On the call today, no one had any objections to bringing this stuff to
the trunk. v1.2.9 and v1.3.0 releases have a higher priority, so I'll
bring this stuff over to the trunk when those two releases are done
(hopefully tomorrow!).
On Jan 10, 2009, at 2:21 PM, Jeff Squyres wrote:
FWIW, I've finished a first cut of this stuff. I'll provide an
overview on next Tuesday's teleconf.
I didn't "fix" MPI_REPLACE yet (it does seem to be a different
issue; I mainly extended what was already there) but I've done most
of the rest of the work:
- Created a new op framework that was inspired by the coll framework.
- Similar to the "coll" framework, the op framework supports:
- Mixing-n-matching op modules on a single MPI_Op
- "Stacking" op modules (e.g., choose at invocation time whether
a module will use its back-end hardware, or whether it should fall
back to a different module's implementation)
- Unlike the coll framework, all the "basic" functions are in the op
base and are pre-loaded onto the MPI_Op during selection as the 0th
priority (so you can stack them naturally -- base functions even
have a [bogus] module, so you can RETAIN them just like any other
module) -- there's no "basic" component or set of modules.
- Created an "example" op component that has a few sample routines
and shows a bunch of different OMPI concepts, both in the op
framework and utilizing other parts of the OMPI code base (hopefully
helpful to newbie OMPI component authors).
==> NOTE: The example op is currently fairly chatty with
opal_output() so that you can see that it is being used.
I'll .ompi_ignore it (or something) when it is brought into the
trunk so that the example component isn't active in production runs.
- Created wiki pages describing autogen, how to create a framework,
and how to create a component (hopefully helpful to newbie OMPI
component authors).
=======================
I think that the second phase of this work will be the various
hardware providers providing their components to Open MPI (e.g.,
cuda, opencl, IBM Cell, ...etc.).
If this all proves worthwhile, I think a third phase of this work
could be optimizing the top-level reduction calls based on what
nodes have hardware acceleration and which do not (e.g., if
accelerators are not available in all nodes, that may changes the
collection/reduction communication pattern).
On Jan 5, 2009, at 10:21 AM, Jeff Squyres wrote:
On Jan 5, 2009, at 10:09 AM, Brian W. Barrett wrote:
I think this sounds reasonable, if (and only if) MPI_Accumulate is
properly handled. The interface for calling the op functions was
broken in some fairly obvious way for accumulate when I was
writing the one-sided code. I think I had to call some supposedly
internal bits of the interface to make accumulate work. I can't
remember what they are now, but I do remember it being a problem.
Coolio; I'll look into it.
Of course, unless it makes mpi_allreduce on one double-sized
floating point number using sum go faster, I'm not entirely sure a
change is helpful ;).
From my (admittedly limited) understanding, since there are memory
registration and/or copy in/out issues with GPUs, the operation has
to be "big enough" and/or already located in GPU memory for the GPU
to outperform the CPU. It is my assumption that the component-ized
CUDA/OpenCL/whatever code will need to make a decision whether it
should perform the operation at run-time or pass it back to a
fallback [probably CPU-based] implementation, analogous to how
"tuned" picks the right coll algorithm.
I'm told that there's some researchy middleware working on exactly
this kind of problem (determining if a given operation is suitable
to run on the GPU or the main CPU). So in a best-case scenario,
OMPI can just link against and use that middleware rather than
implementing all the logic in the component itself. We'll see how
it plays out.
My goal is to give these guys the infrastructure that they need in
OMPI to play with these kind of concepts and see what they can
accomplish in terms of real performance. FWIW: a few SC08
attendees thought that they could avoid writing much CUDA/CL/
whatever code if MPI_REDUCE did the work for them (particularly if
paired with the proposed MPI_REDUCE_LOCAL function, https://svn.mpi-forum.org/trac/mpi-forum-web/ticket/24)
. [shrug] We'll see!
--
Jeff Squyres
Cisco Systems
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Cisco Systems
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