本文整理汇总了C#中MyMemoryBlock.AllocateDevice方法的典型用法代码示例。如果您正苦于以下问题：C# MyMemoryBlock.AllocateDevice方法的具体用法？C# MyMemoryBlock.AllocateDevice怎么用？C# MyMemoryBlock.AllocateDevice使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类MyMemoryBlock的用法示例。
在下文中一共展示了MyMemoryBlock.AllocateDevice方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Init
// Sets up the genetic task
public override void Init(int nGPU)
{
currentGen = 0;
m_weights = 0;
// Load the relevant kernels
m_coeffGenKernel = MyKernelFactory.Instance.Kernel(nGPU, @"Genetic\CosyneGenetics", "generateCoefficients");
m_geneticKernel = MyKernelFactory.Instance.Kernel(nGPU, @"Genetic\CosyneGenetics", "grow");
m_extractKernel = MyKernelFactory.Instance.Kernel(nGPU, @"Genetic\CosyneGenetics", "extractCoeffs");
m_cosineGenKernel = MyKernelFactory.Instance.Kernel(nGPU, @"Genetic\CosyneGenetics", "createCosineMatrix");
m_implantKernel = MyKernelFactory.Instance.Kernel(nGPU, @"Genetic\CosyneGenetics", "implantCoeffs");
// Init the random generator
m_rand = new Random();
// Set up coefficient Generation
m_coeffGenKernel.SetupExecution(Owner.PopulationSize);
// Set up genetic recombination
m_geneticKernel.SetupExecution(Owner.PopulationSize);
// This finds the first nn group in the network. Possibility of getting a list of networks and evolving them all seperately?
List<MyNode> ch = Owner.Owner.Network.Children;
foreach (MyNode n in ch)
{
if (n is MyNeuralNetworkGroup)
{
nn = n as MyNeuralNetworkGroup;
MyLog.INFO.WriteLine("Evolving the layers of node: " + nn.Name);
break;
}
}
if (nn == null)
{
throw new NullReferenceException("There is no top level NeuralNetworkGroup.");
}
// Construct the layerlist which is to be read from and written to
constructLayerList(nn);
// This is how big the weight matrix will be
arr_size = (int)Math.Ceiling(Math.Sqrt(m_weights));
// Get the relevant execution plan
m_executionPlan = Owner.Owner.SimulationHandler.Simulation.ExecutionPlan[0];
#region MemoryBlocks
// Initialise the population
population = new List<MyMemoryBlock<float>>();
outputPop = new List<MyMemoryBlock<float>>();
for (int i = 0; i < Owner.PopulationSize; i++)
{
population.Add(new MyMemoryBlock<float>());
population[i].Owner = Owner;
population[i].Count = arr_size * arr_size;
population[i].AllocateMemory();
outputPop.Add(new MyMemoryBlock<float>());
outputPop[i].Owner = Owner;
outputPop[i].Count = arr_size * arr_size;
outputPop[i].AllocateMemory();
}
// Allocate space to manipulate weight matrices on the device
cudaMatrices = new MyMemoryBlock<float>();
cudaMatrices.Owner = Owner;
cudaMatrices.Count = arr_size * arr_size * Owner.PopulationSize;
cudaMatrices.AllocateDevice();
// Allocate a memory block for the Cosine matrix
multiplier = new MyMemoryBlock<float>();
multiplier.Owner = Owner;
multiplier.Count = arr_size * arr_size;
multiplier.AllocateDevice();
// Fill the cosine Matrices
m_cosineGenKernel.SetupExecution(arr_size);
m_cosineGenKernel.Run(multiplier, arr_size);
// Allocate space needed for chromosomes
chromosomePop = new MyMemoryBlock<float>();
chromosomePop.Owner = Owner;
if (DirectEvolution)
chromosomePop.Count = m_weights * Owner.PopulationSize;
else
chromosomePop.Count = CoefficientsSaved * Owner.PopulationSize;
chromosomePop.AllocateMemory();
// Allocate some space for noise to seed the cuda_rand generator
noise = new MyMemoryBlock<float>();
noise.Owner = Owner;
noise.Count = Owner.PopulationSize;
noise.AllocateMemory();
// Write some noise to the initial array
for (int i = 0; i < Owner.PopulationSize; i++)
{
noise.Host[i] = (float)m_rand.NextDouble() * 100000 + (float)m_rand.NextDouble() * 40;
}
noise.SafeCopyToDevice();
//.........这里部分代码省略.........