After performing my own benchmarks for loading 61,277,203 lines into memory and shoving values into a Dictionary / ConcurrentDictionary() the results seem to support @dtb's answer above that using the following approach is the fastest:
Parallel.ForEach(File.ReadLines(catalogPath), line =>
{
});
My tests also showed the following:
- File.ReadAllLines() and File.ReadAllLines().AsParallel() appear to run at almost exactly the same speed on a file of this size. Looking at my CPU activity, it appears they both seem to use two out of my 8 cores?
- Reading all the data first using File.ReadAllLines() appears to be much slower than using File.ReadLines() in a Parallel.ForEach() loop.
- I also tried a producer / consumer or MapReduce style pattern where one thread was used to read the data and a second thread was used to process it. This also did not seem to outperform the simple pattern above.
I have included an example of this pattern for reference, since it is not included on this page:
var inputLines = new BlockingCollection<string>();
ConcurrentDictionary<int, int> catalog = new ConcurrentDictionary<int, int>();
var readLines = Task.Factory.StartNew(() =>
{
foreach (var line in File.ReadLines(catalogPath))
inputLines.Add(line);
inputLines.CompleteAdding();
});
var processLines = Task.Factory.StartNew(() =>
{
Parallel.ForEach(inputLines.GetConsumingEnumerable(), line =>
{
string[] lineFields = line.Split('\t');
int genomicId = int.Parse(lineFields[3]);
int taxId = int.Parse(lineFields[0]);
catalog.TryAdd(genomicId, taxId);
});
});
Task.WaitAll(readLines, processLines);
Here are my benchmarks:

I suspect that under certain processing conditions, the producer / consumer pattern might outperform the simple Parallel.ForEach(File.ReadLines()) pattern. However, it did not in this situation.