I have to implement an MPI program. There are some global variables (4 arrays of float numbers and other 6 single float variables) which are first inizialized by the main process reading data from a file. Then I call MPI_Init and, while process of rank 0 waits for results, the other processes (rank 1,2,3,4) work on the arrays etc...
The problem is that those array seem not to be initialized anymore, all is set to 0. I tried to move global variable inside the main function but the result is the same. When MPI_Init() is called all processes are created by fork right? So everyone has a memory copy of the father so why do they see not initizialized arrays?
I fear you have misunderstood.
It is probably best to think of each MPI process as an independent program, albeit one with the same source code as every other process in the computation. Operations that process 0 carries out on variables in its address space have no impact on the contents of the address spaces of other processes.
I'm not sure that the MPI standard even requires process 0 to have values for variables which were declared and initialised prior to the call to mpi_init, that is before process 0 really exists.
Whether it does or not you will have to write code to get the values into the variables in the address space of the other processes. One way to do this would be to have process 0 send the values to the other processes, either one by one or using a broadcast. Another way would be for all processes to read the values from the input files; if you choose this option watch out for contention over i/o resources.
In passing, I don't think it is common for MPI implementations to create processes by forking at the call to mpi_init, forking is more commonly used for creating threads. I think that most MPI implementations actually create the processes when you make a call to mpiexec, the call to mpi_init is the formality which announces that your program is starting its parallel computations.
When MPI_Init() is called all processes are created by fork right?
Wrong.
MPI spawns multiple instances of your program. These instances are separate processes, each with its own memory space. Each process has its own copy of every variable, including globals. MPI_Init() only initializes the MPI environment so that other MPI functions can be called.
As the other answers say, that's not how MPI works. Data is unique to each process and must be explicitly transferred between processes using the API available in the MPI specification.
However, there are programming models that allow this sort of behavior. If, when you say parallel computing, you mean multiple cores on one processor, you might be better served by using something like OpenMP to share your data between threads.
Alternatively, if you do in fact need to use multiple processors (either because your data is too big to fit in one processor's memory, or some other reason), you can take a look at one of the Parallel Global Address Space (PGAS) languages. In those models, you have memory that is globally available to all processes in an execution.
Last, there is a part of MPI that does allow you to expose memory from one process to other processes. It's the Remote Memory Access (RMA) or One-Sided chapter. It can be complex, but powerful if that's the kind of computing model you need.
All of these models will require changing the way your application works, but it sounds like they might map to your problem better.
Related
I want to use MPI_Gatherv to collect data onto a single process. The thing is, I don't need data from all other processes, just a subset of them. The only communicator in the code is MPI_COMM_WORLD. So, does every process in MPI_COMM_WORLD have to call MPI_Gatherv? I have looked at the MPI standard and can't really make out what it is saying. If all processes must make the call, can some of the values in MPI_Gatherv's "recvcounts" array be zero? Or would there be some other way to signal to the root process which processes should be ignored? I guess I could introduce another communicator, but for this particular problem that would be coding overkill.
I have 2 processors (this is an example), and I want these 2 processors to write in a file. I want them to write at the end of file, but not in a mixed pattern, like that :
[file content]
proc0
proc1
proc0
proc1
proc0
proc1
(and so on..)
I'd like to make them write following this kind of pattern :
[file content]
proc0
proc0
proc0
proc1
proc1
proc1
(and so on..)
Is it possible? If so, what's the setting to use?
The sequence in which your processes have outputs ready to report is, essentially, unknowable in advance. Even repeated runs of exactly the same MPI program will show differences in the ordering of outputs. So something, somewhere, is going to have to impose an ordering on the writes to the file.
A very common pattern, the one Wesley has already mentioned, is to have all processes send their outputs to one process, often process 0, and let it deal with the writing to file. This master-writer could sort the outputs before writing but this creates a couple of problems: allocating space to store output before writing it and, more difficult to deal with, determining when a collection of output records can be sorted and written to file and the output buffers be reused. How long does the master-writer wait and how does it know if a process is still working ?
So it's common to have the master-writer write outputs as it gets them and for another program to order the output file as desired after the parallel program has finished. You could tack this on to your parallel program as a step after mpi_finalize or you could use a completely separate program (such as sort on a Linux machine). Of course, for this to work each output record has to contain some sequencing information on which to sort.
Another common pattern is to only have one process which does any writing at all, that is, none of the other processes do any output at all. This completely avoids the non-determinism of the sequencing of the writing.
Another pattern, less common partly because it is more difficult to implement and partly because it depends on underlying mechanisms which are not always available, is to use mpi io. With mpi io multiple processes can write to different parts of a file as if simultaneously. To actually write simultaneously the program needs to be executing on hardware, network and operating system which supports parallel i/o. It can be tricky to implement this even with the right platform, and especially when the volume of output from processes is uncertain.
In my experience here on SO people asking question such as yours are probably at too early a stage in their MPI experience to be tackling parallel i/o, even if they have access to the necessary hardware.
I disagree with High Performance Mark. MPI-IO isn't so tricky in 2014 (as long as you have have access to any file system besides NFS -- install PVFS if you need a cheap easy parallel file system).
If you know how much data each process has, you can use MPI_SCAN to efficiently compute how much data was written by "earlier" processes, then use MPI_FILE_WRITE_AT_ALL to carry out the I/O efficiently. Here's one way you might do this:
incr = (count*datatype_size);
MPI_Scan(&incr, &new_offset, 1, MPI_LONG_LONG_INT,
MPI_SUM, MPI_COMM_WORLD);
MPI_File_write_at_all(mpi_fh, new_offset, buf, count,
datatype, status)
The answer to your question is no. If you do things that way, you'll end up with jumbled output from all over the place.
However, you can get the same thing by sending your output to a single processor having it do all of the writing itself. For example, at the end of your application, just have everything send to rank 0 and have rank 0 write it all to a file.
If we call MPI_Init() we know that multiple copies of the same executable run on different machines. Suppose MPI_Init() is in a function f(), then will multiple copies of main() function exist too?
The main problem that I am facing is of taking inputs. In effect, what is happening is that input is being taken once but the main function is running several times. The processor with rank 0 always seems to have the input, rest of them have random values. So to send the values do we have to broadcast the input from processor 0 to all the other processors?
MPI_Init() doesn't create multiple copies, it just initializes in-process MPI library. Multiple copies of your process are created before that, most probably with some kind of mpirun command (that is how you run your MPI application). All processes are independent from the beginning, so answering the first part of your question — yes, multiple copies of main() will exist, and they will exist even if you don't call MPI_Init.
The answer to your question about inputs depends on nature of the inputs: if it's typed in from console, then you have to input the values only in one process (e.g. rank 0) and then broadcast them. If the inputs are in some file or specified as a command-line argument, then all processes can access them.
We have a large Fortran/MPI code-base which makes use of system-V shared memory segments on a node. We run on fat nodes with 32 processors, but only 2 or 4 NICs, and relatively little memory per CPU; so the idea is that we set up a shared memory segment, on which each CPU performs its calculation (in its block of the SMP array). MPI is then used to handle inter-node communications, but only on the master in the SMP group. The procedure is double-buffered, and has worked nicely for us.
The problem came when we decided to switch to asynchronous comms, for a bit of latency hiding. Since only a couple of CPUs on the node communicate over MPI, but all of the CPUs see the received array (via shared memory), a CPU doesn't know when the communicating CPU has finished, unless we enact some kind of barrier, and then why do asynchronous comms?
The ideal, hypothetical solution would be to put the request tags in an SMP segment and run mpi_request_get_status on the CPU which needs to know. Of course, the request tag is only registered on the communicating CPU, so it doesn't work! Another proposed possibility was to branch a thread off on the communicating thread and use it to run mpi_request_get_status in a loop, with the flag argument in a shared memory segment, so all the other images can see. Unfortunately, that's not an option either, since we are constrained not to use threading libraries.
The only viable option we've come up with seems to work, but feels like a dirty hack. We put an impossible value in the upper-bound address of the receive buffer, that way once the mpi_irecv has completed, the value has changed and hence every CPU knows when it can safely use the buffer. Is that ok? It seems that it would only work reliably if the MPI implementation can be guaranteed to transfer data consecutively. That almost sounds convincing, since we've written this thing in Fortran and so our arrays are contiguous; I would imagine that the access would be also.
Any thoughts?
Thanks,
Joly
Here's a pseudo-code template of the kind of thing I'm doing. Haven't got the code as a reference at home, so I hope I haven't forgotten anything crucial, but I'll make sure when I'm back to the office...
pseudo(array_arg1(:,:), array_arg2(:,:)...)
integer, parameter : num_buffers=2
Complex64bit, smp : buffer(:,:,num_buffers)
integer : prev_node, next_node
integer : send_tag(num_buffers), recv_tag(num_buffers)
integer : current, next
integer : num_nodes
boolean : do_comms
boolean, smp : safe(num_buffers)
boolean, smp : calc_complete(num_cores_on_node,num_buffers)
allocate_arrays(...)
work_out_neighbours(prev_node,next_node)
am_i_a_slave(do_comms)
setup_ipc(buffer,...)
setup_ipc(safe,...)
setup_ipc(calc_complete,...)
current = 1
next = mod(current,num_buffers)+1
safe=true
calc_complete=false
work_out_num_nodes_in_ring(num_nodes)
do i=1,num_nodes
if(do_comms)
check_all_tags_and_set_safe_flags(send_tag, recv_tag, safe) # just in case anything else has finished.
check_tags_and_wait_if_need_be(current, send_tag, recv_tag)
safe(current)=true
else
wait_until_true(safe(current))
end if
calc_complete(my_rank,current)=false
calc_complete(my_rank,current)=calculate_stuff(array_arg1,array_arg2..., buffer(current), bounds_on_process)
if(not calc_complete(my_rank,current)) error("fail!")
if(do_comms)
check_all_tags_and_set_safe(send_tag, recv_tag, safe)
check_tags_and_wait_if_need_be(next, send_tag, recv_tag)
recv(prev_node, buffer(next), recv_tag(next))
safe(next)=false
wait_until_true(all(calc_complete(:,current)))
check_tags_and_wait_if_need_be(current, send_tag, recv_tag)
send(next_node, buffer(current), send_tag(current))
safe(current)=false
end if
work_out_new_bounds()
current=next
next=mod(next,num_buffers)+1
end do
end pseudo
So ideally, I would have liked to have run "check_all_tags_and_set_safe_flags" in a loop in another thread on the communicating process, or even better: do away with "safe flags" and make the handle to the sends / receives available on the slaves, then I could run: "check_tags_and_wait_if_need_be(current, send_tag, recv_tag)" (mpi_wait) before the calculation on the slaves instead of "wait_until_true(safe(current))".
"...unless we enact some kind of barrier, and then why do asynchronous comms?"
That sentence is a bit confused. The purpose of asynchrononous communications is to overlap communications and computations; that you can hopefully get some real work done while the communications is going on. But this means you now have two tasks occuring which eventually have to be synchronized, so there has to be something which blocks the tasks at the end of the first communications phase before they go onto the second computation phase (or whatever).
The question of what to do in this case to implement things nicely (it seems like what you've got now works but you're rightly concerned about the fragility of the result) depends on how you're doing the implementation. You use the word threads, but (a) you're using sysv shared memory segments, which you wouldn't need to do if you had threads, and (b) you're constrained not to be using threading libraries, so presumably you actually mean you're fork()ing processes after MPI_Init() or something?
I agree with Hristo that your best bet is almost certainly to use OpenMP for on-node distribution of computation, and would probably greatly simplify your code. It would help to know more about your constraint to not use threading libraries.
Another approach which would still avoid you having to "roll your own" process-based communication layer that you use in addition to MPI would be to have all the processes on the node be MPI processes, but create a few communicators - one to do the global communications, and one "local" communicator per node. Only a couple of processes per node would be a part of a communicator which actually does off-node communications, and the others do work on the shared memory segment. Then you could use MPI-based methods for synchronization (Wait, or Barrier) for the on-node synchronization. The upcoming MPI3 will actually have some explicit support for using local shared memory segments this way.
Finally, if you're absolutely bound and determined to keep doing things through what's essentially your own local-node-only IPC implementation --- since you're already using SysV shared memory segments, you might as well use SysV semaphores to do the synchronization. You're already using your own (somewhat delicate) semaphore-like mechanism to "flag" when the data is ready for computation; here you could use a more robust, already-written semaphore to let the non-MPI processes know when the data is ready for computation (and a similar mechanism to let the MPI process know when the others are done with the computation).
Suppose that I've two big functions. Is it better to write them in a separate kernels and call them sequentially, or is better to write only one kernel? (I don't want to read the data back and force form between host and device in between). What about the speed up if I want to call the kernel many times?
One thing to consider is the effect of register pressure on hardware utilization and performance.
As a general rule, big kernels have big register footprints. Typical OpenCL devices (ie. GPUs) have very finite register file sizes and large kernels can result in lower concurrency (fewer concurrent warps/wavefronts), less opportunities for latency hiding, and poorer overall performance. On the other hand, kernel launch overheads are pretty low on most platforms, so if your algorithm doesn't have an enormous amount of state to save between "phases" of execution, the penalty of using multiple kernels can be rather low.
Using multiple kernels also has another side benefit -- you get implicit synchronization between all work units for free. Often that can eliminate the need for atomic memory operations and synchronization primitives which can have a negative impact on code performance.
The ultimate guide should be measured performance. There is no universal rule-of-thumb for this sort of things. Benchmarking is the only way to know for sure.
In general this is a question of (maybe) slightly better performance vs. readibility of your code. Copying buffers is no issue as long as you keep them within the same context. E.g. you could set one output buffer of a kernel to be an input buffer of the next kernel, which would not involve any copying.
The proper way to code in OpenCL is to separate your code into parallel tasks, and each of them is a kernel. This is, each "for loop" should be a kernel. Some times one single CPU code function could result in a 4 kernel implementation in OCL.
If you need to store data between kernel executions just use OpenCL buffers and do not copy to host (this solves the DEVICE<->HOST bottleneck).
If both functions act to different data you could propably write a single kernel, but that depends on the complexity of the operation being run.