For example, I have a machine with 2 Cores. Let's say I have a queue of tasks to clear using a program called ninja.
What one of the following would do the job faster which is to clear all the tasks from the queue.
./ninja &
./ninja &
or
running ninja more than my core count
./ninja &
./ninja &
./ninja &
./ninja &
This is easier to think about if you consider have huge numbers of tasks to do. How many you should run depends on what the processes are actually doing.
If all the processes are compute bound, take an equal amount of time, and don't consume the entire memory when they are run in parallel, you want the live process count at any moment to match the core count. That way each core runs for an equal amount of time without any need to context switch to other processors.
If the number of live processes at any moment exceeds the memory availability of your machine, and they are computing, you'll just force a lot of paging and that will hurt your runtime badly.
If the processes are doing lots of I/O and getting stuck waiting for events, (disk data reads, input devices, output-buffer-full blocking), then you want a lot more live processes so that there are processes avaialbe to do work when other processes are blocked.
If the processes vary in memory demand size, you'll want to run the biggest processes first and fill memory with processes that have lesser memory demand. This is often hard to do because you often don't know the memory demand of each process. If you have lots and lots of tasks, you can greedily run one big one and as many little ones as fits, and statistically you'll probably do pretty well.
If you have only a small number of them (you show 4), you can simply try the combinations and see.
I'm new to Elixir, and I'm starting to read through Dave Thomas's excellent Programming Elixir. I was curious how far I could take the concurrency of the "pmap" function, so I iteratively boosted the number of items to square from 1,000 to 10,000,000. Out of curiosity, I watched the output of htop as I did so, usually peaking out with CPU usage similar to that shown below:
After showing the example in the book, Dave says:
And, yes, I just kicked off 1,000 background processes, and I used all the cores and processors on my machine.
My question is, how come on my machine only cores 1, 3, 5, and 7 are lighting up? My guess would be that it has to do with my iex process being only a single OS-level process and OSX is managing the reach of that process. Is that what's going on here? Is there some way to ensure all cores get utilized for performance-intensive tasks?
Great comment by #Thiago Silveira about first line of iex's output. The part [smp:8:8] says how many operating system level processes is Erlang using. You can control this with flag --smp if you want to disable it:
iex --erl '-smp disable'
This will ensure that you have only one system process. You can achieve similar result by leaving symmetric multiprocessing enabled, but setting directly NumberOfShcedulers:NumberOfSchedulersOnline.
iex --erl '+S 1:1'
Each operating system process needs to have its own scheduler for Erlang processes, so you can easily see how many of them do you have currently:
:erlang.system_info(:schedulers_online)
To answer your question about performance. If your processors are not working at full capacity (100%) and non of them is doing nothing (0%) then it is probable that making the load more evenly distributed will not speed things up. Why?
The CPU usage is measured by probing the processor state at many points in time. This states are either "working" or "idle". 82% CPU usage means that you can perform couple of more tasks on this CPU without slowing other tasks.
Erlang schedulers try to be smart and not migrate Erlang processes between cores unless they have to because it requires copying. The migration occurs for example when one of schedulers is idle. It can then borrow a process from others scheduler run queue.
Next thing that may cause such a big discrepancy between odd and even cores is Hyper Threading. On my dual core processor htop shows 4 logical cores. In your case you probably have 4 physical cores and 8 logical because of HT. It might be the case that you are utilizing your physical cores with 100%.
Another thing: pmap needs to calculate result in separate process, but at the end it sends it to the caller which may be a bottleneck. The more you send messages the less CPU utilization you can achieve. You can try for fun giving the processes a task that is really CPU intensive like calculating Ackerman function. You can even calculate how much of your job is the sequential part and how much is parallel using Amdahl's law and measuring execution times for different number of cores.
To sum up: the CPU utilization from screenshot looks really great! You don't have to change anything for more performance-intensive tasks.
Concurrency is not Parallelism
In order to get good parallel performance out of Elixir/BEAM coding you need to have some understanding of how the BEAM scheduler works.
This is a very simplistic model, but the BEAM scheduler gives each process 2000 reductions before it swaps out the process for the next process. Reductions can be thought of as function calls. By default a process runs on the core/scheduler that spawned it. Processes only get moved between schedulers if the queue of outstanding processes builds up on a given scheduler. By default the BEAM runs a scheduling thread on each available core.
What this implies is that in order to get the most use of the processors you need to break up your tasks into large enough pieces of work that will exceed the standard "reduction" slice of work. In general, pmap style parallelism only gives significant speedup when you chunk many items into a single task.
The other thing to be aware of is that some parts of the BEAM use a spin/wait loop when awaiting work and that can skew usage when you use
a tool like htop to examine CPU usage. You'll get a much better understanding of your program's performance by using :observer.
is it possible to calculate the computing time of a process based on the number of operations that it performs and the speed of the CPU in GHz?
For example, I have a for loop that performs a total number of 5*10^14 cycles. If it runs on a 2.4 GHz processor, will the computing time in seconds be: 5*10^14/2.4*10^9 = 208333 s?
If the process runs on 4 cores in parallel, will the time be reduced by four?
Thanks for your help.
No, it is not possible to calculate the computing time based just on the number of operations. First of all, based on your question, it sounds like you are talking about the number of lines of code in some higher-level programming language since you mention a for loop. So depending on the optimization level of your compiler, you could see varying results in computation time depending on what kinds of optimizations are done.
But even if you are talking about assembly language operations, it is still not possible to calculate the computation time based on the number of instructions and CPU speed alone. Some instructions might take multiple CPU cycles. If you have a lot of memory access, you will likely have cache misses and have to load data from disk, which is unpredictable.
Also, if the time that you are concerned about is the actual amount of time that passes between the moment the program begins executing and the time it finishes, you have the additional confounding variable of other processes running on the computer and taking up CPU time. The operating system should be pretty good about context switching during disk reads and other slow operations so that the program isn't stopped in the middle of computation, but you can't count on never losing some computation time because of this.
As far as running on four cores in parallel, a program can't just do that by itself. You need to actually write the program as a parallel program. A for loop is a sequential operation on its own. In order to run four processes on four separate cores, you will need to use the fork system call and have some way of dividing up the work between the four processes. If you divide the work into four processes, the maximum speedup you can have is 4x, but in most cases it is impossible to achieve the theoretical maximum. How close you get depends on how well you are able to balance the work between the four processes and how much overhead is necessary to make sure the parallel processes successfully work together to generate a correct result.
I want to run a batch say 20 CPU intensive comps (basically really long nested for loop) on a machine.
Each of these 20 jobs doesn't share data with the other 19.
If the machine has N cores, should I spin off N-1 of these jobs then? Or N? Or should I just launch all 20, and have Windows figure out how to schedule them?
Unfortunately, there is no simple answer. The only way to know for sure is to implement and then profile your application.
Typically, for maximum throughput, if the jobs are pure CPU, you'd want one per core. Depending on the type of work, this would include one per hyperthread code or just one per "true physical core". (If the work is identical for all 20 jobs, then hyperthreading often slows down the overall work...)
If the jobs have any non-CPU functionaltiy (such as reading a file, waiting on anything, etc), then >1 work item per core tends to be much better. For many situations, this will improve.
Generally, if you aren't sharing data, not blocking on IO, and using lots of CPU and nothing else is running on the box (and probably a few more caveats) using all the CPU's (e.g. N threads) is probably the best idea.
The best choice is probably to make it configurable and profile it and see what happens.
You should use a thread pool of some sort, so it's (reasonably) easy to tune the number of threads without affecting the structure of the program.
Once you've done that, it's a fairly simple matter of testing to find a reasonably optimal number of threads relative to the number of processors available. Chances are that even when/if they look like this should be purely CPU bound, you'll get better efficiency with the number of threads >N, but about the only way to be sure is to test.
Let's say I have a 4-core CPU, and I want to run some process in the minimum amount of time. The process is ideally parallelizable, so I can run chunks of it on an infinite number of threads and each thread takes the same amount of time.
Since I have 4 cores, I don't expect any speedup by running more threads than cores, since a single core is only capable of running a single thread at a given moment. I don't know much about hardware, so this is only a guess.
Is there a benefit to running a parallelizable process on more threads than cores? In other words, will my process finish faster, slower, or in about the same amount of time if I run it using 4000 threads rather than 4 threads?
If your threads don't do I/O, synchronization, etc., and there's nothing else running, 1 thread per core will get you the best performance. However that very likely not the case. Adding more threads usually helps, but after some point, they cause some performance degradation.
Not long ago, I was doing performance testing on a 2 quad-core machine running an ASP.NET application on Mono under a pretty decent load. We played with the minimum and maximum number of threads and in the end we found out that for that particular application in that particular configuration the best throughput was somewhere between 36 and 40 threads. Anything outside those boundaries performed worse. Lesson learned? If I were you, I would test with different number of threads until you find the right number for your application.
One thing for sure: 4k threads will take longer. That's a lot of context switches.
I agree with #Gonzalo's answer. I have a process that doesn't do I/O, and here is what I've found:
Note that all threads work on one array but different ranges (two threads do not access the same index), so the results may differ if they've worked on different arrays.
The 1.86 machine is a macbook air with an SSD. The other mac is an iMac with a normal HDD (I think it's 7200 rpm). The windows machine also has a 7200 rpm HDD.
In this test, the optimal number was equal to the number of cores in the machine.
I know this question is rather old, but things have evolved since 2009.
There are two things to take into account now: the number of cores, and the number of threads that can run within each core.
With Intel processors, the number of threads is defined by the Hyperthreading which is just 2 (when available). But Hyperthreading cuts your execution time by two, even when not using 2 threads! (i.e. 1 pipeline shared between two processes -- this is good when you have more processes, not so good otherwise. More cores are definitively better!) Note that modern CPUs generally have more pipelines to divide the workload, so it's no really divided by two anymore. But Hyperthreading still shares a lot of the CPU units between the two threads (some call those logical CPUs).
On other processors you may have 2, 4, or even 8 threads. So if you have 8 cores each of which support 8 threads, you could have 64 processes running in parallel without context switching.
"No context switching" is obviously not true if you run with a standard operating system which will do context switching for all sorts of other things out of your control. But that's the main idea. Some OSes let you allocate processors so only your application has access/usage of said processor!
From my own experience, if you have a lot of I/O, multiple threads is good. If you have very heavy memory intensive work (read source 1, read source 2, fast computation, write) then having more threads doesn't help. Again, this depends on how much data you read/write simultaneously (i.e. if you use SSE 4.2 and read 256 bits values, that stops all threads in their step... in other words, 1 thread is probably a lot easier to implement and probably nearly as speedy if not actually faster. This will depend on your process & memory architecture, some advanced servers manage separate memory ranges for separate cores so separate threads will be faster assuming your data is properly filed... which is why, on some architectures, 4 processes will run faster than 1 process with 4 threads.)
The answer depends on the complexity of the algorithms used in the program. I came up with a method to calculate the optimal number of threads by making two measurements of processing times Tn and Tm for two arbitrary number of threads ‘n’ and ‘m’. For linear algorithms, the optimal number of threads will be N = sqrt ( (mn(Tm*(n-1) – Tn*(m-1)))/(nTn-mTm) ) .
Please read my article regarding calculations of the optimal number for various algorithms: pavelkazenin.wordpress.com
The actual performance will depend on how much voluntary yielding each thread will do. For example, if the threads do NO I/O at all and use no system services (i.e. they're 100% cpu-bound) then 1 thread per core is the optimal. If the threads do anything that requires waiting, then you'll have to experiment to determine the optimal number of threads. 4000 threads would incur significant scheduling overhead, so that's probably not optimal either.
I thought I'd add another perspective here. The answer depends on whether the question is assuming weak scaling or strong scaling.
From Wikipedia:
Weak scaling: how the solution time varies with the number of processors for a fixed problem size per processor.
Strong scaling: how the solution time varies with the number of processors for a fixed total problem size.
If the question is assuming weak scaling then #Gonzalo's answer suffices. However if the question is assuming strong scaling, there's something more to add. In strong scaling you're assuming a fixed workload size so if you increase the number of threads, the size of the data that each thread needs to work on decreases. On modern CPUs memory accesses are expensive and would be preferable to maintain locality by keeping the data in caches. Therefore, the likely optimal number of threads can be found when the dataset of each thread fits in each core's cache (I'm not going into the details of discussing whether it's L1/L2/L3 cache(s) of the system).
This holds true even when the number of threads exceeds the number of cores. For example assume there's 8 arbitrary unit (or AU) of work in the program which will be executed on a 4 core machine.
Case 1: run with four threads where each thread needs to complete 2AU. Each thread takes 10s to complete (with a lot of cache misses). With four cores the total amount of time will be 10s (10s * 4 threads / 4 cores).
Case 2: run with eight threads where each thread needs to complete 1AU. Each thread takes only 2s (instead of 5s because of the reduced amount of cache misses). With four cores the total amount of time will be 4s (2s * 8 threads / 4 cores).
I've simplified the problem and ignored overheads mentioned in other answers (e.g., context switches) but hope you get the point that it might be beneficial to have more number of threads than the available number of cores, depending on the data size you're dealing with.
4000 threads at one time is pretty high.
The answer is yes and no. If you are doing a lot of blocking I/O in each thread, then yes, you could show significant speedups doing up to probably 3 or 4 threads per logical core.
If you are not doing a lot of blocking things however, then the extra overhead with threading will just make it slower. So use a profiler and see where the bottlenecks are in each possibly parallel piece. If you are doing heavy computations, then more than 1 thread per CPU won't help. If you are doing a lot of memory transfer, it won't help either. If you are doing a lot of I/O though such as for disk access or internet access, then yes multiple threads will help up to a certain extent, or at the least make the application more responsive.
Benchmark.
I'd start ramping up the number of threads for an application, starting at 1, and then go to something like 100, run three-five trials for each number of threads, and build yourself a graph of operation speed vs. number of threads.
You should that the four thread case is optimal, with slight rises in runtime after that, but maybe not. It may be that your application is bandwidth limited, ie, the dataset you're loading into memory is huge, you're getting lots of cache misses, etc, such that 2 threads are optimal.
You can't know until you test.
You will find how many threads you can run on your machine by running htop or ps command that returns number of process on your machine.
You can use man page about 'ps' command.
man ps
If you want to calculate number of all users process, you can use one of these commands:
ps -aux| wc -l
ps -eLf | wc -l
Calculating number of an user process:
ps --User root | wc -l
Also, you can use "htop" [Reference]:
Installing on Ubuntu or Debian:
sudo apt-get install htop
Installing on Redhat or CentOS:
yum install htop
dnf install htop [On Fedora 22+ releases]
If you want to compile htop from source code, you will find it here.
The ideal is 1 thread per core, as long as none of the threads will block.
One case where this may not be true: there are other threads running on the core, in which case more threads may give your program a bigger slice of the execution time.
One example of lots of threads ("thread pool") vs one per core is that of implementing a web-server in Linux or in Windows.
Since sockets are polled in Linux a lot of threads may increase the likelihood of one of them polling the right socket at the right time - but the overall processing cost will be very high.
In Windows the server will be implemented using I/O Completion Ports - IOCPs - which will make the application event driven: if an I/O completes the OS launches a stand-by thread to process it. When the processing has completed (usually with another I/O operation as in a request-response pair) the thread returns to the IOCP port (queue) to wait for the next completion.
If no I/O has completed there is no processing to be done and no thread is launched.
Indeed, Microsoft recommends no more than one thread per core in IOCP implementations. Any I/O may be attached to the IOCP mechanism. IOCs may also be posted by the application, if necessary.
speaking from computation and memory bound point of view (scientific computing) 4000 threads will make application run really slow. Part of the problem is a very high overhead of context switching and most likely very poor memory locality.
But it also depends on your architecture. From where I heard Niagara processors are suppose to be able to handle multiple threads on a single core using some kind of advanced pipelining technique. However I have no experience with those processors.
Hope this makes sense, Check the CPU and Memory utilization and put some threshold value. If the threshold value is crossed,don't allow to create new thread else allow...