mpy - Online in the Cloud

PROGRAM:

NAME

mpy - Message Passing Yorick

SYNOPSIS

mpirun -np mp_size mpy [ -j pfile1.i [ -j pfile2.i [ ... ]]] [ -i file1.i [ -i file2.i [
... ]]]
mpirun -np mp_size mpy -batch file.i

DESCRIPTION

Yorick is an interpreted language like Basic or Lisp, but far faster. See yorick (1) to
learn more about it.
Mpy is a parallel version of Yorick based on the Message Passing Interface (MPI). The
exact syntax for launching a parallel job depends on your MPI environment. It may be
necessary to launch a special daemon before calling mirun or an equivalent command.

Explanations
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 mp_task.)

Usage notes
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 parallel task.

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:
mp_handout, msg;
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.

Options
-j file.i includes the Yorick source file file.i as mpy starts in parallel mode
on all ranks. This is equivalent to the mp_include function after mpy
has started.

-i file.i includes the Yorick source file file.i as mpy starts, in serial mode.
This is equivalent to the #include directive after mpy has started.

-batch file.i includes the Yorick source file file.i as mpy starts, in serial mode.
Your customization file custommp.i, if any, is not read, and mpy is
placed in batch mode. Use the help command on the batch function
(help, batch) to find out more about batch mode. In batch mode, all
errors are fatal; normally, mpy will halt execution and wait for more
input after an error.

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