static void Main(string[] args)
{
Console.WriteLine("Starting up an MPI program!");
var dataFilePath = args[0];
// Start up MPI
// We can wrap the MPI helper library in a using statement so that we can instantiate the MPI connection
// and have MPI_Finalize automatically called when we are done.
using (var mpi = new Mpi(args))
{
// How big is the cluster, and which node are we?
int worldSize = mpi.GetWorldSize();
int worldRank = mpi.GetWorldRank();
Console.WriteLine($"Rank: {worldRank}, Size: {worldSize}");
// Do a quick allreduce to show that everything works
int sum = 0;
mpi.AllReduce(worldRank, ref sum);
Console.WriteLine($"AllReduce Rank-Sum: {sum}");
// Now let's give a little demo showing how we can cooperatively build a model.
Console.WriteLine("Training a linear model....");
TrainLinearModel(dataFilePath, (uint)worldRank, out float[] biasAndWeights);
var averageBiasAndWeights = new float[biasAndWeights.Length];
mpi.AllReduce(biasAndWeights, averageBiasAndWeights);
for (int i = 0; i < averageBiasAndWeights.Length; i++)
{
averageBiasAndWeights[i] /= (float)worldSize;
}
Console.WriteLine($"Rank-{worldRank}: bias={biasAndWeights[0]} weight[0]={biasAndWeights[1]} | bias={averageBiasAndWeights[0]} weight[0]={averageBiasAndWeights[1]}");
}
}
