I'm working on a project (Hardware: RaspberryPI 3B+), which has lots of computation and parallel processing. At present, I'm noticing some sort of lag in the code performance. Therefore, I'm constantly looking for efficient ways to improve my code and its performance.
Currently, I'm using C-language (because I can access and manipulate lower-level drivers easily) and developing my own set of functions, libraries and the drivers, which runs faster than any other pre-defined or readymade libraries or plugins.
Now, instead of the software-based muti-treading (Pthread), I wanted to use the separate cores for performing the corresponding task. So, any suggestion or guideline how I can use the different cores of the RaspberryPI?
Moreover, how can I check the CPU utilization to choose the best core to perform a certain task?
Thanking with regards,
Aatif Shaikh
At the C/C++ level you do not have access to which CPU core will run which thread. Just use the C++ 11 standard threads and let the OS scheduler to decide which thread runs where.
That said, Linux has the taskset tool to check thread affinity and there 's also sched_setaffinity() function.
I am new to openMPI, I have problem understanding the concepts. (I found this pretty helpful)
1- Could anyone breifly explain why we use openMPI? To my understanding, OpenMPI is used to parallelize those sections of the code which can run in parallel.
2- why mpirun duplicates a single program? simply because my laptop is dual core?
3 - what changes in the code I need to apply to make it run correctly? I mean ONE program parallelized on two available cores? not 2 similar threads of the same program.
MPI is primarily of benefit when used in a multiple machine environment, in which you must run multiple processes.
It requires heavy modification of the program.
I am trying to learn threading in C++, and just had a few questions about it (more specifically <thread>.
Let's say the machine this code will run on has 4 cores, should I split up an operation into 4 threads? If I were to create 8 threads instead of 4, would this run slower on a 4 core machine? What if the processor has hyperthreading, should I try and make the threads match the number of physical cores or logical cores?
Should I just not worry about the number of cores a machine has, and try to create as many threads as possible?
I apologize if these questions have been already answered; I've been looking for information about threading with <thread>, which was introduced in c11 so I haven't been able to find too much about it.
The program in question is going to run many independent simulations.
If anybody has any insight about <thread> or just multithreading in general, I would be glad to hear it.
If you are performing pure calculations with no I/O - and those calculations are freestanding and not relying on results from other calculations happening in another thread, the maximum number of such threads should be the number of cores (possibly one or two less if the system is also loaded with other tasks).
If you are doing network I/O or similar, more threads are certainly a possibility.
If you are doing disk-I/O, a single thread reading from the disk is often best, because disk reads from multiple threads leads to moving the read/write head around on the disk, which just makes things slower.
If you're using threads for to make the code simpler, then the number of threads will probably depend on what you are doing.
It also depends on how "freestanding" each thread is. If they need to share data in complex ways, the sharing/waiting for other thread/etc, may well make it slower with more threads.
And as others have said, try to make your framework for this flexible and test different options. Preferably on multiple machines (unless you only have one kind of machine that you will ever run your code on).
There is no such thing as <threads.h>, you mean <thread>, the thread support library introduced in C++11.
The only answer to your question is "test and see". You can make your code flexible enough, so that it can be run by passing an N parameter (where N is the desired number of threads).
If you are CPU-bound, the answer will be very different from the case when you are IO bound.
So, test and see! For your reference, this link can be helpful. And if you are serious, then go ahead and get this book. Multithreading, concurrency, and the like are hairy topics.
Let's say the machine this code will run on has 4 cores, should I split up an operation into 4 threads?
If some portions of your code can be run in parallel, then yes it can be made to go faster, but this is very tricky to do since loading threads and switching data between them takes a ton of time.
If I were to create 8 threads instead of 4, would this run slower on a 4 core machine?
It depends on the context switching it has to do. Sometimes the execution will switch between threads very often and sometimes it will not but this is very difficult to control. It will not in any case run faster than 4 threads doing the same work.
What if the processor has hyperthreading, should I try and make the threads match the number of physical cores or logical cores?
Hyperthreading works nearly the same as having more cores. When you will notice the differences between a real core and an execution core, you will have enough knowledge to work around the caveats.
Should I just not worry about the number of cores a machine has, and try to create as many threads as possible?
NO, threads are hard to manage, avoid them as much as you can.
The program in question is going to run many independent simulations.
You should look into openmp. It is a library in C made to parallelize computation when your program can be split up. Do not confuse parallel with concurrent. Concurrent is simply multiple threads working together while parallel is made specifically to speed up your application. Maybe openmp is overkill for your thing, but it is a good thing to know when you are approaching parallel computing
Don't think of the number of threads you need as in comparison to the machine you're running on. Threading is valuablue any time you have a process that:
A: There is some very slow operation, that the rest of the process need not wait for.
B: Certain functions can run faster than one another and don't need to be executed inline.
C: There is a lot of non-order dependant I/O going on(web servers).
These are just a few of the obvious examples when launching a thread makes sense. The number of threads you launch is therefore more dependant on the number of these scenarios that pop up in your code, than the architecture you expect to run on. In fact unless you're running a process that really really needs to be optimized, it is likely that you can only eek out a few percentage points of additional performance by benchmarking for your architecture in comparison to the number of threads that you launch, and in modern computers this number shouldn't vary much at all.
Let's take the I/O example, as it is the scenario that will see the most benefit. Let's assume that some program needs to interract with 200 users over the network. Network I/O is very very slow. Thousands of times slower than the CPU. If we were to handle each user in turn we would waste thousands of processor cycles just waiting for data to come from the first user. Could we not have been processing information from more than one user at a time? In this case since we have roughly 200 users, and the data that we're waiting for we know to be 1000s of times slower than what we can handle(assuming we have a minimal amount of processing to do on this data), we should launch as many threads as the operating system allows. A web server that takes advantage of threading can serve hundreds of more people per second than one that does not.
Now, let's consider a less I/O intensive example, where say we have several functions that execute in turn, but are independant of one another and some of them might run faster, say because there is disk I/O in one, and no disk I/O in another. In this case, our I/O is still fairly fast, but we will certainly waste processing time waiting for the disk to catch up. As such we can launch a few threads, just to take advantage of our processing power, and minimize wasted cycles. However, if we launch as many threads as the operating system allows we are likely to cuase memory management issues for branch predictors, etc... and launching too many threads in this case is actually sub optimal and can slow the program down. Note that in this, I never mentioned how many cores the machine has! NOt that optimizing for different architectures isn't valuable, but if you optimize for one architecture you are likely very close to optimal for most. Assuming, again, that you're dealing with all reasonably modern processors.
I think most people would say that large scale threading projects are better supported by languages other than c++ (go, scala,cuda). Task parallelism as opposed to data parallelism works better in c++. I would say that you should create as many threads as you have tasks to dole out but if data parallelism is more related to your problem consider maybe using cuda and linking to the rest of your project at a later time
NOTE: if you look at some sort of system monitor you will notice that there are likely far more than 8 threads running, I looked at my computer and it had hundreds of threads running at once so don't worry too much about the overhead. The main reason I choose to mention the other languages is that managing many threads in c++ or c tends to be very difficult and error prone, I did not mention it because the c++ program will run slower(which unless you use cuda it probably won't)
In regards to hyper-threading let me comment on what I have found from experience.
In large dense matrix multiplication hyper-threading actually gives worse performance. For example Eigen and MKL both use OpenMP (at least the way I have used them) and get better results on my system which has four cores and hyper-threading using only four threads instead of eight. Also, in my own GEMM code which gets better performance than Eigen I also get better results using four threads instead of eight.
However, in my Mandelbrot drawing code I get a big performance increase using hyper-threading with OpenMP (eight threads instead of four). The general trend (so far) seems to be that if the code works well using schedule(static) in OpenMP then hyper-threading does not help and may even be worse. If the code works better using schedule(dynamic) then hyper-threading may help.
In other words, my observation so far is that if the run time of each thread can vary a lot hyper-threading can help. If the run time of each thread is constant then it may even make performance worse. But YOU have to test and see for each case.
I have a main processes that will create 4 threads. If i simply run all 4 threads would the kernel utilize all 4 cores or will the program be multithreaded on a single core? if not then how would synchronization be handled on a multicore. I have a 4core intel cpu and my program is in c++
Im running this on a linux in a Virtual machine.
You don't really know.
For one thing, the C++03 Standard doesn't know anything about threads, cores or any of that kind of stuff. So this is all platform-dependant anyway.
But even from a platform point-of-view, you often still don't really know. The operating system schedules threads and jobs. The OS might -- or might not -- give you the means to specify a "processor affinity" for a particular thread, but this typically takes some hoop-jumping-through to utilize.
One of the things you also should keep in mind is that if your goal is to keep each core 100% utilized, you'll often need more than n threads (where n is number of cores). Threads spend a lot of time sleeping, waiting on disk, and generally not doing anything on the core. The exact number of threads you'll need depends on your actual application and platform, but experimentation can help guide you towards fine tuning this.
I developed a program in C++ for research purpose. It takes several days to complete.
Now i executing it on our lab 8core server machine to get results quickly, but i see machine assigns only one processor to my program and it remains at 13% processor usage(even i set process priority at high level and affinity for 8 cores).
(It is a simple object oriented program without any parallelism or multi threading)
How i can get true benefit from the powerful server machine?
Thanks in advance.
Partition your code into chunks you can execute in parallel.
You need to go read about data parallelism
and task parallelism.
Then you can use OpenMP or
MPI
to break up your program.
(It is a simple object oriented program without any parallelism or
multi threading)
How i can get true benefit from the powerful server machine?
By using more threads. No matter how powerful the computer is, it cannot spread a thread across more than one processor. Find independent portions of your program and run them in parallel.
C++0x threads
Boost threads
OpenMP
I personally consider OpenMP a toy. You should probably go with one of the other two.
You have to exploit multiparallelism explicitly by splitting your code into multiple tasks that can be executed independently and then either use thread primitives directly or a higher level parallelization framework, such as OpenMP.
If you don't want to make your program itself use multithreaded libraries or techniques, you might be able to try breaking your work up into several independent chunks. Then run multiple copies of your program...each being assigned to a different chunk, specified by getting different command-line parameters.
As for just generally improving a program's performance...there are profiling tools that can help you speed up or find the bottlenecks in memory usage, I/O, CPU:
https://stackoverflow.com/questions/tagged/c%2b%2b%20profiling
Won't help split your work across cores, but if you can get an 8x speedup in an algorithm that might be able to help more than multithreading would on 8 cores. Just something else to consider.