//What happens when you press Map Reduce, number represents how many threads.
private void map_reduce_button_Click(object sender, EventArgs e)
{
try
{
resultsTextBox.Text = ""; //Clear current text in text box.
threads = (int)threadsTextBox.Value; //Number of threads.
var usageBytes0 = GC.GetTotalMemory(true); //Memory usage after GC and before processing.
var watch = System.Diagnostics.Stopwatch.StartNew(); //Start a timer for calculations.
Results res = MapReduce.MainMapReduceThread(processingData, threads); //Activate MapReduce
watch.Stop(); //End timer for calculation
var usageBytes = GC.GetTotalMemory(false) - usageBytes0; //Memory usage after processing/before GC. Minus previous memory.
var elapsedMs = watch.ElapsedMilliseconds; //Get timer value
resultsTextBox.Text = DataFunc.ResultsText(res); //Push results into Textbox
resultsTextBox.Text += "Overall run time: " + elapsedMs + " miliseconds. \n";
resultsTextBox.Text += "Current memory usage: " + usageBytes / 1024 + " KB";
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
//Maps the split data for the "computers" - our threads. Each thread has one table.
public static void Map(DataTable dt, int threads)
{
finalRes = new Results[threads];
thr = new Thread[threads];
DataTable[] splitDT = MapReduce.TableSplit(dt, threads);
if (splitDT.Length == threads + 1) //**In a case where taking a bigger chunk isn't worth is (small table) merge the 2 last tables.
{
splitDT[threads - 1].Merge(splitDT[threads]);
}
for (int i = 0; i < threads; i++)
{
int temp = i; //Using Lambda function and return values in threads forces this temp variable.
thr[i] = new Thread(() => finalRes[temp] = ProcessData(splitDT[temp])); //Whatever the thread returns, will return to finalRes.
}
}
