Speed Up with multithreading

i have a parse method in my program, which first reads a file from disk then, parses the lines and creats an object for 开发者_Python百科every line. For every file a collection with the objects from the lines is saved afterwards. The files are about 300MB. This takes about 2.5-3 minutes to complete.

My question: Can i expect a significant speed up if i split the tasks up to one thread just reading files from disk, another parsing the lines and a third saving the collections? Or would this maybe slow down the process?

How long is it common for a modern notebook harddisk to read 300MB? I think, the bottleneck is the cpu in my task, because if i execute the method one core of cpu is always at 100% while the disk is idle more then the half time.

greetings, rain

EDIT:

private CANMessage parseLine(String line)
    {
        try
        {
            CANMessage canMsg = new CANMessage();
            int offset = 0;
            int offset_add = 0;

            char[] delimiterChars = { ' ', '\t' };

            string[] elements = line.Split(delimiterChars);

            if (!isMessageLine(ref elements))
            {
                return canMsg = null;
            }

            offset = getPositionOfFirstWord(ref elements);

            canMsg.TimeStamp = Double.Parse(elements[offset]);

            offset += 3;

            offset_add = getOffsetForShortId(ref elements, ref offset);

            canMsg.ID = UInt16.Parse(elements[offset], System.Globalization.NumberStyles.HexNumber);
            offset += 17;   // for signs between identifier and data length number
            canMsg.DataLength = Convert.ToInt16(elements[offset + offset_add]);
            offset += 1;
            parseDataBytes(ref elements, ref offset, ref offset_add, ref canMsg);
            return canMsg;
        }
        catch (Exception exp)
        {
            MessageBox.Show(line);
            MessageBox.Show(exp.Message + "\n\n" + exp.StackTrace);
            return null;
        }   
    }
}

So this is the parse method. It works this way, but maybe you are right and it is inefficient. I have .NET Framwork 4.0 and i am on Windows 7. I have a Core i7 where every core has HypterThreading, so i am only using about 1/8 of the cpu.

EDIT2: I am using Visual Studio 2010 Professional. It looks like the tools for a performance profiling are not available in this version (according to msdn MSDN Beginners Guide to Performance Profiling).

EDIT3: I changed the code now to use threads. It looks now like this:

foreach (string str in checkedListBoxImport.CheckedItems)
{
    toImport.Add(str); 
}

for(int i = 0; i < toImport.Count; i++)
{
    String newString = new String(toImport.ElementAt(i).ToArray());
    Thread t = new Thread(() => importOperation(newString));
    t.Start();
}

While the parsing you saw above is called in the importOperation(...).

With this code it was possible to reduce the time from about 2.5 minutes to "only" 40 seconds. I got some concurrency problems i have to track but at least this is much faster then before.

Thank you for your advice.

It's unlikely that you are going to get consistent metrics for laptop hard disk performance as we have no idea how old your laptop is nor do we know if it is sold state or spinning.

Considering you have already done some basic profiling, I'd wager the CPU really is your bottleneck as it is impossible for a single threaded application to use more than 100% of a single cpu. This is of course ignoring your operating system splitting the process over multiple cores and other oddities. If you were getting 5% CPU usage instead, it'd be most likely were bottle necking at IO.

That said your best bet would be to create a new thread task for each file you are processing and send that to a pooled thread manager. Your thread manager should limit the number of threads you are running to either the number of cores you have available or if memory is an issue (you did say you were generating 300MB files after all) the maximum amount of ram you can use for the process.

Finally, to answer the reason why you don't want to use a separate thread for each operation, consider what you already know about your performance bottlenecks. You are bottle necked on cpu processing and not IO. This means that if you split your application into separate threads your read and write threads would be starved most of the time waiting for your processing thread to finish. Additionally, even if you made them process asynchronously, you have the very real risk of running out of memory as your read thread continues to consume data that your processing thread can't keep up with.

Thus, be careful not to start each thread immediately and let them instead be managed by some form of blocking queue. Otherwise you run the risk of slowing your system to a crawl as you spend more time in context switches than processing. This is of course assuming you don't crash first.

It's unclear how many of these 300MB files you've got. A single 300MB file takes about 5 or 6 seconds to read on my netbook, with a quick test. It does indeed sound like you're CPU-bound.

It's possible that threading will help, although it's likely to complicate things significantly of course. You should also profile your current code - it may well be that you're just parsing inefficiently. (For example, if you're using C# or Java and you're concatenating strings in a loop, that's frequently a performance "gotcha" which can be easily remedied.)

If you do opt for a multi-threaded approach, then to avoid thrashing the disk, you may want to have one thread read each file into memory (one at a time) and then pass that data to a pool of parsing threads. Of course, that assumes you've also got enough memory to do so.

If you could specify the platform and provide your parsing code, we may be able to help you optimize it. At the moment all we can really say is that yes, it sounds like you're CPU bound.

That long for only 300 MB is bad.

There's different things that could be impacting performance as well depending upon the situation, but typically it's reading the hard disk is still likely the biggest bottleneck unless you have something intense going on during the parsing, and which seems the case here because it only takes several seconds to read 300MB from a harddisk (unless it's way bad fragged maybe).

If you have some inefficient algorithm in the parsing, then picking or coming up with a better algorithm would probably be more beneficial. If you absolutely need that algorithm and there's no algorithmic improvement available, it sounds like you might be stuck.

Also, don't try to multithread to read and write at the same time with the multithreading, you'll likely slow things way down to increased seeking.

Given that you think this is a CPU bound task, you should see some overall increase in throughput with separate IO threads (since otherwise your only processing thread would block waiting for IO during disk read/write operations).

Interestingly I had a similar issue recently and did see a significant net improvement by running separate IO threads (and enough calculation threads to load all CPU cores).

You don't state your platform, but I used the Task Parallel Library and a BlockingCollection for my .NET solution and the implementation was almost trivial. MSDN provides a good example.

UPDATE:

As Jon notes, the time spent on IO is probably small compared to the time spent calculating, so while you can expect an improvement, the best use of time may be profiling and improving the calculation itself. Using multiple threads for the calculation will speed up significantly.

Hmm.. 300MB of lines that have to be split up into a lot of CAN message objects - nasty! I suspect the trick might be to thread off the message assembly while avoiding excessive disk-thrashing between the read and write operations.

If I was doing this as a 'fresh' requirement, (and of course, with my 20/20 hindsight, knowing that CPU was going to be the problem), I would probably use just one thread for reading, one for writing the disk and, initially at least, one thread for the message object assembly. Using more than one thread for message assembly means the complication of resequencing the objects after processing to prevent the output file being written out-of-order.

I would define a nice disk-friendly sized chunk-class of lines and message-object array instances, say 1024 of them, and create a pool of chunks at startup, 16 say, and shove them onto a storage queue. This controls and caps memory use, greatly reduces new/dispose/malloc/free, (looks like you have a lot of this at the moment!), improves the efficiency of the disk r/w operations as only large r/w are performed, (except for the last chunk which will be, in general, only partly filled), provides inherent flow-control, (the read thread cannot 'run away' because the pool will run out of chunks and the read thread will block on the pool until the write thread returns some chunks), and inhibits excess context-switching because only large chunks are processed.

The read thread opens the file, gets a chunk from the queue, reads the disk, parses into lines and shoves the lines into the chunk. It then queues the whole chunk to the processing thread and loops around to get another chunk from the pool. Possibly, the read thread could, on start or when idle, be waiting on its own input queue for a message class instance that contains the read/write filespecs. The write filespec could be propagated through a field of the chunks, so supplying the the write thread wilth everything it needs via. the chunks. This makes a nice subsystem to which filespecs can be queued and it will process them all without any further intervention.

The processing thread gets chunks from its input queue and splits the the lines up into the message objects in the chunk and then queues the completed, whole chunks to the write thread.

The write thread writes the message objects to the output file and then requeues the chunk to the storage pool queue for re-use by the read thread.

All the queues should be blocking producer-consumer queues.

One issue with threaded subsystems is completion notification. When the write thread has written the last chunk of a file, it probably needs to do something. I would probably fire an event with the last chunk as a parameter so that the event handler knows which file has been completely written. I would probably somethihng similar with error notifications.

If this is not fast enough, you could try:

1) Ensure that the read and write threads cannot be preemepted in favour of the other during chunk-disking by using a mutex. If your chunks are big enough, this probably won't make much difference.

2) Use more than one processing thread. If you do this, chunks may arrive at the write-thread 'out-of-order'. You would maybe need a local list and perhaps some sort of sequence-number in the chunks to ensure that the disk writes are correctly ordered.

Good luck, whatever design you come up with..

Rgds, Martin