[Beowulf] Parallel memory

Richard Walsh rbw at ahpcrc.org
Wed Oct 19 10:26:40 EDT 2005


Henderson, T Todd @ IS wrote:

> Are there any drivers, tools, etc. that can make the memory space on a 
> cluster look shared, something like pvfs but for memory?  I'm sure 
> there would be a speed hit, but in this instance, speed isn't the 
> problem as much as memory.  We have a code that uses a huge amount of 
> memory and the memory usage is proportional to the cube of the problem 
> size, but the time for it to run isn't too much of an issue.  I've 
> been asked to parallelize the code using mpi which is going to be a 
> major effort.  However, I thought that if there was anyway, even if 
> inefficient speed wise, to create a virtual parallel memory system it 
> would be better than it using swap space and save a bunch of coding time.
>
>  
>
> Also, are there any tools to help implement mpi in an older code?
>
>  
>
> Any thoughts?
>
>  
>
> Thanks,
>
> Todd
>
>  
>
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Todd,

I have not read through all the replies, but options to investigate 
include the
Unified Parallel C (UPC) and Co-Array Fortran (CAF) programming models.  
These are
parallel programming extensions to standard C and F90.  They prefer hardware
that supports instruction level remote memory access like that of the 
Cray X1
and SGI Altix (so-call partitioned Global Address Space memory pGAS) where
they out performance MPI in both bandwidth and latency measures, but
there are compilers (source-to-source) available for clusters that 
compile to
RDMA abstraction layers/libraries.  Most of the common cluster interconnects
have a library, so you can grab a version of the Berkeley UPC compiler for
your particular interconnect/cluster.

In UPC, global arrays are declared as shared:

shared double mass[32][THREADS];

and distributed across the abstracted global address space according to 
a blocking
factor that you can control.  References are implicitly mixed between 
data that
happens to be local and the rest that is remote.  Performance is managed 
by minimizing
remote references through that blocking factor, casting shared pointers 
to local pointers,
and affinity mapped indexing in looping structures.  The performance 
issues that
others I am sure are mentioninng do not dissappear, but coding is more 
implicitly
parallel, more idiomatic ... than message passing and can be introduced 
incrementally
even to MPI code.

In CAF, your declare a co-array that creates a copy on each image/processor:

real, dimension(1:100)[*] :: mydata

then any reference to the a non-local piece of the co-array like:

mass = mydata(59)[4]

implies a remote reference (this assignment grabs the 59th element from the
4th image on each image executing this code).  Local references just 
drop the
[]s.  Again, the performance issues related to remote references do not 
disappear,
but are abstracted away in the particular RDMA library for the 
interconnect of
the cluster.

The target for performance of these models is to equal or slightly 
improve on MPI
on cluster systems while simplfying the conversion of the serial code to 
parallel. There
are two competing abstraction libraries, GASnet and ARMCI.  Google these and
the two extensions. 

If you are interested I can send you my course notes for both 
extensions.  There are
several good references as well and a recently published book on UPC.  
These are
not replacements for MPI especially in the short run, but do create the 
impression
of a single memory space regardless of the real remoteness of the each 
partition.

Regards,

Richard Walsh
AHPCRC

-- 

Richard B. Walsh

Project Manager
Network Computing Services, Inc.
Army High Performance Computing Research Center (AHPCRC)
rbw at ahpcrc.org  |  612.337.3467

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