CS 591 x I/O in MPI

MPI exists as many different implementations MPI implementations are based on MPI standards MPI standards are developed and maintained by the MPI Forum

I/O in MPI MPI implementations conform well to MPI standards MPI 1 standards avoid the issue of I/O This is a problem since it is rare that a useful program does no I/O How to handle I/O is left to the individual implementations

I/O in MPI To use C I/O functions – which processes have access to stdin, stdout, stderr? This is undefined in MPI. Sometimes all processes have access to stdout. In some implementations only one process has access to stdout

I/O in MPI Sometimes stdout is only available to rank 0 in MPI_COMM_WORLD Same is true of stdin Some implementations provide no access to stdin

I/O in MPI So how do you create portable programs? Make some assumptions Do some checking

I/O in MPI Recall in our MPI implementation – MPI running under PBS puts stdout in a file (*.oxxxxx) No direct access to stdin

stdin in PBS/Torque -I -- means interactive can be on qsub command line or in script job still starts under the control of scheduler When job starts PBS/MPI will provide you with an interactive shell Not terribly obvious

I/O in MPI Two ways to deal with I/O in MPI define a specific approach in your program use specialized parallel I/O system I/O in parallel systems in a hot topic in high performance computing research

I/O in MPI Learn or define a single process that can do input (stdin) and output (stdout) Usually this will be rank 0 in MPI_COMM_WORLD Write program to have IO process manage all user IO (user input/reports, prompts,etc.)

I/O in MPI Attribute caching recall that topologies are attributes associated (attached to communicators) There are other attributes attached to communicators… … and you can assign your own for example, designate a process to handle IO

Attribute Caching Duplicate the communicator MPI_Comm_dup(old_comm, &new_comm); Define a key value (index) for the new attribute MPI_Keyval_create(MPI_DUP_FN, MPI_NULL_DELETE_FN, &IO_KEY, extra_arg);

Attribute caching Define a value for the attribute – define the rank of the designated IO process *io_rank = 0; Assign the attribute to to communicator MPI_Attr_put(io_comm, IO_KEY, io_rank); To retrieve an attribute MPI_Attr_get(io_comm, IO_KEY, &io_rank_att, &flag);

Attribute Caching Attribute caching functions are local you may need to share attribute values with other processes in the comm.

I/O Process Even though no IO mechanism is defined in MPI… MPI implementations should have several predefined attributes for MPI_COMM_WORLD One of these in MPI_IO Defines which in process in the comm is suppose to be able to do IO

I/O process If no process can do IO MPI_IO = MPI_PROC_NULL If every process in the comm can do IO MPI_IO = MPI_ANY_SOURCE If some can and some cannot process that can MPI_IO = myrank process that cannot MPI_IO = rank that can

I/O Process MPI_IO really means which process can do output still may not have access to stdin

MPI-IO –stdin, stdout,stderr for stdout – create an io communicator identify an IO process in the communicator or – create an IO process in the communicator IO process gathers results from compute processes IO process outputs results

MPI-IO -stdin Recall that all processes have access to stdin -only one process may have access to stdin, or no processes have access to stdin How will we know?

Testing stdin in MPI #include #include "mpi.h" main(int argc, char** argv) { int size, rank, numb; MPI_Init(&argc, &argv); MPI_Comm_size(MPI_COMM_WORLD, &size); MPI_Comm_rank(MPI_COMM_WORLD, &rank); printf("enter an integer "); scanf(" %d",&numb); printf("Hello world! I'm %d of %d - numb = %d\n", rank, size,numb); MPI_Finalize(); }

Testing stdin in MPI #include #include "mpi.h" main(int argc, char** argv) { int size, rank, numb; MPI_Init(&argc, &argv); MPI_Comm_size(MPI_COMM_WORLD, &size); MPI_Comm_rank(MPI_COMM_WORLD, &rank); if (rank == 0) { printf("enter an integer "); scanf(" %d",&numb);} MPI_Bcast(&numb, 1, MPI_INT, 0, MPI_COMM_WORLD); printf("Hello world! I'm %d of %d - numb = %d\n", rank, size,numb); MPI_Finalize();}

stdin – what to do? If all processes have access to stdin – designate one process as the IO process have that process read from stdin distribute input to other processes If only one process has access to stdin- identify which process has access to stdin have IO process read from stdin distribute data to other processes

stdin – What to do? If no process has access to stdin – pass data as command line arguments read input data from files create include files with data values  nuisance

File IO in MPI File IO can be a major bottleneck in the performance of a parallel application Parallel application can have large (enormous) data sets We often think of file IO as a side-effect – least in terms of performance – not true in parallel applications “One half hour of IO for every 2 hours of computation”

MPI File IO types of Applications Large grids and meshes storing grid point results for post pressing distributing data for input Checkpointing periodically saving the state of a job how much work can you afford to lose?

MPI File IO types of applications Disk caching data to large for local memories Data mining small compute load but a lot of file IO combing through large datasets  ex. CFD

File IO in MPI Recall that the use stdin, stdout, stderr assume, generally, a single channel for each of these This is not true with respect to file IO – sort of. Gathering to an IO node may not be the most efficient strategy

File IO in MPI In parallel systems you have multiple processors running concurrently each may have the ability to do file IO – concurrently Know your architecture Network shared disk storage  diskless compute nodes  directories shared across nodes

Directories on Energy /home/user - is shared and same on all nodes (r/w) /usr/local/packages/ - is shared and same on all nodes (ro) all other directories on any node are local to each node Implications?

IO example staging data for input dividing data before input to job distribute data pieces to local compute node disk drives each compute node reads local files to get its piece of the data  as opposed to “read and scatter” uses standard file IO calls

IO Example Dump and collect In some cases large results datasets do not need to gathered to an IO node compute node writes data to file on local disk drive postprocess program “visits” compute nodes and collects locally stored data postprocessor store integrated data set.

File IO strategy IO Process/Scatter-Gather vs. Local IO/distribute-collect Depends on – use of input/output size of dataset file IO capacity of compute nodes  available disk space  disk IO performance