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simply put for a hw problem I need to run a bubble sort with 3 million elements. Do this normally. and then sort 4 individual lists (not nearly accurate but is all thats required) of 750000 elements each on a different thread.
I have a class that extends thread that simply prints a line if im running the thread. But I'm not sure how to get it to run a method
class thread extends Thread{
override def run()
{
// Displaying the thread that is running
println("Thread " + Thread.currentThread().getName() +" is running.")
}
}
//this for loop is in an object
for (c <- 1 to 5)
{
var th = new thread()
th.setName(c.toString())
th.start()
}
I am going to stick to answering the question you asked instead of trying to do your homework for you. I hope that is enough to get your started trying things out for yourself.
Recall that class names should be capitalized. That is probably a good thing to remember as your instructor will probably mark you down if you forget that during an exam.
class MyThread extends Thread{
override def run() = {
// Displaying the thread that is running
println("Thread " + Thread.currentThread().getName() +" is running.")
sort()
}
def sort() = {
// my sorting code here
}
}
If your instructor has not restricted you to using Thread only, I would also, similar to Luis Miguel Mejía Suárez, recommend Future instead of Thread. The syntax it uses is cleaner and preferable for how I would do multithreading in a professional setting. However, the choice may not be yours, so if your teacher says use Thread, use Thread.
For a primer on using Futures, see here.
Good luck!
I have a function which receives tasks and puts them into a channel. Every task has ID, some properties and a channel where result will be placed. It looks like this
task.Result = make(chan *TaskResult)
queue <- task
result := <-task.Result
sendReponse(result)
Another goroutine takes a task from the channel, processes it and puts the result into task's channel
task := <-queue
task.Result <- doExpensiveComputation(task)
This code works fine. But now I want to coalesce tasks in the queue. Task processing is a very expensive operation, so I want process all the tasks in the queue with the same IDs once. I see two ways of doing it.
First one is not to put tasks with the same IDs to the queue, so when existing task arrives it will wait for it's copy to complete. Here is pseudo-code
if newTask in queue {
existing := queue.getById(newTask.ID)
existing.waitForComplete()
sendResponse(existing.ProcessingResult)
} else {
queue.enqueue(newTask)
}
So, I can implement it using go channel and map for random access + some synchronization means like mutex. What I don't like about this way is that I have to carry both map and channel around the code and keep their contents synchronized.
The second way is to put all the tasks into queue, but to extract task and all the tasks with the same IDs from the queue when result arrives, then send result to all the tasks. Here is pseudo-code
someTask := queue.dequeue()
result := doExpensiveComputation(someTask)
someTask.Result <- result
moreTasks := queue.getAllWithID(someTask.ID)
for _,theSameTask := range moreTasks {
theSameTask.Result <- result
}
And I have an idea how to implement this using chan + map + mutex in the same way as above.
And here is the question: is there some builtin/existing data structures which I can use for such a problem? Are there another (better) ways of doing this?
If I understand the problem correctly, the simplest solution that comes into my mind is adding a middle layer between task senders (putting into queue) and workers (taking from queue). This, probably routine, would be responsible for storing current tasks (by ID) and broadcasting the results to every matching tasks.
Pseugo code:
go func() {
active := make(map[TaskID][]Task)
for {
select {
case task := <-queue:
tasks := active[task.ID]
// No tasks with such ID, start heavy work
if len(tasks) == 0 {
worker <- task
}
// Save task for the result
active[task.ID] = append(active[task.ID], task)
case r := <-response:
// Broadcast to all tasks
for _, task := range active[r.ID] {
task.Result <- r.Result
}
}
}
}()
No mutexes needed and probably no need to carry anything around either, workers will simply need to put all the results into this middle layer, which is then routing responses correctly. You could even easily add caching here if there's a chance clashing IDs can arrive some time apart.
Edit: I had this dream where the above code caused a deadlock. If you send a lot of requests at once and choke worker channel there's a serious problem – this middle layer routine is stuck on worker <- task waiting for a worker to finish, but all the workers will be probably blocked on send to response channel (because our routine can't collect it). Playable proof.
One could think of adding some buffers into the channels but this is not a proper solution (unless you can design the system in such way the buffer will never fill up). There're a few ways of solving this problem; for example, you can run a separate routine for collecting responses, but then you would need to protect active map with a mutex. Doable. You could also put worker <- task into a select, which would try to send task to a worker, receive new task (if nothing to send) or collect a response. One could take advantage of the fact that nil channel is never ready for communication (ignored by select), so you can alternate between receiving and sending tasks within a single select. Example:
go func() {
var next Task // received task which needs to be passed to a worker
in := queue // incoming channel (new tasks) -- active
var out chan Task // outgoing channel (to workers) -- inactive
for {
select {
case t := <-in:
next = t // store task, so we can pass to worker
in, out = nil, worker // deactivate incoming channel, activate outgoing
case out <- next:
in, out = queue, nil // deactivate outgoing channel, activate incoming
case r := <-response:
collect <- r
}
}
}()
play
I'm using WinCE 7 Visual Studio 2008 and writing a driver code for ALS (MAX44009). I have written the following code for reading the interrupt status register and displaying messages when the interrupt has occurs. But, it works randomly for a few times only. For eg., when I close the sensor with my hand, I get the messages only few times, and then, it doesn't go into the data==1 condition even when it has to interrupt and continues to loop. The threshold timer is 0. The AlsRegRead function does an I2CRead. pAlsDrvInfo is the driver context. ADD_ALS_INT_STATUS is 0. DumpAlsRegistry function will print the content of all the registers except register 0x0.
while(1)
{
AlsRegRead(pAlsDrvInfo, ADD_ALS_INT_STATUS, &data, sizeof(UINT8));
if (data == 1)
{
DumpAlsRegistry(pAlsDrvInfo);
RETAILMSG(1,(L"Interrupt Received...\r\n"));
}
}
Please guide me where I'm making mistake.
I have found the reason behind this. Two issues had been behind this and both of them are equally important.
1) The sensor had been in a partially damaged state.
2) It requires some delay. So, I added Sleep(1000) at the start of the loop.
while(1)
{
Sleep(1000);
AlsRegRead(pAlsDrvInfo, ADD_ALS_INT_STATUS, &data, sizeof(UINT8));
if (data == 1)
{
DumpAlsRegistry(pAlsDrvInfo);
RETAILMSG(1,(L"Interrupt Received...\r\n"));
}
}
Thanks.
I am trying to execute parallel functions on a list of objects using the new C# 4.0 Parallel.ForEach function. This is a very long maintenance process. I would like to make it execute in the order of the list so that I can stop and continue execution at the previous point. How do I do this?
Here is an example. I have a list of objects: a1 to a100. This is the current order:
a1, a51, a2, a52, a3, a53...
I want this order:
a1, a2, a3, a4...
I am OK with some objects being run out of order, but as long as I can find a point in the list where I can say that all objects before this point were run. I read the parallel programming csharp whitepaper and didn't see anything about it. There isn't a setting for this in the ParallelOptions class.
Do something like this:
int current = 0;
object lockCurrent = new object();
Parallel.For(0, list.Count,
new ParallelOptions { MaxDegreeOfParallelism = MaxThreads },
(ii, loopState) => {
// So the way Parallel.For works is that it chunks the task list up with each thread getting a chunk to work on...
// e.g. [1-1,000], [1,001- 2,000], [2,001-3,000] etc...
// We have prioritized our job queue such that more important tasks come first. So we don't want the task list to be
// broken up, we want the task list to be run in roughly the same order we started with. So we ignore tha past in
// loop variable and just increment our own counter.
int thisCurrent = 0;
lock (lockCurrent) {
thisCurrent = current;
current++;
}
dothework(list[thisCurrent]);
});
You can see how when you break out of the parallel for loop you will know the last list item to be executed, assuming you let all threads finish prior to breaking. I'm not a big fan of PLINQ or LINQ. I honestly don't see how writing LINQ/PLINQ leads to maintainable source code or readability.... Parallel.For is a much better solution.
If you use Parallel.Break to terminate the loop then you are guarenteed that all indices below the returned value will have been executed. This is about as close as you can get. The example here uses For but ForEach has similar overloads.
int n = ...
var result = new double[n];
var loopResult = Parallel.For(0, n, (i, loopState) =>
{
if (/* break condition is true */)
{
loopState.Break();
return;
}
result[i] = DoWork(i);
});
if (!loopResult.IsCompleted &&
loopResult.LowestBreakIteration.HasValue)
{
Console.WriteLine("Loop encountered a break at {0}",
loopResult.LowestBreakIteration.Value);
}
In a ForEach loop, an iteration index is generated internally for each element in each partition. Execution takes place out of order but after break you know that all the iterations lower than LowestBreakIteration will have been completed.
Taken from "Parallel Programming with Microsoft .NET" http://parallelpatterns.codeplex.com/
Available on MSDN. See http://msdn.microsoft.com/en-us/library/ff963552.aspx. The section "Breaking out of loops early" covers this scenario.
See also: http://msdn.microsoft.com/en-us/library/dd460721.aspx
For anyone else who comes across this question - if you're looping over an array or list (rather than an IEnumberable ), you can use the overload of Parallel.Foreach that gives the element index to maintain original order too.
string[] MyArray; // array of stuff to do parallel tasks on
string[] ProcessedArray = new string[MyArray.Length];
Parallel.ForEach(MyArray, (ArrayItem,loopstate,ArrayElementIndex) =>
{
string ProcessedArrayItem = TaskToDo(ArrayItem);
ProcessedArray[ArrayElementIndex] = ProcessedArrayItem;
});
As an alternate suggestion, you could record which object have been run and then filter the list when you resume exection to exclude the objects which have already run.
If this needs to be persistent across application restarts, you can store the ID's of the already executed objects (I assume here the objects have some unique identifier).
For anybody looking for a simple solution, I have posted 2 extension methods (one using PLINQ and one using Parallel.ForEach) as part of an answer to the following question:
Ordered PLINQ ForAll
Not sure if question was altered as my comment seems wrong.
Here improved, basically remind that parallel jobs run in out of your control order.
ea printing 10 numbers might result in 1,4,6,7,2,3,9,0.
If you like to stop your program and continue later.
Problems alike this usually endup in batching workloads.
And have some logging of what was done.
Say if you had to check 10.000 numbers for prime or so.
You could loop in batches of size 100, and have a prime log1, log2, log3
log1= 0..99
log2=100..199
Be sure to set some marker to know if a batch job was finished.
Its a general aprouch since the question isnt that exact either.
I want to process some data. I have about 25k items in a Dictionary. IN a foreach loop, I query a database to get results on that item. They're added as value to the Dictionary.
foreach (KeyValuePair<string, Type> pair in allPeople)
{
MySqlCommand comd = new MySqlCommand("SELECT * FROM `logs` WHERE IP = '" + pair.Key + "' GROUP BY src", con);
MySqlDataReader reader2 = comd.ExecuteReader();
Dictionary<string, Dictionary<int, Log>> allViews = new Dictionary<string, Dictionary<int, Log>>();
while (reader2.Read())
{
if (!allViews.ContainsKey(reader2.GetString("src")))
{
allViews.Add(reader2.GetString("src"), reader2.GetInt32("time"));
}
}
reader2.Close();
reader2.Dispose();
allPeople[pair.Key].View = allViews;
}
I was hoping to be able to do this faster by multi-threading. I have 8 threads available, and CPU usage is about 13%. I just don't know if it will work because it's relying on the MySQL server. On the other hand, maybe 8 threads would open 8 DB connections, and so be faster.
Anyway, if multi-threading would help in my case, how? o.O I've never worked with (multiple) threads, so any help would be great :D
MySqlDataReader is stateful - you call Read() on it and it moves to the next row, so each thread needs their own reader, and you need to concoct a query so they get different values. That might not be too hard, as you naturally have many queries with different values of pair.Key.
You also need to either have a temp dictionary per thread, and then merge them, or use a lock to prevent concurrent modification of the dictionary.
The above assumes that MySQL will allow a single connection to perform concurrent queries; otherwise you may need multiple connections too.
First though, I'd see what happens if you only ask the database for the data you need ("SELECT src,time FROMlogsWHERE IP = '" + pair.Key + "' GROUP BY src") and use GetString(0) and GetInt32(1) instead of using the names to look up the src and time; also only get the values once from the result.
I'm also not sure on the logic - you are not ordering the log events by time, so which one is the first returned (and so is stored in the dictionary) could be any of them.
Something like this logic - where each of N threads only operates on the Nth pair, each thread has its own reader, and nothing actually changes allPeople, only the properties of the values in allPeople:
private void RunSubQuery(Dictionary<string, Type> allPeople, MySqlConnection con, int threadNumber, int threadCount)
{
int hoppity = 0; // used to hop over the keys not processed by this thread
foreach (var pair in allPeople)
{
// each of the (threadCount) threads only processes the (threadCount)th key
if ((hoppity % threadCount) == threadNumber)
{
// you may need con per thread, or it might be that you can share con; I don't know
MySqlCommand comd = new MySqlCommand("SELECT src,time FROM `logs` WHERE IP = '" + pair.Key + "' GROUP BY src", con);
using (MySqlDataReader reader = comd.ExecuteReader())
{
var allViews = new Dictionary<string, Dictionary<int, Log>>();
while (reader.Read())
{
string src = reader.GetString(0);
int time = reader.GetInt32(1);
// do whatever to allViews with src and time
}
// no thread will be modifying the same pair.Value, so this is safe
pair.Value.View = allViews;
}
}
++hoppity;
}
}
This isn't tested - I don't have MySQL on this machine, nor do I have your database and the other types you're using. It's also rather procedural (kind of how you would do it in Fortran with OpenMPI) rather than wrapping everything up in task objects.
You could launch threads for this like so:
void RunQuery(Dictionary<string, Type> allPeople, MySqlConnection connection)
{
lock (allPeople)
{
const int threadCount = 8; // the number of threads
// if it takes 18 seconds currently and you're not at .net 4 yet, then you may as well create
// the threads here as any saving of using a pool will not matter against 18 seconds
//
// it could be more efficient to use a pool so that each thread takes a pair off of
// a queue, as doing it this way means that each thread has the same number of pairs to process,
// and some pairs might take longer than others
Thread[] threads = new Thread[threadCount];
for (int threadNumber = 0; threadNumber < threadCount; ++threadNumber)
{
threads[threadNumber] = new Thread(new ThreadStart(() => RunSubQuery(allPeople, connection, threadNumber, threadCount)));
threads[threadNumber].Start();
}
// wait for all threads to finish
for (int threadNumber = 0; threadNumber < threadCount; ++threadNumber)
{
threads[threadNumber].Join();
}
}
}
The extra lock held on allPeople is done so that there is a write barrier after all the threads return; I'm not quite sure if it's needed. Any object would do.
Nothing in this guarantees any performance gain - it might be that the MySQL libraries are single threaded, but the server certainly can handle multiple connections. Measure with various numbers of threads.
If you're using .net 4, then you don't have to mess around creating the threads or skipping the items you aren't working on:
// this time using .net 4 parallel; assumes that connection is thread safe
static void RunQuery(Dictionary<string, Type> allPeople, MySqlConnection connection)
{
Parallel.ForEach(allPeople, pair => RunPairQuery(pair, connection));
}
private static void RunPairQuery(KeyValuePair<string, Type> pair, MySqlConnection connection)
{
MySqlCommand comd = new MySqlCommand("SELECT src,time FROM `logs` WHERE IP = '" + pair.Key + "' GROUP BY src", connection);
using (MySqlDataReader reader = comd.ExecuteReader())
{
var allViews = new Dictionary<string, Dictionary<int, Log>>();
while (reader.Read())
{
string src = reader.GetString(0);
int time = reader.GetInt32(1);
// do whatever to allViews with src and time
}
// no iteration will be modifying the same pair.Value, so this is safe
pair.Value.View = allViews;
}
}
The biggest problem that comes to mind is that you are going to use multithreading to add values to a dictionary, which isn't thread safe.
You'll have to do something like this to make it work, and you might not get that much of a benefit from implementing it this was as it still has to lock the dictionary object to add a value.
Assumptions:
There is a table People in your
database
There are alot of people in
your database
Each database query adds overhead you are doing one db query for each of the people in your database I would suggest it was faster to get all the data back in one query then to make repeated calles
select l.ip,l.time,l.src
from logs l, people p
where l.ip = p.ip
group by l.ip, l.src
Try this with a loop in a single thread, I belive this will be much faster then your existing code.
With in your existing code another thing you can do is to take the creation of the MySqlCommand out of the loop, prepare it in advance and just change the parameter. This should speed up execution of the SQL. see http://dev.mysql.com/doc/refman/5.0/es/connector-net-examples-mysqlcommand.html#connector-net-examples-mysqlcommand-prepare
MySqlCommand comd = new MySqlCommand("SELECT * FROM `logs` WHERE IP = ?key GROUP BY src", con);
comd.prepare();
comd.Parameters.Add("?key","example");
foreach (KeyValuePair<string, Type> pair in allPeople)
{
comd.Parameters[0].Value = pair.Key;
If you are using mutiple threads, each thread will still need there own Command, at lest in MS-SQL this would still be faster even if you recreated and prepared the statment every time, due to the ability for the SQL server to be able to cache the execution plan of a paramertirised statment.
Before you do anything else, find out exactly where the time is being spent. Check the execution plan of the query. The first thing I'd suspect is a missing index on logs.IP.
18 minutes for something like this seems much too long to me. Even if you can cut the execution time in eight by adding more threads (which is unlikely!) you still end up using more than 2 minutes. You could probably read the whole 25k rows into memory in less than five seconds and do the necessary processing in memory...
EDIT: Just to clarify, I'm not advocating actually doing this in memory, just saying that it looks like there's a bigger bottleneck here that can be removed.
I think if you are running this on a multi core machine you could gain benefits from multi threading.
However the way I would approach it is to first look at unblocking the thread you are currently using by making asynchronous database calls. The call backs will execute on background threads, so you will get some multi core benefit there and you won't be blocking threads waiting for the db to come back.
For IO intensive apps like this example sounds like you are likely to see improved throughput depending on what load the db can handle. Assuming the db scales to handle more than one concurrent request you should be good.
Thanks everyone for your help. Currently I am using this
for (int i = 0; i < 8; i++)
{
ThreadPool.QueueUserWorkItem(addDistinctScres, i);
}
ThreadPool to run all the threads. I use the method provided by Pete Kirkham, and I'm creating a new connection per thread.
Times went down to 4 minutes.
Next I'll make something wait for the callback of the threadpool? before performing other functions.
I think the bottleneck now is the MySQL server, because the CPU usage has drops.
#odd parity I thought about that, but the real thing is waaay more than 25k rows. Idk if that'd work.
This sound like the perfect job for map/reduce, i am not a .Net-programmer, but this seems like a reasonable guide:
http://ox.no/posts/minimalistic-mapreduce-in-net-4-0-with-the-new-task-parallel-library-tpl