spmd | Spmd , Scala Port Mapper Daemon and friends
kandi X-RAY | spmd Summary
kandi X-RAY | spmd Summary
Port mapper daemon which keeps track of local nodes and assigns free ports to them. spmd is started automatically when first node on host starts (unless it is already running).
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of spmd
spmd Key Features
spmd Examples and Code Snippets
Lines of Code : 43
License : No License
Copyfrom jax import random, pmap
import jax.numpy as jnp
# Create 8 random 5000 x 6000 matrices, one per GPU
keys = random.split(random.PRNGKey(0), 8)
mats = pmap(lambda key: random.normal(key, (5000, 6000)))(keys)
# Run a local matmul on each device i import jax.numpy as jnp
from jax import grad, jit, vmap
def predict(params, inputs):
for W, b in params:
outputs = jnp.dot(inputs, W) + b
inputs = jnp.tanh(outputs) # inputs to the next layer
return outputs # no activatio def _postprocess_flat_outputs(
outputs: Any,
need_spmd_partitioning: bool
) -> Tuple[List[Optional[core_types.Tensor]], List[ops.Operation], List[Any]]:
"""Validates non-flat outputs, add backs device assignments and other attrs.
Args def approx_min_k(operand,
k,
reduction_dimension=-1,
recall_target=0.95,
reduction_input_size_override=-1,
aggregate_to_topk=True,
name=None):
""" def _postprocess_non_flat_outputs(
outputs: Any,
need_spmd_partitioning: bool
) -> Tuple[List[Optional[core_types.Tensor]], List[ops.Operation], List[Any]]:
"""Validates non-flat outputs, add backs device assignments and other attrs.
Community Discussions
Trending Discussions on spmd
QUESTION
Using matlab's spmd to compute simple triple integral is giving me incorrect solution, any thoughts on what I am doing wrong?
...ANSWER
Answered 2020-Oct-20 at 10:23The problem here is that your spmd block is dividing the 3-dimensional region to be integrated in each dimension, rather than just a single dimension. You need to pick a dimension in which to divide the integral, and vary the limits in only that dimension. For example, you could correct things by replacing your integral3 call inside spmd with this:
QUESTION
I am trying to loop inside the codistributed pieces of a distributed array within a spmd block in MATLAB
R2020a:
ANSWER
Answered 2020-Jul-30 at 15:56Are you sure the problem here isn't inside myFunction? The following code works correctly for me:
QUESTION
I have a laptop with 4 physical cores, and the MatLab parallel computing toolbox. I need to perform two independent task (really expensive, let's say to compute the largest eigenvalue of a dense,large, matrix).
So, I want to distribute the tasks to my core in the following way:
- 2 cores on the first task
- 2 cores on the second task
but I really can't understand/find how to set this in a MatLab code.
After searching a lot, I've seen I should use spmd, but I can't find in the documentation a proper example that allows me to use 2 cores for the same task.
Any minimal working example in MatLab would be really appreciated!
EDIT after Daniel's comment: After creating a parallel pool of 4, workers, I could do:
...ANSWER
Answered 2020-May-12 at 05:53Following on from the various comments, if you set a cluster object's NumThreads property, then each worker you launch will use that number of computational threads. You can do this through the Cluster Profile Manager, or programmatically.
When you launch parpool, the number you specify is the number of worker processes you want to launch, and each worker will have a number of threads corresponding to the cluster object's NumThreads property.
Putting this together, we get:
QUESTION
I would like to shut down my parallel pool by a button press in a Matlab GUI to stop the execution of functions running on these pool workers.
Unfortunately this only works when starting the functions with "parfeval()". In this case, as soon as I press the button, my parallel pool is shutting down and therefore the functions called with parfeval() stop running.
As I prefer using "spmd" over "parfeval" to establish communication between the workers, I tried the same but it failed.
Nothing is happening on a button press and the parallel pool is only shutting down as soon as I cancel the whole script with ctrl+c.
Hope someone can assist me with this problem.
Working:
...ANSWER
Answered 2020-Apr-09 at 11:16For your use-case, asynchronous execution of the parallel tasks is critical.
The asynchronous evaluation of fcn does not block MATLAB (from
doc parfeval)
When using parfeval your primary MATLAB instance is not blocked, allowing the GUI to execute code. Synchronous interfaces like spmd or parfor are not suitable for your situation. While the workers are busy, your primary instance is blocked and unable to execute any code.
Related (same question asking for parfor): https://mathworks.com/matlabcentral/answers/401838-how-to-halt-parfor-execution-from-a-ui
QUESTION
I am currently studying concurrent systems, and I've become a little confused with the concept of cache coherency when working with multiple threads and multiple cores at the same time.
Some assumptions as I understand:
- Cores have caches
- Cores may have multiple threads at one time (if hyperthreaded)
- A thread is a single line of commands that are getting processed
- Thus, threads are not physical hardware and threads don't have caches and use the core's cache
Suppose a core has we have two threads and x is a shared variable with value five. Both want to execute:
my_y = x;
Where my_y is a private variable defined by both threads. Now suppose thread 0 executes:
x++;
Finally, suppose that thread 1 now executes:
my_z = x;
Where my_z is another private variable.
My book says the following:
What's the value in
my_z? Is it five? Or is it six? The problem is that there are (at least) three copies ofx: the one in main memory, the one in thread 0's cache, and the one in thread 1's cache.
How does this work? How are there at least three copies of x and why does the book specify that each thread has its own cache? To me, it would make sense that the core which is running the two threads has the value of x in its cache and thus both threads have the value in "their" (shared) cache.
In other words, when x++ is updated, the value in the core's cache would be updated. Then, thread 1 would execute my_z = x; which is still in the core's cache and it is up to date. Thus, there would be no coherency issue because the two threads basically share the cache.
It could be that the book assumes that each core has only one thread, but the book did previously mention something about "if there are more threads than cores". Does "if there are more threads than cores" imply that a core has more than one thread (hyperthreading) or is there some sort of thread scheduling happening so that each core only has one thread at a time?
Even if this is the case (the scheduling of cores and that a core can only have one thread at a time), if a core owns thread 0, executes x++ and then obtains thread 1 who is trying to execute my_z = x; the value of x would still exist in that core's cache, if I'm not mistaken.
Bonus question: how are the private variables of threads stored in memory? Are they stored in the same way as any variable where they get copied in a core's cache when used? If this is the case, will it be a problem to have a private variable in the core's cache if multiple threads are using the cache - whether simultaneously or scheduled?
As per @biziclop 's request, the book states the following assumptions:
- We use MIMD systems, that is, the nodes have the same architecture. (The book doesn't specifically state which architecture this is, though)
- Our programs are SPMD. Thus, we'll write a single program that can use branching to have multiple behaviours.
- We assume cores are identical but operate asynchronously.
- We program in the C language and in this section we focused on Pthreads.
Any help would be appreciated!
...ANSWER
Answered 2019-May-22 at 16:14why does the book specify that each thread has its own cache?
The author is being sloppy. Threads don't have caches. The processor cores on which the threads run have caches.
the book did previously [say,] "if there are more threads than cores". Does [that] imply that a core has more than one thread (hyperthreading) or is there some sort of thread scheduling happening so that each core only has one thread at a time?
Either one of those things could be true. We've already established that the author is a bit sloppy with language, so taking that quote out of its context, there's no way to tell whether it's talking about more hardware threads than cores or more software threads.
how are the private variables of threads stored in memory?
All of the threads in a process see the same exact virtual address space. In the broadest sense, "private" simply describes a memory location that is only used by one thread, and it doesn't matter why the location is only used by one thread.
In a more narrow sense, each thread has a stack of function activation records (a.k.a., the "call stack") containing the arguments and the local variables of all of the active function calls. In many programming languages it is impossible for one thread to share its args or locals with any other thread, so those memory locations are automatically "private." In other programming languages, it is possible to share an arg or a local, but the programmer has to write explicit code to share it, and in any case, it's probably a bad idea.
will it be a problem to have a private variable in the core's cache if multiple threads are using the cache - whether simultaneously or scheduled?
When two different memory locations both hash to the same cache location, that's called a collision. And yeah! collisions happen some times. If a certain cache line contains variable X, and thread T wants to access variable Y which happens to use the same cache line, then the memory system will make thread T wait while it fetches the data from the main memory.
The phenomenon also is called "false sharing" (typically when it becomes a problem,) and you can Google for strategies to avoid it if and when you determine that it actually brings down the performance of your program.
QUESTION
I have a code that goes like this which I want to run using parpool: result = zeros(J,K)
...ANSWER
Answered 2018-Sep-29 at 23:45old1 and old2 can be used, I think. Initialize as constants using:
old1 = parallel.pool.Constant(old1);
old2 = parallel.pool.Constant(old2);
Have you seen this post? https://www.mathworks.com/help/distcomp/improve-parfor-performance.html
QUESTION
I am experimenting with MATLAB SPDM. However, I have the following problem to solve:
- I am running a quite long algorithm and I would like to save the progress along the way in case the power gets cut, someone unplugs the power plug or memory error.
- The loop has 144 iterations that take each around 30 minutes to complete => 72h. A lot of problems can occur in that interval. Of course, I have the distributed computing toolbox on my machine. The computer has 4 physical cores. I run MATLAB R2016a.
- I do not really want to use a parfor loop because I concatenate results and have dependency across iterations. I think SPMD is the best choice for what I want to do.
I'll try to describe what I want as best as I can: I want to be able to save at a set iteration of the loop the results so far, and I want to save the results by worker.
Below is a Minimum (non)-Working Example. The last four lines should be put in a different .m file. This function, called within a parfor loop, allows to save intermediate iterations. It is working properly in other routines that I use. The error is at line 45 (output_save). Somehow, I would like to "pull" the composite object into a "regular" object (cell/structure).
My hunch is that I do not quite understand how Composite objects work and especially how they can be saved into "regular" objects (cells, structures, etc).
...ANSWER
Answered 2018-Jul-03 at 08:49A Composite is created only outside an spmd block. In particular, variables that you define inside an spmd block exist as a Composite outside that block. When the same variable is used back inside an spmd block, it is transformed back into the original value. Like so:
QUESTION
I have a spmd block inside a for-loop. I need a 2*9 vector called Aa to be distributed between three or more workers, some calculations are performed and as a result, few matrices and vectors are generated. then the matrices of each worker and all workers are concatenated together. The resulted matrix and vector are converted to double and a new spmd block is started. The error "Subscripted assignment dimension mismatch" is shown when I run the code. Can I increase the number of workers? Here is some part of the code:
Thank you in advance
...ANSWER
Answered 2018-Mar-05 at 07:50The following code works well for that purpose.
QUESTION
I'm pretty sure I'm controlling correctly the Random Number Generator when analyzing my data in my PC. Yet when I put the script to run in another server I get back different results. And the reason I think my code is correct is because I have total reproducability within a machine. Same results in same machines, always! Different results when using the server...
- My PC has Windows and one Intel i7 (4cores), while the server has Linux and one Intel XEON (8cores).
I've read the documentation regarding parfor really thoroughly, and what I'm doing is to assign a specific Substream number in each iteration, according to the iteration number and not the worker id.
Even when trying to create a pool with the same number of workers (4) in the 8core machine, I still get different results...
Here is the basic code used, without the irrelevant details.
...ANSWER
Answered 2017-May-08 at 19:52Are you sure that this is unique to parfor and running code in parallel? In general, MATLAB isn't guaranteed to give bit-for-bit identical answers on different operating systems because of different optimizations that the system and 3rd party libraries use across OSes. Moreover, if the two systems have different processor architectures, it's even less likely you'll get bit-for-bit identical answers because there will be different optimizations for the instruction sets of each processor.
QUESTION
I've used spmd to calculate two piece of code simultaneously. The computer which I'm using have a processor with 8 cores.which means the communication overhead is something like zero!
I compare the running time of this spmd block and same code outside of spmd with tic & toc.
When I run the code, The parallel version of my code take more time than the sequential form.
Any idea why is that so?
Here is a sample code of what I'm talking about :
ANSWER
Answered 2017-Jan-16 at 08:14There are two reasons here. Firstly, your use of if labindex == 2 means that the main body of the spmd block is being executed by only a single worker - there's no parallelism here.
Secondly, it's important to remember that (by default) parallel pool workers run in single computational thread mode. Therefore, when using local workers, you can only expect speedup when the body of your parallel construct cannot be implicitly multi-threaded by MATLAB.
Finally, in this particular case, you're much better off using bsxfun (or implicit expansion in R2016b or later), like so:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install spmd
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
