static void Main(string[] args)
{
const int N = 1024 * 1024 * 32;
float[] a = new float[N];
// initialization
Random random = new Random(42);
Parallel.For(0, N, i => a[i] = (float)random.NextDouble());
// hybridizer configuration
cudaDeviceProp prop;
cuda.GetDeviceProperties(out prop, 0);
int gridDimX = 16 * prop.multiProcessorCount;
int blockDimX = 256;
cuda.DeviceSetCacheConfig(cudaFuncCache.cudaFuncCachePreferShared);
HybRunner runner = HybRunner.Cuda().SetDistrib(gridDimX, 1, blockDimX, 1, 1, blockDimX * sizeof(float));
float[] buffMax = new float[1];
float[] buffAdd = new float[1];
var maxReductor = new GridReductor <MaxReductor>();
var addReductor = new GridReductor <AddReductor>();
dynamic wrapped = runner.Wrap(new EntryPoints());
// device reduction
wrapped.ReduceMax(maxReductor, buffMax, a, N);
wrapped.ReduceAdd(addReductor, buffAdd, a, N);
cuda.ERROR_CHECK(cuda.DeviceSynchronize());
// check results
float expectedMax = a.AsParallel().Aggregate((x, y) => Math.Max(x, y));
float expectedAdd = a.AsParallel().Aggregate((x, y) => x + y);
bool hasError = false;
if (buffMax[0] != expectedMax)
{
Console.Error.WriteLine($"MAX Error : {buffMax[0]} != {expectedMax}");
hasError = true;
}
// addition is not associative, so results cannot be exactly the same
// https://en.wikipedia.org/wiki/Associative_property#Nonassociativity_of_floating_point_calculation
if (Math.Abs(buffAdd[0] - expectedAdd) / expectedAdd > 1.0E-5F)
{
Console.Error.WriteLine($"ADD Error : {buffAdd[0]} != {expectedAdd}");
hasError = true;
}
if (hasError)
{
Environment.Exit(1);
}
Console.Out.WriteLine("OK");
}
public static void ReduceMax(GridReductor <MaxReductor> reductor, float[] result, float[] input, int N)
{
reductor.Reduce(result, input, N);
}