I'm working on a project that simulates multiple processors handling commands and queuing strings to be printed via one spooler.
There are up to ten processors, each executing a series of jobs that have "compute" and "print" statements. Compute is just a mathematical process to take up time to simulate other work, while print transfers a short string to the spooler to be printed. There is one spooler, with one printer hooked up to the spooler. Each processor will handle a number of jobs before termination, all print statements from a specific job on a specific processor should print together (no interleaving of printing from individual jobs), and the spooler should never be blocked on a process that is computing.
I generally understand how to code this using semaphore and mutex structures, but a statement in the specifications confused me:
Try to maximize the concurrency of your system. (You might consider using
an array of semaphores indexed by processor id.)
Is there a specific advantage I'm missing to using a semaphore for each individual process?
If further clarification is needed, let me know--I tried to describe the problem in a concise way.
EDIT:
Another possibly important piece: each processor has a buffer that can hold up to ten strings for sending to the spooler. Could the sempahores for each process be for waiting when the buffer is full?
EDIT 2:
A job can contain multiple compute and print statements mixed in with each other:
Job 1
Calculate 4
Print Foo
Calculate 2
Print Bar
End Job
Print statements within a job should all be printed in order (Foo and Bar should be printed sequentially without a print from another job/processor in between).
The important information is here:
(no interleaving of printing from individual jobs),
This implies a new Semaphore(1) (if you are using Java).
And
and the spooler should never be blocked on a process that is
computing.
If you had a semaphore that accepts one party this last piece would not be satisfied. An executing processor should not have to wait for another to complete, it can be done in parallel.
You can do this by creating a striped set of semaphores. You have it indexed by the processor ID so that each thread/processor would run without interleaving but without waiting for other processors to complete.
Semaphore[] semaphores = new Semaphore[Number_of_proessors];
//initialize all semaphore indexes
semaphores[Process.id].acquire();
//work
semaphores[Process.id].release();
Related
Hi I am a newbie learning Direct 3D 12.
So far, I understood that Direct 3D 12 is designed for multithreading and I'm trying to make my own simple multithread demo by following the tutorial by braynzarsoft.
https://www.braynzarsoft.net/viewtutorial/q16390-03-initializing-directx-12
Environment is windows, using C++, Visual Studio.
As far as I understand, multithreading in Direct 3D 12 seems, in a nutshell, populating command lists in multiple threads.
If it is right, it seems
1 Swap Chain
1 Command Queue
N Command Lists (N corresponds to number of threads)
N Command Allocators (N corresponds to number of threads)
1 Fence
is enough for a single window program.
I wonder
Q1. When do we need multiple command queues?
Q2. Why do we need multiple fences?
Q3. When do we submit commands multiple times?
Q4. Does GetCPUDescriptorHandleForHeapStart() return value changes?
Q3 comes from here.
https://developer.nvidia.com/sites/default/files/akamai/gameworks/blog/GDC16/GDC16_gthomas_adunn_Practical_DX12.pdf
Purpose of Q4 is I thought of calling the function once and store the value for reuse, it didn't change when I debugged.
Rendering loop in my mind is (based on Game Loop pattern), for example,
Thread waits for fence value (eg. Main thread).
Begin multiple threads to populate command lists.
Wait all threads done with population.
ExecuteCommandLists.
Swap chain present.
Return to 1 in the next loop.
If I am totally misunderstanding, please help.
Q1. When do we need multiple command queues?
Read this https://learn.microsoft.com/en-us/windows/win32/direct3d12/user-mode-heap-synchronization:
Asynchronous and low priority GPU work. This enables concurrent execution of low priority GPU work and atomic operations that enable one GPU thread to consume the results of another unsynchronized thread without blocking.
High priority compute work. With background compute it is possible to interrupt 3D rendering to do a small amount of high priority compute work. The results of this work can be obtained early for additional processing on the CPU.
Background compute work. A separate low priority queue for compute workloads allows an application to utilize spare GPU cycles to perform background computation without negative impact on the primary rendering (or other) tasks.
Streaming and uploading data. A separate copy queue replaces the D3D11 concepts of initial data and updating resources. Although the application is responsible for more details in the Direct3D 12 model, this responsibility comes with power. The application can control how much system memory is devoted to buffering upload data. The app can choose when and how (CPU vs GPU, blocking vs non-blocking) to synchronize, and can track progress and control the amount of queued work.
Increased parallelism. Applications can use deeper queues for background workloads (e.g. video decode) when they have separate queues for foreground work.
Q2. Why do we need multiple fences?
All gpu work is asynchronous. So you can think of fences as low level tools to achieve the same result as futures/coroutines. You can check if the work has been completed, wait for work to complete or set an event on completion. You need a fence whenever you need to guarantee a resource holds the output of work (when resource barriers are insufficient).
Q4. Does GetCPUDescriptorHandleForHeapStart() return value changes?
No it doesn't.
store the value for reuse, it didn't change when I debugged.
The direct3d12 samples do this, you should know them intimately if you want to become proficient.
Rendering loop in my mind is (based on Game Loop pattern), for example,
That sounds okay, but I urge you to look at the direct3d12 samples and steal the patterns (and the code) they use there.
I am writing my first threaded application for an industrial machine that has a very fast line speed. I am using the MFC for the UI and once the user pushes the "Start" machine button, I need to be simultaneously executing three operations. I need to collect data, process it and output results very quickly as well as checking to see if the user has turned the machine "off". When I say very quickly, I expect the analyze portion of the execution to take the longest and it needs to happen in well under a second. I am mostly concerned about overhead elimination associated with threads. What is the fastest way to implement the loop below:
void Scanner(CString& m_StartStop) {
std::thread Collect(CollectData);
while (m_StartStop == "Start") {
Collect.join();
std::thread Analyze(AnalyzeData);
std::thread Collect(CollectData);
Analyze.join();
std::thread Send(SendData);
Send.join();
}
}
I realize this sample is likely way off base, but hopefully it gets the point across. Should I be creating three threads and suspending them instead of creating and joining them over and over? Also, I am a little unclear if the UI needs its own thread since the user needs to able to pause or stop the line at anytime.
In case anyone is wondering why this needs to be threaded as opposed to sequential, the answer is that the line speed of the machine will cause the need to be collecting data for the second part while the first part is being analyzed. Every 1 second equates to 3 ft of linear part movement down this machine.
Think about functionnal problem before thinking about implementation.
So we have a continuous flow of data that need to be collected, analyzed and sent elsewhere, with a supervision point to be able to stop of pause the process.
collection should be limited by the input flow
analyze should only be cpu limited
sending should be io bound
You just need to make sure that the slowest part must be collection.
That is a correct use case for threads. Implementation could use:
a pool of input buffers that would be filled by collect task and used by analyze task
one thread that continuously:
controls if it should exit (a dedicated variable)
takes an input object from the pool
fills it with data
passes it to analyze task
one thread that continuously
waits for the first of an input object from collect task and a request to exit
analyzes the object and prepares output
send the output
Optionnaly, you can have a separate thread for processing the output. In that case, the last lines becomes
passes an output object to the sending task
and we must add:
one thread that continuously
waits for the first of an output object from analze task and a request to exit
send the output
And you must provide a way to signal the request for pause or exit, either with a completely external program and a signalisation mechanism, or a GUI thread
Any threads you need should already be running, waiting for work. You should not create or join threads.
If job A has to finish before job B can start, the completion of job A should trigger the start of job B. That is, when the thread doing job A finished doing job A, it should either do job B itself or trigger the dispatch of job B. There shouldn't need to be some other thread that's waiting for job A to finish so that it can start job B.
New description of the problem:
I currently run our new data acquisition software in a test environment. The software has two main threads. One contains a fast loop which communicates with the hardware and pushes the data into a dual buffer. Every few seconds, this loop freezes for 200 ms. I did several tests but none of them let me figure out what the software is waiting for. Since the software is rather complex and the test environment could interfere too with the software, I need a tool/technique to test what the recorder thread is waiting for while it is blocked for 200 ms. What tool would be useful to achieve this?
Original question:
In our data acquisition software, we have two threads that provide the main functionality. One thread is responsible for collecting the data from the different sensors and a second thread saves the data to disc in big blocks. The data is collected in a double buffer. It typically contains 100000 bytes per item and collects up to 300 items per second. One buffer is used to write to in the data collection thread and one buffer is used to read the data and save it to disc in the second thread. If all the data has been read, the buffers are switched. The switch of the buffers seems to be a major performance problem. Each time the buffer switches, the data collection thread blocks for about 200 ms, which is far too long. However, it happens once in a while, that the switching is much faster, taking nearly no time at all. (Test PC: Windows 7 64 bit, i5-4570 CPU #3.2 GHz (4 cores), 16 GB DDR3 (800 MHz)).
My guess is, that the performance problem is linked to the data being exchanged between cores. Only if the threads run on the same core by chance, the exchange would be much faster. I thought about setting the thread affinity mask in a way to force both threads to run on the same core, but this also means, that I lose real parallelism. Another idea was to let the buffers collect more data before switching, but this dramatically reduces the update frequency of the data display, since it has to wait for the buffer to switch before it can access the new data.
My question is: Is there a technique to move data from one thread to another which does not disturb the collection thread?
Edit: The double buffer is implemented as two std::vectors which are used as ring buffers. A bool (int) variable is used to tell which buffer is the active write buffer. Each time the double buffer is accessed, the bool value is checked to know which vector should be used. Switching the buffers in the double buffer just means toggling this bool value. Of course during the toggling all reading and writing is blocked by a mutex. I don't think that this mutex could possibly be blocking for 200 ms. By the way, the 200 ms are very reproducible for each switch event.
Locking and releasing a mutex just to switch one bool variable will not take 200ms.
Main problem is probably that two threads are blocking each other in some way.
This kind of blocking is called lock contention. Basically this occurs whenever one process or thread attempts to acquire a lock held by another process or thread. Instead parallelism you have two thread waiting for each other to finish their part of work, having similar effect as in single threaded approach.
For further reading I recommend this article for a read, which describes lock contention with more detailed level.
Since you are running on windows maybe you use visual studio? if yes I would resort to VS profiler which is quite good (IMHO) in such cases, once you don't need to check data/instruction caches (then the Intel's vTune is a natural choice). From my experience VS is good enough to catch contention problems as well as CPU bottlenecks. you can run it directly from VS or as standalone tool. you don't need the VS installed on your test machine you can just copy the tool and run it locally.
VSPerfCmd.exe /start:SAMPLE /attach:12345 /output:samples - attach to process 12345 and gather CPU sampling info
VSPerfCmd.exe /detach:12345 - detach from process
VSPerfCmd.exe /shutdown - shutdown the profiler, the samples.vsp is written (see first line)
then you can open the file and inspect it in visual studio. if you don't see anything making your CPU busy switch to contention profiling - just change the "start" argument from "SAMPLE" to "CONCURRENCY"
The tool is located under %YourVSInstallDir%\Team Tools\Performance Tools\, AFAIR it is available from VS2010
Good luck
After discussing the problem in the chat, it turned out that the Windows Performance Analyser is a suitable tool to use. The software is part of the Windows SDK and can be opened using the command wprui in a command window. (Alois Kraus posted this useful link: http://geekswithblogs.net/akraus1/archive/2014/04/30/156156.aspx in the chat). The following steps revealed what the software had been waiting on:
Record information with the WPR using the default settings and load the saved file in the WPA.
Identify the relevant thread. In this case, the recording thread and the saving thread obviously had the highest CPU load. The saving thread could be easily identified. Since it saves data to disc, it is the one that with file access. (Look at Memory->Hard Faults)
Check out Computation->CPU usage (Precise) and select Utilization by Process, Thread. Select the process you are analysing. Best display the columns in the order: NewProcess, ReadyingProcess, ReadyingThreadId, NewThreadID, [yellow bar], Ready (µs) sum, Wait(µs) sum, Count...
Under ReadyingProcess, I looked for the process with the largest Wait (µs) since I expected this one to be responsible for the delays.
Under ReadyingThreadID I checked each line referring to the thread with the delays in the NewThreadId column. After a short search, I found a thread that showed frequent Waits of about 100 ms, which always showed up as a pair. In the column ReadyingThreadID, I was able to read the id of the thread the recording loop was waiting for.
According to its CPU usage, this thread did basically nothing. In our special case, this led me to the assumption that the serial port io command could cause this wait. After deactivating them, the delay was gone. The important discovery was that the 200 ms delay was in fact composed of two 100 ms delays.
Further analysis showed that the fetch data command via the virtual serial port pair gets sometimes lost. This might be linked to very high CPU load in the data saving and compression loop. If the fetch command gets lost, no data is received and the first as well as the second attempt to receive the data timed out with their 100 ms timeout time.
(the problem is embarrassingly parallel)
Consider an array of 12 cells:
|__|__|__|__|__|__|__|__|__|__|__|__|
and four (4) CPUs.
Naively, I would run 4 parallel jobs and feeding 3 cells to each CPU.
|__|__|__|__|__|__|__|__|__|__|__|__|
=========|========|========|========|
1 CPU 2 CPU 3 CPU 4 CPU
BUT, it appears, that each cell has different evaluation time, some cells are evaluated very quickly, and some are not.
So, instead of wasting "relaxed CPU", I think to feed EACH cell to EACH CPU at time and continue until the entire job is done.
Namely:
at the beginning:
|____|____|____|____|____|____|____|____|____|____|____|____|
1cpu 2cpu 3cpu 4cpu
if, 2cpu finished his job at cell "2", it can jump to the first empty cell "5" and continue working:
|____|done|____|____|____|____|____|____|____|____|____|____|
1cpu 3cpu 4cpu 2cpu
|-------------->
if 1cpu finished, it can take sixth cell:
|done|done|____|____|____|____|____|____|____|____|____|____|
3cpu 4cpu 2cpu 1cpu
|------------------------>
and so on, until the full array is done.
QUESTION:
I do not know a priori which cell is "quick" and which cell is "slow", so I cannot spread cpus according to the load (more cpus to slow, less to quick).
How one can implement such algorithm for dynamic evaluation with MPI?
Thanks!!!!!
UPDATE
I use a very simple approach, how to divide the entire job into chunks, with IO-MPI:
given: array[NNN] and nprocs - number of available working units:
for (int i=0;i<NNN/nprocs;++i)
{
do_what_I_need(start+i);
}
MPI_File_write(...);
where "start" corresponds to particular rank number. In simple words, I divide the entire NNN array into fixed size chunk according to the number of available CPU and each CPU performs its chunk, writes the result to (common) output and relaxes.
IS IT POSSIBLE to change the code (Not to completely re-write in terms of Master/Slave paradigm) in such a way, that each CPU will get only ONE iteration (and not NNN/nprocs) and after it completes its job and writes its part to the file, will Continue to the next cell and not to relax.
Thanks!
There is a well known parallel programming pattern, known under many names, some of which are: bag of tasks, master / worker, task farm, work pool, etc. The idea is to have a single master process, which distributes cells to the other processes (workers). Each worker runs an infinite loop in which it waits for a message from the master, computes something and then returns the result. The loop is terminated by having the master send a message with a special tag. The wildcard tag value MPI_ANY_TAG can be used by the worker to receive messages with different tags.
The master is more complex. It also runs a loop but until all cells have been processed. Initially it sends each worker a cell and then starts a loop. In this loop it receives a message from any worker using the wildcard source value of MPI_ANY_SOURCE and if there are more cells to be processed, sends one of them to the same worker that have returned the result. Otherwise it sends a message with a tag set to the termination value.
There are many many many readily available implementations of this model on the Internet and even some on Stack Overflow (for example this one). Mind that this scheme requires one additional MPI process that often does very little work. If this is unacceptable, one can run a worker loop in a separate thread.
You want to implement a kind of client-server architecture where you have workers asking the server for work whenever they are out of work.
Depending on the size of the chunks and the speed of your communication between workers and server, you may want to adjust the size of the chunks sent to workers.
To answer your updated question:
Under the master/slave (or worker pool if that's how you prefer it to be labelled) model, you will basically need a task scheduler. The master should have information about what work has been done and what still needs to be done. The master will give each process some work to be done, then sit and wait until a process completes (using nonblocking receives and a wait_all). Once a process completes, have it send the data to the master then wait for the master to respond with more work. Continue this until the work is done.
I'm working on an algorithm that does prettymuch the same operation a bunch of times. Since the operation consists of some linear algebra(BLAS), I thourght I would try using the GPU for this.
I've writen my kernel and started pushing kernels on the command queue. Since I don't wanna wait after each call I figures I would try daisy-chaining my calls with events and just start pushing these on the queue.
call kernel1(return event1)
call kernel2(wait for event 1, return event 2)
...
call kernel1000000(vait for event 999999)
Now my question is, does all of this get pushed to the graphic chip of does the driver store the queue? It there a bound on the number of event I can use, or to the length of the command queue, I've looked around but I've not been able to find this.
I'm using atMonitor to check the utilization of my gpu' and its pretty hard to push it above 20%, could this simply be becaurse I'm not able to push the calls out there fast enough? My data is already stored on the GPU and all I'm passing out there is the actual calls.
First, you shouldn't wait for an event from a previous kernel unless the next kernel has data dependencies on that previous kernel. Device utilization (normally) depends on there always being something ready-to-go in the queue. Only wait for an event when you need to wait for an event.
"does all of this get pushed to the graphic chip of does the driver store the queue?"
That's implementation-defined. Remember, OpenCL works on more than just GPUs! In terms of the CUDA-style device/host dichotomy, you should probably consider command queue operations (for most implementations) on the "host."
Try queuing up multiple kernels calls without waits in-between them. Also, make sure you are a using an optimal work group size. If you do both of those, you should be able to max out your device.
Unfortunately i don't know the answers to all of your questions and you've got me wondering about the same things now too but i can say that i doubt the OpenCL queue will ever become full since you GPU should finish executing the last queued command before at least 20 commands are submitted. This is only true though if your GPU has a "watchdog" because that would stop ridiculously long kernels (i think 5 seconds or more) from executing.