MPY = Message Passing Yorick
The mpy package interfaces yorick to the MPI parallel programming
library. MPI stands for Message Passing Interface; the idea is to
connect multiple instances of yorick that communicate among themselves
via messages. Mpy can either perform simple, highly parallel tasks as
pure interpreted programs, or it can start and steer arbitrarily
complex compiled packages which are free to use the compiled MPI API.
The interpreted API is not intended to be an MPI wrapper; instead it
is stripped to the bare minimum.
This is version 2 of mpy (released in 2010); it is incompatible with
version 1 of mpy (released in the mid 1990s), because version 1 had
numerous design flaws making it very difficult to write programs free
of race conditions, and impossible to scale to millions of processors.
However, you can run most version 1 mpy programs under version 2 by
doing mp_include,"mpy1.i" before you mp_include any file defining an
mpy1 parallel task (that is before any file containg a call to
There are two major open source MPI implementations:
Open MPI: http://www.open-mpi.org/
(Note that Open MPI is the successor to several other implementations,
most notably LAM.) Documentation is at:
The configuration script accepts several options:
--cflags='flags to use when compiling with mpicc'
--ldflags='flags to use when loading with mpicc'
You need to build and install yorick first. If this mpy source directory
is in the yorick distribution tree, and if yorick has been installed in
the relocate subdirectory (a sibling of this directory, and the recommended
way to build yorick), and if the CFLAGS and LDFLAGS are the same as for
CC when yorick is built, then simply ./configure will suffice.
removes all products of the build, but not configure
removes all products of the build and configure
If you want mpy to be scalable to large numbers of processes (say more
than 100), you will need to use TGT=exe versions of any additional
packages you want mpy to have. This is because (as of 2010), no known
MPI platforms support scalable dlopen, so that attempting to load a
shared library in a shared filesystem will overload the filesystem as
every process simultaneously attempts to read the shared library.
The MPI environment is not really specified by the standard; existing
environments are very crude, and strongly favor non-interactive batch
jobs. The number of processes is fixed before MPI begins; each
process has a rank, a number from 0 to one less than the number of
processes. You use the rank as an address to send messages, and the
process receiving the message can probe to see which ranks have sent
messages to it, and of course receive those messages.
A major problem in writing a message passing program is handling
events or messages arriving in an unplanned order. MPI guarantees
only that a sequence of messages send by rank A to rank B will arrive
in the order sent. There is no guarantee about the order of arrival
of those messages relative to messages sent to B from a third rank C.
In particular, suppose A sends a message to B, then A sends a message
to C (or even exchanges several messages with C) which results in C
sending a message to B. The message from C may arrive at B before the
message from A. An MPI program which does not allow for this
possibility has a bug called a "race condition". Race conditions may
be extremely subtle, especially when the number of processes is large.
The basic mpy interpreted interface consists of two variables:
mp_size = number of proccesses
mp_rank = rank of this process
and four functions:
mp_send, to, msg; // send msg to rank "to"
msg = mp_recv(from); // receive msg from rank "from"
ranks = mp_probe(block); // query senders of pending messages
mp_exec, string; // parse and execute string on every rank
You call mp_exec on rank 0 to start a parallel task. When the main
program thus created finishes, all ranks other than rank 0 return to
an idle loop, waiting for the next mp_exec. Rank 0 picks up the next
input line from stdin (that is, waits for input at its prompt in an
interactive session), or terminates all processes if no more input is
available in a batch session.
The mpy package modifies how yorick handles the #include parser
directive, and the include and require functions. Namely, if a
parallel task is running (that is, a function started by mp_exec),
these all become collective operations. That is, rank 0 reads the
entire file contents, and sends the contents to the other processes as
an MPI message (like mp_exec of the file contents). Every process
other than rank 0 is only running during parallel tasks; outside a
parallel task when only rank 0 is running (and all other ranks are
waiting for the next mp_exec), the #include directive and the include
and require functions return to their usual serial operation,
affecting only rank 0.
When mpy starts, it is in parallel mode, so that all the files yorick
includes when it starts (the files in Y_SITE/i0) are included as
collective operations. Without this feature, every yorick process
would attempt to open and read the startup include files, overloading
the file system before mpy ever gets started. Passing the contents of
these files as MPI messages is the only way to ensure there is enough
bandwidth for every process to read the contents of a single file.
The last file included at startup is either the file specified in the
-batch option, or the custom.i file. To avoid problems with code in
custom.i which may not be safe for parallel execution, mpy does not
look for custom.i, but for custommp.i instead. The instructions in
the -batch file or in custommp.i are executed in serial mode on rank 0
only. Similarly, mpy overrides the usual process_argv function, so
that -i and other command line options are processed only on rank 0 in
serial mode. The intent in all these cases is to make the -batch or
custommp.i or -i include files execute only on rank 0, as if you had
typed them there interactively. You are free to call mp_exec from any
of these files to start parallel tasks, but the file itself is serial.
An additional command line option is added to the usual set:
mpy -j somefile.i
includes somefile.i in parallel mode on all ranks (again, -i other.i
includes other.i only on rank 0 in serial mode). If there are
multiple -j options, the parallel includes happen in command line
order. If -j and -i options are mixed, however, all -j includes
happen before any -i includes.
As a side effect of the complexity of include functions in mpy, the
autoload feature is disabled; if your code actually triggers an
include by calling an autoloaded function, mpy will halt with an
error. You must explicitly load any functions necessary for a
parallel tasks using require function calls themselves inside a
The mp_send function can send any numeric yorick array (types char,
short, int, long, float, double, or complex), or a scalar string
value. The process of sending the message via MPI preserves only the
number of elements, so mp_recv produces only a scalar value or a 1D
array of values, no matter what dimensionality was passed to mp_send.
The mp_recv function requires you to specify the sender of the message
you mean to receive. It blocks until a message actually arrives from
that sender, queuing up any messages from other senders that may
arrive beforehand. The queued messages will be retrieved it the order
received when you call mp_recv for the matching sender. The queuing
feature makes it dramatically easier to avoid the simplest types of
race condition when you are write interpreted parallel programs.
The mp_probe function returns the list of all the senders of queued
messages (or nil if the queue is empty). Call mp_probe(0) to return
immediately, even if the queue is empty. Call mp_probe(1) to block if
the queue is empty, returning only when at least one message is
available for mp_recv. Call mp_probe(2) to block until a new message
arrives, even if some messages are currently available.
The mp_exec function uses a logarithmic fanout -- rank 0 sends to F
processes, each of which sends to F more, and so on, until all
processes have the message. Once a process completes all its send
operations, it parses and executes the contents of the message. The
fanout algorithm reaches N processes in log to the base F of N steps.
The F processes rank 0 sends to are ranks 1, 2, 3, ..., F. In
general, the process with rank r sends to ranks r*F+1, r*F+2, ...,
r*F+F (when these are less than N-1 for N processes). This set is
called the "staff" of rank r. Ranks with r>0 receive the message from
rank (r-1)/F, which is called the "boss" of r. The mp_exec call
interoperates with the mp_recv queue; in other words, messages from
a rank other than the boss during an mp_exec fanout will be queued for
later retrieval by mp_recv. (Without this feature, any parallel task
which used a message pattern other than logarithmic fanout would be
susceptible to race conditions.)
The logarithmic fanout and its inward equivalent are so useful that
mpy provides a couple of higher level functions that use the same
fanout pattern as mp_exec:
total = mp_handin(value);
To use mp_handout, rank 0 computes a msg, then all ranks call
mp_handout, which sends msg (an output on all ranks other than 0)
everywhere by the same fanout as mp_exec. To use mp_handin, every
process computes value, then calls mp_handin, which returns the sum of
their own value and all their staff, so that on rank 0 mp_handin
returns the sum of the values from every process.
You can call mp_handin as a function with no arguments to act as a
synchronization; when rank 0 continues after such a call, you know
that every other rank has reached that point. All parallel tasks
(anything started with mp_exec) must finish with a call to mp_handin,
or an equivalent guarantee that all processes have returned to an idle
state when the task finishes on rank 0.
You can retrieve or change the fanout parameter F using the mp_nfan
function. The default value is 16, which should be reasonable even
for very large numbers of processes.
One special parallel task is called mp_connect, which you can use to
feed interpreted command lines to any single non-0 rank, while all
other ranks sit idle. Rank 0 sits in a loop reading the keyboard and
sending the lines to the "connected" rank, which executes them, and
sends an acknowledgment back to rank 0. You run the mp_disconnect
function to complete the parallel task and drop back to rank 0.
Finally, a note about error recovery. In the event of an error during
a parallel task, mpy attempts to gracefully exit the mp_exec, so that
when rank 0 returns, all other ranks are known to be idle, ready for
the next mp_exec. This procedure will hang forever if any one of the
processes is in an infinite loop, or otherwise in a state where it
will never call mp_send, mp_recv, or mp_probe, because MPI provides no
means to send a signal that interrupts all processes. (This is one of
the ways in which the MPI environment is "crude".) The rank 0 process
is left with the rank of the first process that reported a fault, plus
a count of the number of processes that faulted for a reason other
than being sent a message that another rank had faulted. The first
faulting process can enter dbug mode via mp_connect; use mp_disconnect
or dbexit to drop back to serial mode on rank 0.