on this device. Larger work groups can improve memory
179 * locality and reduce synchronization overhead.
180 *
181 * @return the maximum work group size for kernel execution
182 */
183 long maxWorkGroupSize();
184
185 /**
186 * Returns the maximum number of work-items in each dimension of a work group.
187 *
188 * <p>The array contains the maximum work-item count for each dimension,
189 * providing more granular control over work group configuration than
190 * the overall {@link #maxWorkGroupSize()} limit.
191 *
192 * @return array of maximum work-item counts per dimension
193 */
194 long[] maxWorkItemSizes();
195
196 /**
197 * Returns whether the device supports image objects in kernels.
198 *
199 * @return true if the device supports image processing operations
200 */
201 boolean imageSupport();
202
203 /**
204 * Returns the preferred vector width for float operations.
205 *
206 * <p>This indicates the optimal vector width for floating-point operations
207 * on this device, which can be used to optimize numerical computations
208 * in fitness evaluation kernels.
209 *
210 * @return the preferred vector width for float operations
211 */
212 int preferredVectorWidthFloat();
213
214 /**
215 * Creates a new builder for constructing Device instances.
216 *
217 * @return a new builder for creating device objects
218 */
219 static ImmutableDevice.Builder builder() {
220 return ImmutableDevice.builder();
221 }
222 }