KernelInfo.java

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package net.bmahe.genetics4j.gpu.opencl.model;
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import org.immutables.value.Value;
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/**
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 * Represents kernel-specific execution characteristics and resource requirements for an OpenCL kernel on a specific
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 * device.
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 * 
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 * <p>KernelInfo encapsulates the device-specific compilation and execution characteristics of an OpenCL kernel,
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 * providing essential information for optimal work group configuration and resource allocation in GPU-accelerated
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 * evolutionary algorithms. This information is determined at kernel compilation time and varies by device.
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 * 
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 * <p>Key kernel characteristics include:
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 * <ul>
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 * <li><strong>Work group constraints</strong>: Maximum and preferred work group sizes for efficient execution</li>
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 * <li><strong>Memory usage</strong>: Local and private memory requirements per work-item</li>
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 * <li><strong>Performance optimization</strong>: Preferred work group size multiples for optimal resource
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 * utilization</li>
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 * <li><strong>Resource validation</strong>: Constraints for validating kernel launch parameters</li>
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 * </ul>
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 * 
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 * <p>Kernel optimization considerations for evolutionary algorithms:
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 * <ul>
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 * <li><strong>Work group sizing</strong>: Configure launch parameters within device-specific limits</li>
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 * <li><strong>Memory allocation</strong>: Ensure sufficient local memory for parallel fitness evaluation</li>
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 * <li><strong>Performance tuning</strong>: Align work group sizes with preferred multiples</li>
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 * <li><strong>Resource planning</strong>: Account for per-work-item memory requirements</li>
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 * </ul>
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 * 
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 * <p>Common usage patterns for kernel configuration:
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 * 
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 * <pre>{@code
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 * // Query kernel information after compilation
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 * KernelInfo kernelInfo = kernelInfoReader.read(deviceId, kernel, "fitness_evaluation");
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 * 
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 * // Configure work group size within device limits
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 * long maxWorkGroupSize = Math.min(kernelInfo.workGroupSize(), device.maxWorkGroupSize());
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 * 
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 * // Optimize for preferred work group size multiple
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 * long preferredMultiple = kernelInfo.preferredWorkGroupSizeMultiple();
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 * long optimalWorkGroupSize = (maxWorkGroupSize / preferredMultiple) * preferredMultiple;
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 * 
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 * // Validate memory requirements for population size
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 * long populationSize = 1000;
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 * long totalLocalMem = kernelInfo.localMemSize() * optimalWorkGroupSize;
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 * long totalPrivateMem = kernelInfo.privateMemSize() * populationSize;
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 * 
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 * // Configure kernel execution with validated parameters
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 * clEnqueueNDRangeKernel(
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 * 		commandQueue,
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 * 			kernel,
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 * 			1,
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 * 			null,
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 * 			new long[] { populationSize },
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 * 			new long[] { optimalWorkGroupSize },
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 * 			0,
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 * 			null,
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 * 			null);
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 * }</pre>
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 * 
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 * <p>Performance optimization workflow:
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 * <ol>
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 * <li><strong>Kernel compilation</strong>: Compile kernel for target device</li>
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 * <li><strong>Information query</strong>: Read kernel-specific execution characteristics</li>
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 * <li><strong>Work group optimization</strong>: Calculate optimal work group size based on preferences</li>
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 * <li><strong>Memory validation</strong>: Ensure memory requirements fit within device limits</li>
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 * <li><strong>Launch configuration</strong>: Configure kernel execution with optimized parameters</li>
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 * </ol>
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 * 
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 * <p>Memory management considerations:
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 * <ul>
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 * <li><strong>Local memory</strong>: Shared among work-items in the same work group</li>
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 * <li><strong>Private memory</strong>: Individual memory per work-item</li>
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 * <li><strong>Total allocation</strong>: Sum of all work-items' memory requirements</li>
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 * <li><strong>Device limits</strong>: Validate against device memory constraints</li>
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 * </ul>
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 * 
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 * <p>Error handling and validation:
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 * <ul>
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 * <li><strong>Work group limits</strong>: Ensure launch parameters don't exceed kernel limits</li>
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 * <li><strong>Memory constraints</strong>: Validate total memory usage against device capabilities</li>
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 * <li><strong>Performance degradation</strong>: Monitor for suboptimal work group configurations</li>
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 * <li><strong>Resource conflicts</strong>: Handle multiple kernels competing for device resources</li>
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 * </ul>
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 * 
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 * @see Device
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 * @see net.bmahe.genetics4j.gpu.opencl.KernelInfoReader
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 * @see net.bmahe.genetics4j.gpu.opencl.KernelInfoUtils
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 */
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@Value.Immutable
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public interface KernelInfo {
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	/**
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	 * Returns the name of the kernel function.
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	 * 
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	 * @return the kernel function name as specified in the OpenCL program
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	 */
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	String name();
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	/**
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	 * Returns the maximum work group size that can be used when executing this kernel on the device.
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	 * 
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	 * <p>This value represents the maximum number of work-items that can be in a work group when executing this specific
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	 * kernel on the target device. It may be smaller than the device's general maximum work group size due to
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	 * kernel-specific resource requirements.
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	 * 
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	 * @return the maximum work group size for this kernel
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	 */
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	long workGroupSize();
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	/**
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	 * Returns the preferred work group size multiple for optimal kernel execution performance.
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	 * 
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	 * <p>For optimal performance, the work group size should be a multiple of this value. This represents the native
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	 * vector width or wavefront size of the device and helps achieve better resource utilization and memory coalescing.
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	 * 
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	 * @return the preferred work group size multiple for performance optimization
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	 */
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	long preferredWorkGroupSizeMultiple();
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	/**
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	 * Returns the amount of local memory in bytes used by this kernel.
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	 * 
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	 * <p>Local memory is shared among all work-items in a work group and includes both statically allocated local
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	 * variables and dynamically allocated local memory passed as kernel arguments. This value is used to validate that
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	 * the total local memory usage doesn't exceed the device's local memory capacity.
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	 * 
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	 * @return the local memory usage in bytes per work group
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	 */
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	long localMemSize();
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	/**
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	 * Returns the minimum amount of private memory in bytes used by each work-item.
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	 * 
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	 * <p>Private memory is individual to each work-item and includes local variables, function call stacks, and other
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	 * per-work-item data. This value helps estimate the total memory footprint when launching kernels with large work
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	 * group sizes.
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	 * 
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	 * @return the private memory usage in bytes per work-item
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	 */
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	long privateMemSize();
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	/**
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	 * Creates a new builder for constructing KernelInfo instances.
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	 * 
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	 * @return a new builder for creating kernel information objects
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	 */
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	static ImmutableKernelInfo.Builder builder() {
149 2 1. builder : replaced return value with null for net/bmahe/genetics4j/gpu/opencl/model/KernelInfo::builder → NO_COVERAGE
2. builder : removed call to net/bmahe/genetics4j/gpu/opencl/model/ImmutableKernelInfo::builder → NO_COVERAGE
		return ImmutableKernelInfo.builder();
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	}
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}

Mutations

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1.1
Location : builder
Killed by : none
replaced return value with null for net/bmahe/genetics4j/gpu/opencl/model/KernelInfo::builder → NO_COVERAGE

2.2
Location : builder
Killed by : none
removed call to net/bmahe/genetics4j/gpu/opencl/model/ImmutableKernelInfo::builder → NO_COVERAGE

Active mutators

Tests examined


Report generated by PIT 1.20.3