Device.java

package net.bmahe.genetics4j.gpu.opencl.model;

import java.util.Set;

import org.immutables.value.Value;
import org.jocl.cl_device_id;

/**
 * Represents an OpenCL compute device with its capabilities and characteristics for GPU-accelerated evolutionary
 * algorithms.
 * 
 * <p>Device encapsulates the properties and capabilities of an OpenCL compute device (GPU, CPU, or accelerator) that
 * can be used for fitness evaluation in evolutionary algorithms. This information is essential for device selection,
 * kernel optimization, and workload configuration to achieve optimal performance.
 * 
 * <p>Key device characteristics include:
 * <ul>
 * <li><strong>Device identification</strong>: Name, vendor, and version information</li>
 * <li><strong>Compute capabilities</strong>: Number of compute units and maximum work group sizes</li>
 * <li><strong>Memory hierarchy</strong>: Global, local, and constant memory sizes and characteristics</li>
 * <li><strong>Processing features</strong>: Vector width preferences, image support, and built-in kernels</li>
 * <li><strong>Performance metrics</strong>: Clock frequency and execution capabilities</li>
 * </ul>
 * 
 * <p>Device selection considerations for evolutionary algorithms:
 * <ul>
 * <li><strong>Device type</strong>: GPU devices typically provide highest parallelism for large populations</li>
 * <li><strong>Compute units</strong>: More compute units allow better utilization of large populations</li>
 * <li><strong>Work group sizes</strong>: Must accommodate the parallelism patterns of fitness kernels</li>
 * <li><strong>Memory capacity</strong>: Must be sufficient for population data and intermediate results</li>
 * <li><strong>Vector operations</strong>: Vector width preferences can optimize numerical computations</li>
 * </ul>
 * 
 * <p>Common device filtering patterns:
 * 
 * <pre>{@code
 * // Select GPU devices with sufficient parallel processing capability
 * Predicate<Device> gpuFilter = device -> device.deviceType()
 * 		.contains(DeviceType.GPU) && device.maxComputeUnits() >= 8;
 * 
 * // Select devices with large work group support for population processing
 * Predicate<Device> workGroupFilter = device -> device.maxWorkGroupSize() >= 256;
 * 
 * // Select devices with high clock frequency for compute-intensive fitness
 * Predicate<Device> performanceFilter = device -> device.maxClockFrequency() >= 1000; // MHz
 * 
 * // Select devices that support floating-point vector operations
 * Predicate<Device> vectorFilter = device -> device.preferredVectorWidthFloat() >= 4;
 * 
 * // Comprehensive filter for evolutionary algorithm suitability
 * Predicate<Device> eaOptimizedFilter = device -> device.deviceType()
 * 		.contains(DeviceType.GPU) && device.maxComputeUnits() >= 4 && device.maxWorkGroupSize() >= 128
 * 		&& device.preferredVectorWidthFloat() >= 2;
 * }</pre>
 * 
 * <p>Performance optimization using device information:
 * <ul>
 * <li><strong>Work group sizing</strong>: Configure kernel work groups based on {@link #maxWorkGroupSize()}</li>
 * <li><strong>Parallel dispatch</strong>: Scale parallelism based on {@link #maxComputeUnits()}</li>
 * <li><strong>Vector operations</strong>: Optimize data layouts for {@link #preferredVectorWidthFloat()}</li>
 * <li><strong>Memory access patterns</strong>: Design kernels considering memory hierarchy characteristics</li>
 * </ul>
 * 
 * <p>Device capability assessment workflow:
 * <ol>
 * <li><strong>Device discovery</strong>: Enumerate devices from selected platforms</li>
 * <li><strong>Capability query</strong>: Read device properties from OpenCL runtime</li>
 * <li><strong>Model creation</strong>: Create device objects with discovered capabilities</li>
 * <li><strong>Filtering</strong>: Apply user-defined predicates to select suitable devices</li>
 * <li><strong>Context creation</strong>: Create OpenCL contexts for selected devices</li>
 * </ol>
 * 
 * <p>Common device types in evolutionary computation:
 * <ul>
 * <li><strong>GPU devices</strong>: Provide massive parallelism for large population fitness evaluation</li>
 * <li><strong>CPU devices</strong>: Offer good sequential performance and large memory capacity</li>
 * <li><strong>Accelerator devices</strong>: Specialized hardware for specific computational patterns</li>
 * <li><strong>Custom devices</strong>: FPGA or other specialized compute devices</li>
 * </ul>
 * 
 * <p>Error handling and compatibility:
 * <ul>
 * <li><strong>Device availability</strong>: Devices may become unavailable during execution</li>
 * <li><strong>Capability validation</strong>: Ensure device supports required kernel features</li>
 * <li><strong>Memory constraints</strong>: Validate device memory is sufficient for population size</li>
 * <li><strong>Work group limits</strong>: Ensure kernels respect device work group size limits</li>
 * </ul>
 * 
 * @see Platform
 * @see DeviceType
 * @see net.bmahe.genetics4j.gpu.spec.GPUEAExecutionContext#deviceFilters()
 * @see net.bmahe.genetics4j.gpu.opencl.DeviceUtils
 */
@Value.Immutable
public interface Device {

	/**
	 * Returns the native OpenCL device identifier.
	 * 
	 * @return the OpenCL device ID for low-level operations
	 */
	cl_device_id deviceId();

	/**
	 * Returns the device name provided by the vendor.
	 * 
	 * @return the human-readable device name (e.g., "GeForce RTX 3080", "Intel Core i7")
	 */
	String name();

	/**
	 * Returns the device vendor name.
	 * 
	 * @return the vendor name (e.g., "NVIDIA Corporation", "Intel", "AMD")
	 */
	String vendor();

	/**
	 * Returns the OpenCL version supported by this device.
	 * 
	 * @return the device OpenCL version string (e.g., "OpenCL 2.1")
	 */
	String deviceVersion();

	/**
	 * Returns the device driver version.
	 * 
	 * @return the driver version string provided by the vendor
	 */
	String driverVersion();

	/**
	 * Returns the maximum configured clock frequency of the device compute units in MHz.
	 * 
	 * @return the maximum clock frequency in megahertz
	 */
	int maxClockFrequency();

	/**
	 * Returns the set of device types that classify this device.
	 * 
	 * @return set of device types (e.g., GPU, CPU, ACCELERATOR)
	 */
	Set<DeviceType> deviceType();

	/**
	 * Returns the set of built-in kernel names available on this device.
	 * 
	 * @return set of built-in kernel names provided by the device
	 */
	Set<String> builtInKernels();

	/**
	 * Returns the number of parallel compute units on the device.
	 * 
	 * <p>Compute units represent the primary parallel processing elements and directly impact the device's ability to
	 * execute work groups concurrently.
	 * 
	 * @return the number of parallel compute units available
	 */
	int maxComputeUnits();

	/**
	 * Returns the maximum number of work-item dimensions supported by the device.
	 * 
	 * @return the maximum number of dimensions for work-item indexing
	 */
	int maxWorkItemDimensions();

	/**
	 * Returns the maximum number of work-items in a work group for kernel execution.
	 * 
	 * <p>This limit constrains the local work group size that can be used when launching kernels on this device. Larger
	 * work groups can improve memory locality and reduce synchronization overhead.
	 * 
	 * @return the maximum work group size for kernel execution
	 */
	long maxWorkGroupSize();

	/**
	 * Returns the maximum number of work-items in each dimension of a work group.
	 * 
	 * <p>The array contains the maximum work-item count for each dimension, providing more granular control over work
	 * group configuration than the overall {@link #maxWorkGroupSize()} limit.
	 * 
	 * @return array of maximum work-item counts per dimension
	 */
	long[] maxWorkItemSizes();

	/**
	 * Returns whether the device supports image objects in kernels.
	 * 
	 * @return true if the device supports image processing operations
	 */
	boolean imageSupport();

	/**
	 * Returns the preferred vector width for float operations.
	 * 
	 * <p>This indicates the optimal vector width for floating-point operations on this device, which can be used to
	 * optimize numerical computations in fitness evaluation kernels.
	 * 
	 * @return the preferred vector width for float operations
	 */
	int preferredVectorWidthFloat();

	/**
	 * Creates a new builder for constructing Device instances.
	 * 
	 * @return a new builder for creating device objects
	 */
	static ImmutableDevice.Builder builder() {
		return ImmutableDevice.builder();
	}
}



Active mutators

CONDITIONALS_BOUNDARY
CONSTRUCTOR_CALLS
EMPTY_RETURNS
EXPERIMENTAL_ARGUMENT_PROPAGATION
EXPERIMENTAL_BIG_DECIMAL
EXPERIMENTAL_BIG_INTEGER
EXPERIMENTAL_MEMBER_VARIABLE
EXPERIMENTAL_NAKED_RECEIVER
EXPERIMENTAL_REMOVE_SWITCH_MUTATOR_[0-99]
EXPERIMENTAL_SWITCH
FALSE_RETURNS
INCREMENTS
INLINE_CONSTS
INVERT_NEGS
MATH
NEGATE_CONDITIONALS
NON_VOID_METHOD_CALLS
NULL_RETURNS
PRIMITIVE_RETURNS
REMOVE_CONDITIONALS_EQUAL_ELSE
REMOVE_CONDITIONALS_EQUAL_IF
REMOVE_CONDITIONALS_ORDER_ELSE
REMOVE_CONDITIONALS_ORDER_IF
REMOVE_INCREMENTS
TRUE_RETURNS
VOID_METHOD_CALLS

Tests examined

Report generated by PIT 1.20.3

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
10		* algorithms.
11		*
12		* <p>Device encapsulates the properties and capabilities of an OpenCL compute device (GPU, CPU, or accelerator) that
13		* can be used for fitness evaluation in evolutionary algorithms. This information is essential for device selection,
14		* kernel optimization, and workload configuration to achieve optimal performance.
15		*
16		* <p>Key device characteristics include:
17		* <ul>
18		* <li><strong>Device identification</strong>: Name, vendor, and version information</li>
19		* <li><strong>Compute capabilities</strong>: Number of compute units and maximum work group sizes</li>
20		* <li><strong>Memory hierarchy</strong>: Global, local, and constant memory sizes and characteristics</li>
21		* <li><strong>Processing features</strong>: Vector width preferences, image support, and built-in kernels</li>
22		* <li><strong>Performance metrics</strong>: Clock frequency and execution capabilities</li>
23		* </ul>
24		*
25		* <p>Device selection considerations for evolutionary algorithms:
26		* <ul>
27		* <li><strong>Device type</strong>: GPU devices typically provide highest parallelism for large populations</li>
28		* <li><strong>Compute units</strong>: More compute units allow better utilization of large populations</li>
29		* <li><strong>Work group sizes</strong>: Must accommodate the parallelism patterns of fitness kernels</li>
30		* <li><strong>Memory capacity</strong>: Must be sufficient for population data and intermediate results</li>
31		* <li><strong>Vector operations</strong>: Vector width preferences can optimize numerical computations</li>
32		* </ul>
33		*
34		* <p>Common device filtering patterns:
35		*
36		* <pre>{@code
37		* // Select GPU devices with sufficient parallel processing capability
38		* Predicate<Device> gpuFilter = device -> device.deviceType()
39		* .contains(DeviceType.GPU) && device.maxComputeUnits() >= 8;
40		*
41		* // Select devices with large work group support for population processing
42		* Predicate<Device> workGroupFilter = device -> device.maxWorkGroupSize() >= 256;
43		*
44		* // Select devices with high clock frequency for compute-intensive fitness
45		* Predicate<Device> performanceFilter = device -> device.maxClockFrequency() >= 1000; // MHz
46		*
47		* // Select devices that support floating-point vector operations
48		* Predicate<Device> vectorFilter = device -> device.preferredVectorWidthFloat() >= 4;
49		*
50		* // Comprehensive filter for evolutionary algorithm suitability
51		* Predicate<Device> eaOptimizedFilter = device -> device.deviceType()
52		* .contains(DeviceType.GPU) && device.maxComputeUnits() >= 4 && device.maxWorkGroupSize() >= 128
53		* && device.preferredVectorWidthFloat() >= 2;
54		* }</pre>
55		*
56		* <p>Performance optimization using device information:
57		* <ul>
58		* <li><strong>Work group sizing</strong>: Configure kernel work groups based on {@link #maxWorkGroupSize()}</li>
59		* <li><strong>Parallel dispatch</strong>: Scale parallelism based on {@link #maxComputeUnits()}</li>
60		* <li><strong>Vector operations</strong>: Optimize data layouts for {@link #preferredVectorWidthFloat()}</li>
61		* <li><strong>Memory access patterns</strong>: Design kernels considering memory hierarchy characteristics</li>
62		* </ul>
63		*
64		* <p>Device capability assessment workflow:
65		* <ol>
66		* <li><strong>Device discovery</strong>: Enumerate devices from selected platforms</li>
67		* <li><strong>Capability query</strong>: Read device properties from OpenCL runtime</li>
68		* <li><strong>Model creation</strong>: Create device objects with discovered capabilities</li>
69		* <li><strong>Filtering</strong>: Apply user-defined predicates to select suitable devices</li>
70		* <li><strong>Context creation</strong>: Create OpenCL contexts for selected devices</li>
71		* </ol>
72		*
73		* <p>Common device types in evolutionary computation:
74		* <ul>
75		* <li><strong>GPU devices</strong>: Provide massive parallelism for large population fitness evaluation</li>
76		* <li><strong>CPU devices</strong>: Offer good sequential performance and large memory capacity</li>
77		* <li><strong>Accelerator devices</strong>: Specialized hardware for specific computational patterns</li>
78		* <li><strong>Custom devices</strong>: FPGA or other specialized compute devices</li>
79		* </ul>
80		*
81		* <p>Error handling and compatibility:
82		* <ul>
83		* <li><strong>Device availability</strong>: Devices may become unavailable during execution</li>
84		* <li><strong>Capability validation</strong>: Ensure device supports required kernel features</li>
85		* <li><strong>Memory constraints</strong>: Validate device memory is sufficient for population size</li>
86		* <li><strong>Work group limits</strong>: Ensure kernels respect device work group size limits</li>
87		* </ul>
88		*
89		* @see Platform
90		* @see DeviceType
91		* @see net.bmahe.genetics4j.gpu.spec.GPUEAExecutionContext#deviceFilters()
92		* @see net.bmahe.genetics4j.gpu.opencl.DeviceUtils
93		*/
94		@Value.Immutable
95		public interface Device {
96
97		/**
98		* Returns the native OpenCL device identifier.
99		*
100		* @return the OpenCL device ID for low-level operations
101		*/
102		cl_device_id deviceId();
103
104		/**
105		* Returns the device name provided by the vendor.
106		*
107		* @return the human-readable device name (e.g., "GeForce RTX 3080", "Intel Core i7")
108		*/
109		String name();
110
111		/**
112		* Returns the device vendor name.
113		*
114		* @return the vendor name (e.g., "NVIDIA Corporation", "Intel", "AMD")
115		*/
116		String vendor();
117
118		/**
119		* Returns the OpenCL version supported by this device.
120		*
121		* @return the device OpenCL version string (e.g., "OpenCL 2.1")
122		*/
123		String deviceVersion();
124
125		/**
126		* Returns the device driver version.
127		*
128		* @return the driver version string provided by the vendor
129		*/
130		String driverVersion();
131
132		/**
133		* Returns the maximum configured clock frequency of the device compute units in MHz.
134		*
135		* @return the maximum clock frequency in megahertz
136		*/
137		int maxClockFrequency();
138
139		/**
140		* Returns the set of device types that classify this device.
141		*
142		* @return set of device types (e.g., GPU, CPU, ACCELERATOR)
143		*/
144		Set<DeviceType> deviceType();
145
146		/**
147		* Returns the set of built-in kernel names available on this device.
148		*
149		* @return set of built-in kernel names provided by the device
150		*/
151		Set<String> builtInKernels();
152
153		/**
154		* Returns the number of parallel compute units on the device.
155		*
156		* <p>Compute units represent the primary parallel processing elements and directly impact the device's ability to
157		* execute work groups concurrently.
158		*
159		* @return the number of parallel compute units available
160		*/
161		int maxComputeUnits();
162
163		/**
164		* Returns the maximum number of work-item dimensions supported by the device.
165		*
166		* @return the maximum number of dimensions for work-item indexing
167		*/
168		int maxWorkItemDimensions();
169
170		/**
171		* Returns the maximum number of work-items in a work group for kernel execution.
172		*
173		* <p>This limit constrains the local work group size that can be used when launching kernels on this device. Larger
174		* work groups can improve memory locality and reduce synchronization overhead.
175		*
176		* @return the maximum work group size for kernel execution
177		*/
178		long maxWorkGroupSize();
179
180		/**
181		* Returns the maximum number of work-items in each dimension of a work group.
182		*
183		* <p>The array contains the maximum work-item count for each dimension, providing more granular control over work
184		* group configuration than the overall {@link #maxWorkGroupSize()} limit.
185		*
186		* @return array of maximum work-item counts per dimension
187		*/
188		long[] maxWorkItemSizes();
189
190		/**
191		* Returns whether the device supports image objects in kernels.
192		*
193		* @return true if the device supports image processing operations
194		*/
195		boolean imageSupport();
196
197		/**
198		* Returns the preferred vector width for float operations.
199		*
200		* <p>This indicates the optimal vector width for floating-point operations on this device, which can be used to
201		* optimize numerical computations in fitness evaluation kernels.
202		*
203		* @return the preferred vector width for float operations
204		*/
205		int preferredVectorWidthFloat();
206
207		/**
208		* Creates a new builder for constructing Device instances.
209		*
210		* @return a new builder for creating device objects
211		*/
212		static ImmutableDevice.Builder builder() {
213	2 1. builder : replaced return value with null for net/bmahe/genetics4j/gpu/opencl/model/Device::builder → NO_COVERAGE 2. builder : removed call to net/bmahe/genetics4j/gpu/opencl/model/ImmutableDevice::builder → NO_COVERAGE	return ImmutableDevice.builder();
214		}
215		}
		Mutations
213		1.1 Location : builder Killed by : none replaced return value with null for net/bmahe/genetics4j/gpu/opencl/model/Device::builder → NO_COVERAGE 2.2 Location : builder Killed by : none removed call to net/bmahe/genetics4j/gpu/opencl/model/ImmutableDevice::builder → NO_COVERAGE

Device.java

Mutations

Active mutators

Tests examined