I know MP is the manage of multiple processes within multiple processors, but is there any difference between that and SMP? Is it that in SMP you can execute multiple threads from the same process simultaniously, and MP you can only have one process occupy one processor?
example of what i think the differences are:
SMP
P1 has 3 threads: P1T1, P1T2 and P1T3
P2 has 2 threads: P2T1 and P2T2.
on a computer with 3 processors, you can assign P1T1 to processor 1, P1T2 to processor 2 and P1T3 to processor 3 simultaneously if all are available, or P2T1 to processor 1, and P2T2 to processor 2 and P1T1 to processor 3.
MP
P1 has 3 threads: P1T1, P1T2 and P1T3
P2 has 2 threads: P2T1 and P2T2.
on a computer with 3 processors, you can assign P1T1 to processor 1 and -
P1 has 3 threads: P1T1, P1T2 and P1T3
P2 has 2 threads: P2T1 and P2T2.
on a computer with 3 processors, you can assign P1T1 to processor 1, but P1T2 and P1T3 have to wait until P1T1 is done in order to execute, while P2T1 can go to processor 2, and, again, P2T2 would have to wait until P2T1 is done executing before it ca execute.
Does this make sence? If it does, am I on the right track? Thx, I got an OS exam today and I'm studying. Thank you for any help you guys can provide.
Also, how are threads scheaduled? I know that is a very broad question, but is there any specific way? or is it based on the scheaduling the system has implemented? I know there is round robin scheaduling, higher priority, time slicing, time sharing, shortest amount of time... If this question doesnt make sence, no worries, I appreciate any help you guys can give.
Actually,SMP is a division of MP. SO, the question of difference doesn't make much sense. Any MP can be either of the two---either Symmetric MP or Parallel(Asymmetric) MP.
In your case,examples can't be taken into account to differentiate these two because of the mentioned above reason.
Also, in SMP, the two CPU's or processors reside on different machines or are separate processors or are different cores which work on the same shared memory to achieve the work done!
As mentioned in Wikipedia about Symmetric Multiprocessing :-
Symmetric multiprocessing (SMP) involves a symmetric multiprocessor
system hardware and software architecture where two or more identical
processors connect to a single, shared main memory, have full access
to all I/O devices, and are controlled by a single operating system
instance that treats all processors equally, reserving none for
special purposes. Most multiprocessor systems today use an SMP
architecture. In the case of multi-core processors, the SMP
architecture applies to the cores, treating them as separate
processors.
In ye olde days of multiprocessing systems (e.g., VAX 11/782) one processor was the master and the remainder were slaves. The master processor assigned the tasks to the other processors when it was idle and did work otherwise.
In an SMP system, god created processors equal. They use locking mechanism to select tasks.
Related
I model on an ancient PC and recently got some lab funds for a new modeling computer. The choice of processor confounds me. For optimal AnyLogic simulation modeling, should I focus on maxing out the single-core speed or max the number of processor cores? Also, would a high-end graphics card help? I have heard from my engineering colleagues that for certain modeling tools that they do help with the work load. Any advice helps. Thanks.
This is what AnyLogic answered when I asked for the perfect computer to buy:
The recommended platform for AnyLogic is a powerful PC/laptop running
64-bit operating system (Windows preferable), plus CPU with multiple
cores like i7 and at least 8 Gb of RAM.
In general, faster CPU (3GHz or more recommended) means faster single
run execution. More cores means faster execution of the experiments
running the model multiple times in parallel (optimization, parameter
variation, monte carlo, etc.). Also, pedestrians and transporters
benefit from many cores (even single run, since the algorithm causing
movement of pedestrians and transporters uses all available cores).
For the time being, AnyLogic doesn't support GPU processing. RAM is
crucial when you have a lot of agents and many parallel runs (e.g. if
single run takes 1GB, then 8 parallel runs will take 8 Gb). For
working with GIS map, it may be needed to have a good connection to
the Internet. For example, if model requests a lot of routes from
online route provider.
On average, a middle-end PC/laptop in sufficient for most of the
models, high-end PC or server/instance will be useful in case of
really heavy models.
Just to add to Felipe's reply: graphic card is completely irrelevant, AnyLogic does not support outsourcing computations to their tensor cores.
Focus on decent processor speed and 8-12 cores as well as at least 16 GB of RAM and (crucial!!) an SSD harddrive. Good to go :)
Oh, and you may want to use Windows. Linux and Mac OS seem to feature more problems/bugs in AnyLogic than Windows
I have a question:
In which scenario FIFO (First In First Out) scheduling is possible in multiprogramming when we have only 1 processor?
Multiprogramming concept is the ability of the operating system to have multiple programs in memory.
Multiprogramming actually mean switching between the processes or interleaving the I/O time and CPU time of processes.
So it is independent of number of processors.(i.e. Even if there is only one processor it may be working on multiple programs.)
Cal a multicore architecture with 10 computing cores: 2 processor cores and 8 coprocessors. Each processor core can deliver 2.0 GFlops, while each coprocessor can deliver 1.0 GFlops. All computing cores can perform calculation simultaneously. Any instruction can execute in either processor or coprocessor cores unless there are any explicit restrictions.
If 70% of dynamic instructions in an application are parallelizable, what is the maximum average performance (Flops) you can get in the optimal situation? Please note that the remaining 30% instructions can be executed only after the execution of the parallel 70% is over.
Consider another application where all the dynamic instructions can be partitioned into 6 groups (A, B, C, D, E, F) with the following dependency. For example, A --> C implies that all the instructions in A need to be completed before starting the execution of instructions in C. Each of the first four groups (A, B, C and D) contains 20% of the dynamic instructions whereas each of the remaining two groups (E and F) contains 10% of the dynamic instructions. All the instructions in each group must be executed sequentially on the same processor or coprocessor core. How to schedule them on the multicore architecture to achieve the best possible performance? What is the maximum average performance (Flops) now?
A(20%) --> C(20%) -->
E(10%)-->F(10%)
B(20%) --> d(20%) -->
For the first part, you need to use Amdahl's Law, which is:
max speed-up = 1/(1-p+p/n)
where p is the parallelizable part. n is the improvement factor in executing the parallel portion.
(Note that the Amdahl's Law formula can be used for first order estimates on other types of changes. E.g., given a factor of N reduction in ALU energy use and P fraction of energy used by the ALU, one can find the improvement in total energy use.)
In your case, since the serial portion would be executed on the higher performance (2 GFLOPS) processor core, n is 6 ([8 coprocessor cores * 1 GFLOPS/core + 2 processor cores * 2 GFLOPS/core]/ 2 GFLOPS/processor core).
A quick calculation shows the max speed-up you can get is 2.4 related to 1 processor core. The maximum FLOPS would therefore be the speed-up times the speed if the whole program was executed serially on one processor core, i.e., 2.4 * 2 GFLOPS = 4.8 GFLOPS.
For the second part, note that initially there are two independent instruction streams: A -> C and B -> C. Since the system has two processor cores, both can be executed in parallel on the higher performance processor cores. Furthermore, both have the same amount of work (40% of total for each stream), so one the same performance core they will complete at the same time.
Since E depends on results from both C and D, it must be started after both finish. E and F would execute on a processor core (which core is arbitrary since E must wait for the tasks running on both processor cores to complete).
As you can see 80% of the program (40% for A+C; 40% for B+D) can be parallelized by a factor of 2 and 20% of the program (E+F) is serial. You can then just plug the numbers into the Amdahl's Law formula (p=0.8, n=2).
I'm running memory-intensive parallel computations in MATLAB on a 64-core NUMA machine under Windows 7, 8 cores per socket. I'm using parallel computing toolbox to do that. I've noticed a very strange cpu load pattern: then running say 36 parallel MATLABs, the cores on the 1st socket are fully loaded, 2nd socket is almost fully loaded too, third socket is about 50% and so on. The last socket is usually almost completely free and doing nothing. Running more than 12 parallel workers simultaneously seem to very adversely affect performance of all workers.
I tried to experiment with cpu affinity, pinning different workers to different cores. While it helps in simple tests (i.e. cpu load pattern becomes uniform across all cores), it doesn't help in our real-life memory-intensive computations.
I suspect the problem is with memory locality. I.e. all memory is allocated on 1st and 2nd sockets. This would explain strange cpu load: OS tires to run computational threads closer to the data. But I don't know neither how to confirm this suspicion directly, nor how to fix it, if it's true.
I use maxNumCompThreads(4) in all my parallel workers, if that's important. Hyperthreading is off.
You should only be able to run 12 local workers using Parallel Computing Toolbox. See the data sheet.
Please note that in R2014a the limit on the number of local workers was removed. See the release notes.
I have a strange problem but may not be that much strange to some of you.
I am writing an application using boost threads and using boost barriers to synchronize the threads. I have two machines to test the application.
Machine 1 is a core2 duo (T8300) cpu machine (windows XP professional - 4GB RAM) where I am getting following performance figures :
Number of threads :1 , TPS :21
Number of threads :2 , TPS :35 (66 % improvement)
further increase in number of threads decreases the TPS but that is understandable as the machine has only two cores.
Machine 2 is a 2 quad core ( Xeon X5355) cpu machine (windows 2003 server with 4GB RAM) and has 8 effective cores.
Number of threads :1 , TPS :21
Number of threads :2 , TPS :27 (28 % improvement)
Number of threads :4 , TPS :25
Number of threads :8 , TPS :24
As you can see, performance is degrading after 2 threads (though it has 8 cores). If the program has some bottle neck , then for 2 thread also it should have degraded.
Any idea? , Explanations ? , Does the OS has some role in performance ? - It seems like the Core2duo (2.4GHz) scales better than Xeon X5355 (2.66GHz) though it has better clock speed.
Thank you
-Zoolii
The clock speed and the operating system doesn't have as much to do with it as the way your code is written. Things to check might include:
Are you actually spinning up more than two threads at one time?
Do you have unnecessary synchronization artifacts in your code?
Are you synchronizing your code at the appropriate places?
What is your shareable resource and how many of then are there? If each of your transactions is relying on a single section of code, native library, file, database, whatever, then it doesn't matter how many CPUs you've got.
One tool at your disposal when analyzing software bottlenecks is the simple thread dump. Taking a few dumps throughout the life of an execution of your software should expose bottlenecks in your software. You may be able to take that output and use it to reevaluate your code.
Adding more CPU's does not always equate to better performance, locking and contention can severely degrade performance. Factors to consider are:
Is your algorithm suited to parallelisation?
Any inherently sequential portions of code?
Can you partition work into coarse grained 'chunks'? Corase is usually better than fine grained...
Can you alter your code to use less locking?
Synchronisation overheads can often be reduced by ensuring chunks of work are similiar sized.
Based on experience it could be that the Intel policy is 2 threads or dual-process only on that processor, that only pthreads can be used with that version of operating system, that the two processors were designed to conform to different laws with different provisions or allows, that the own thread process is not allowed, that more than n threads are being backed-out by the processor and the processing of error messages reporting this is slowing down throughput of the two cores and may lead to deactivate of cores 3 and 4.