I have a piece of code that needs to perform 18 million tasks. I'm currently using Qt, and more specifically a QThreadPool with QRunnables to multithread the operation on my 8 core CPU.
I have a single QRunnable class that encapsulates and implements the task that must be repeated 18 million times. I'm wondering what the overhead of spooling QRunnables across the QThreadPool is. If there is appreciable overhead, this will accumulate over the 18 million QRunnables that must be pushed through the QThreadPool.
I'm wondering if it makes sense to instead break the 18 million tasks up into larger chunks, where say each QRunnable implements 10,000 tasks instead of one. Would this reduce the overall computation time by removing overhead associated with QRunnable assignments and deallocations in the QThreadPool?
I guess the real question is: is the overhead of starting QRunnables in a QThreadPool high enough to add significant computation time if using 18million QRunnables?
Thanks all!
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I hear everyone talking about how multi-threading can improve performance. I don't believe this, unless there is something I'm missing. If I have an array of 100 elements and traversing it takes 6 seconds. When I divide the work between two threads, the processor would have to go through the same amount of work and therefore time, except that they are working simultaneously but at half the speed. Shouldn't multi threading make it even slower? Since you need additional instructions for dividing the work?
For a simple task of iterating 100 elements multi-threading the task will not provide a performance benefit.
Iterating over 100 billion elements and do processing on each element, then the use of additional CPU's may well help reduce processing time. And more complicated tasks will likely incur interrupts due to I/O for example. When one thread is sleeping waiting for a peripheral to complete I/O (e.g. a disk write, or a key press from the keyboard), other threads can continue their work.
For CPU bound tasks where you have more than one core in your processor you can divide your work on each of your processor core. If you have two cores, split the work on two threads. This way you have to threads working at full speed.
However threads are really expensive to create, so you need a pretty big workload to overcome the initial cost of creating the threads.
You can also use threads to improve appeared performance (or responsiveness) in an interactive application. You run heavy computations on a background thread to avoid blocking UI interactions. Your computations does not complete faster, but your application does not have those "hangs" that make it appear slow and unresponsive.
In general what is the relation between CPU usage and number of threads in a program.
Assumptions:
Multi-core CPU
Threads do the exact same job (assume they fetch identical work items from a queue and process them)
It depends on the nature of the application.
An application that mostly do calculations - a ratio of 1 thread per
core is a reasonable decision, since you don't want to spawn too many threads due to overhead, and you want to take advantage of all your cores.
An application that mostly do IO operations (like http requests) can spawn much more threads then the #cores and still increase efficiency, since the bottleneck is the waiting time per IO request, and you want to gain as much information as possible in each time you need to wait.
That said, the CPU-usage you are going to get is still dependent on many factors (IO, synchronization, non parallel parts in your program).
If you are interested in the speed the application will take - always remember Amdahl's law, which gives you a strict bound on the time (speed-up) your application is going to take, even when having infinite number of working cores.
There is no such general relationship, except for the obvious ones:
an application can't use more CPU time (CPU seconds) than the number of available cores multiplied by the number of (wall clock) seconds that it runs, and
a single thread can't use more than one CPU second per second.
The actual amount of CPU that a multi-threaded application depends mostly on the nature of the application, and the way that you've implemented it:
If the computation performed by each thread does not generate contention with other threads for locks, memory access and so on, then you should be able to approach the theoretical limit of available CPU resources.
Contention is liable to reduce effective CPU usage, sometimes dramatically.
But there are no general formulae that will tell you how much speed-up you can get.
I think there is no relation or not easy one. It depends on the jobs the threads are doing. A program with one thread can consume 100% of CPU and a program with lots of threads can consume less.
If you are looking for an optimized relation between threads and job done, you must study your case, and possibly found an empiric solution.
As the other answers already state, "it depends". In an ideal world, for n cores, you would get a throughput of factor n, given that you do the same job in a separate thread on each core (which already contains a false assumption, since you need to somehow synchronize the threads when they read from the same queue).
Understanding the Disruptor, a Beginner's Guide to Hardcore Concurrency gives some nice examples what you need to consider when parallezing tasks, and also shows some cases where the attempt to parallelize leads to a longer execution time.
I've a multi-threaded application which uses a threadpool of 10 threads. Each thread takes 5 minutes to process input. Is there a law/formula which governs the total time taken to process n inputs?
In other words, is it right to say that every 5 minutes, 10 inputs can be processed, so to process 100 inputs, it will take 50 minutes?
In addition to the computing power (processors/cores) and hardware resource dependencies (hard disk, I/O competition, etc.), the data dependency should also be considered. For example, if the processing of each input includes updating a shared data by all the other threads, which requires locking (mutex), then the total throughput will be less than 10 times, even if it is a multi-core processor with more than 10 cores. The maximum speed-up depends on the proportion of the critical section. If you need a formula, refer to the famous Amdahl's law: en.wikipedia.org/wiki/Amdahl's_law
Not really, you have to consider the total computing power required. If for example a thread takes 5 minutes to do the work, and the processor is completely consumed during that time, then additional threads will not help you. On the other extreme, if the processor utilization is near zero (all of the time is spent waiting for I/O for example), then your proposed calculation would work. So you have to consider the actual resources being used by the computation.
Lets assume we have fixed amount of calculation work, without blocking, sleeping, i/o-waiting. The work can be parallelized very well - it consists of 100M small and independent calculation tasks.
What is faster for 4-core CPU - to run 4 threads or... lets say 50? Why second variant should be slover and how much slover?
As i assume: when you run 4 heavy threads on 4-core CPU without another CPU-consuming processes/threads, scheduler is allowed to not move the threads between cores at all; it has no reason to do that in this situation. Core0 (main CPU) will be responsible for executing interruption handler for hardware timer 250 times per second (basic Linux configuration) and other hardware interruption handlers, but another cores may not feel any worries.
What is the cost of context switching? The time for store and restore CPU registers for different context? What about caches, pipelines and various code-prediction things inside CPU? Can we say that each time we switch context, we hurt caches, pipelines and some code-decoding facilities in CPU? So more threads executing on a single core, less work they can do together in comparison to their serial execution?
Question about caches and another hardware optimization in multithreading environment is the interesting question for me now.
As #Baile mentions in the comments, this is highly application, system, environment-specific.
And as such, I'm not going to take the hard-line approach of mentioning exactly 1 thread for each core. (or 2 threads/core in the case of Hyperthreading)
As an experienced shared-memory programmer, I have seen from my experience that the optimal # of threads (for a 4 core machine) can range anywhere from 1 to 64+.
Now I will enumerate the situations that can cause this range:
Optimal Threads < # of Cores
In certain tasks that are very fine-grained paralleled (such as small FFTs), the overhead of threading is the dominant performance factor. In some cases, it's it not helpful to parallelize at all. In some cases, you get speedup with 2 threads, but backwards scaling at 4 threads.
Another issue is resource contention. Even if you have a highly parallelizable task that can easily split across 4 cores/threads, you may be bottlenecked by memory bandwidth and cache effects. So often, you find that 2 threads will be just as fast as 4 threads. (as if often the case with very large FFTs)
Optimal Threads = # of Cores
This is the optimal case. No need to explain here - one thread per core. Most embarrassingly parallel applications that are not memory or I/O bound fit right here.
Optimal Threads > # of Cores
This is where it gets interesting... very interesting. Have you heard about load-imbalance? How about over-decomposition and work-stealing?
Many parallelizable applications are irregular - meaning that the tasks do not split into sub-tasks of equal size. So if you may end up splitting a large task into 4 unequal sizes, assign them to 4 threads and run them on 4 cores... the result? Poor parallel performance because 1 thread happened to get 10x more work than the other threads.
A common solution here is to over-decompose the task into many sub-tasks. You can either create threads for each one of them (so now you get threads >> cores). Or you can use some sort of task-scheduler with a fixed number of threads. Not all tasks are suited for both, so quite often, the approach of over-decomposing a task to 8 or 16 threads for a 4-core machine gives optimal results.
Although spawning more threads can lead to better load-balance, the overhead builds up. So there's typically an optimal point somewhere. I've seen as high as 64 threads on 4 cores. But as mentioned, it's highly application specific. And you need to experiment.
EDIT : Expanding answer to more directly answer the question...
What is the cost of context switching? The time for store and restore
CPU registers for different context?
This is very dependent on the environment - and is somewhat difficult to measure directly. Short answer: Very Expensive This might be a good read.
What about caches, pipelines and various code-prediction things inside
CPU? Can we say that each time we switch context, we hurt caches,
pipelines and some code-decoding facilities in CPU?
Short answer: Yes When you context switch out, you likely flush your pipeline and mess up all the predictors. Same with caches. The new thread is likely to replace the cache with new data.
There's a catch though. In some applications where the threads share the same data, it's possible that one thread could potentially "warm" the cache for another incoming thread or another thread on a different core sharing the same cache. (Although rare, I've seen this happen before on one of my NUMA machines - superlinear speedup: 17.6x across 16 cores!?!?!)
So more threads executing on a single core, less work they can do
together in comparison to their serial execution?
Depends, depends... Hyperthreading aside, there will definitely be overhead. But I've read a paper where someone used a second thread to prefetch for the main thread... Yes it's crazy...
Creating 50 threads will actually hurt performance, not improve it. It just doesn't make any sense.
Ideally you should make the 4 threads, not more, not less. There will be some overhead because of context switching, but that is unavoidable. The OS/services/other applications threads should too be executed. But nowadays with so powerful and lighting-fast CPUs this is of no concern since those OS threads will only take less that 2 % of the CPU's time. Almost all of them will be in blocked state while your program is running.
You might think that, since performance is of critical importance, you should code those small critical areas in low-level assembly language. Modern programming languages allow this.
But seriously... compilers and, in case of Java, the JVM, will optimize those portions so well that it just isn't worth it (unless you actually want to exercise something like this). Instead of your calculations finishing in 100 seconds, they'll finish in 97 or 98. The question you must ask yourself is: is it worth all those hours of coding and debugging ?
You asked about the time cost of context switching. These days, these are extremely low. Look at modern day dual-core CPUs that run Windows 7 for example. If you start an Apache web server on that machine and a MySQL database server, you will easily go over 800 threads. The machine just doesn't feel it. To see how low this cost is, read here: How to estimate the thread context switching overhead? . To spare you the searching/reading part: context switching can be done hundreds of thousands of times per second.
4 threads are faster if you can program your 40 tasks switching better than Operating System does.
If you can use 4 threads, use them. There's no way 50 will go faster than 4 on a 4-core machine. All you get is more overhead.
Of course, you're describing an ideal non-real-world situation, so whatever you are actually building, you'll need to measure in order to understand how the performance is affected.
There is Hyperthreading technology which can handle more that one thread per CPU, but it is hardly dependent on type of calculation you want to do. Consider using of GPU or very low assembly language to achieve maximum power.
5, 100, 1000?
I guess, "it depends", but on what?
What is common in applications that run as server daemons / services?
What are hard limits?
Given that the machine can handle the overall workload, how do I determine at how many threads the overhead starts to have an impact on performance?
What are important differences between OS's?
What else should be considered?
I'm asking because I would like to employ threads in an application to organize subcomponents of my application that do not share data and are designed to do their work in parallel. As the application would also use thread pools for parallelizing some tasks, I was wondering at what point I should start to think about the number of threads that's going to run in total.
I know the n+1 rule as a guideline for determining the number of threads that simultaneously work on the same task to gain performance. However, I want to use threads like one might use processes in a larger scope, i. e. to organize independent tasks that should not interfere with each other.
In this related question, some people advise to minimise the number of threads because of the added complexity. To me it seems that threads can also help to keep things sorted more orderly and actually reduce interference. Isn't that correct?
I can't answer your question about "how much is many" but I agree that you should not use threads for every task possible.
The optimal amount of threads for performance of application is (n+1), where n is the amount of processors/cores your computer/claster has.
The more your actual thread amount differs from n+1, the less optimal it gets and gets your system resources wasted on thread calculations.
So usually you use 1 thread for the UI, 1 thread for some generic tasks, and (n+1) threads for some huge-calculation tasks.
Actually Ajmastrean is a little out of date. Quoting from his own link
The thread pool has a default size of
250 worker threads per available
processor, and 1000 I/O completion
threads. The number of threads in the
thread pool can be changed by using
the SetMaxThreads method.
But generally I think 25 is really where the law of diminishing returns (and programmers abilities to keep track of what is going on) starts coming into effect. Although Max is right, as long as all of the threads are performing non-blocking calculations n+1 is the optimal number, in the real world most of the threading tasks I perform tend to be done on stuff with some kind of IO.
Also depends on your architecture. E.g. in NVIDIA GPGPU lib CUDA you can put on an 8 thread multiprocessor 512 threads simoultanously. You may ask why assign each of the scalar processors 64 threads? The answer is easy: If the computation is not compute bound but memory IO bound, you can hide the mem latencies by executing other threads. Similar applies to normal CPUs. I can remember that a recommendation for the parallel option for make "-j" is to use approx 1.5 times the number of cores you got. Many of the compiling tasks are heavy IO burden and if a task has to wait for harddisk, mem ... whatever, CPU could work on a different thread.
Next you have to consider, how expensive a task/thread switch is. E.g. it is comes free, while CPU has to perform some work for a context switch. So in general you have to estimate if the penalty for two task switches is longer than the time the thread would block (which depends heavily on your applications).
Microsoft's ThreadPool class limits you to 25 threads per processor. The limit is based on context switching between threads and the memory consumed by each thread. So, that's a good guideline if you're on the Windows platform.