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Parallel MATLAB: Doing it Right
Ron ChoyCSAIL
MIT
• The case for parallel MATLAB• Parallel MATLAB Survey• Star-P
The Rise of Clusters• Cheap and everyone seems to have one
Some MIT Clusters
• Cheap & everyone seems to have one
Impact of Clusters
• More and more ‘non-experts’ are using clusters in their work/research
• Increased computing power does not necessarily translate to increased productivity
• Someone has to program these parallel machines, and this can be hard
Classes of interesting problems
• Large collection of small problems – easy, usually involves running a large number of serial programs – embarrassingly parallel
• Large problems – might not even fit into the memory of a machine. Much harder since it usually involves communication between processes
Parallel programming (1990 – now)
• Dominant model (for distributed memory): MPI (Message Passing Interface)
• Low level control of communications between processes
• Send, Recv, Scatter, Gather …
MPI
• It is not an easy way to do parallel programming.
• Error prone – it is hard to get complex low level communications right
• Hard to debug – MPI programs tend to die with obscure error messages. There is also no good debugger for MPI. (Totalview? Multiple GDBs?)
The result?
• A lot of researchers in the sciences need the power of parallel computing, but do not have the expertise to tackle the programming
• Time saved by using parallel computers is offset by the additional time needed to program them
• Reduced productivity!
The result? (2)
• Also the parallel programs written might not be efficient! Writing good parallel programs is hard
• A high level tool is needed to hide the complexity away from the users of parallel computers
Let’s go back to the serial world
for a moment
What do people want?
• Real applications programmers care less about “p-fold speedups”
• Ease of programming, debugging, correct answer matters much more
MATLAB
• MATrix LABoratory• Started out as an interactive frontend to
LINPACK and EISPACK• Has since grown into a powerful computing
environment with ‘Toolboxes’ ranging from neural networks to computational finance to image processing.
• Many current users know nothing/little about linear algebra (Toolbox users)
MATLAB ‘language’
• MATLAB has a C-like scripting language, with FORTRAN90-like array indexing
• Interpreted language• A wide range of linear algebra routines• Very popular in the scientific community as
a prototyping tool – easier to use than C/FORTRAN
MATLAB ‘language’ (2)
• MATLAB makes use of ATLAS (optimized BLAS) for basic linear algebra routines
• Still, performance is not as good as C/FORTRAN, especially for loops
• However MATLAB is catching up with its release 13 – (just-in-time compiling?)
Why people like MATLAB?
• MATLAB made good choices on numerical libraries that are efficient, and combined them into one interactive, easy to use package.
• People were skeptical at first because they feel MATLAB slows things down. At the end convenience, not performance, won.
Wouldn’t it be nice to mixthe convenience of MATLAB
withparallel computing?
Why there should be a parallel MATLAB
• Cleve Moler, ‘Why there isn’t a parallel MATLAB’– Memory Model– Granularity– Business Situation
Parallel MATLAB Survey
• http://theory.lcs.mit.edu/~cly/survey.html• 27 parallel MATLAB projects found, using
4 approaches– Embarrassingly Parallel– Message Passing– Backend Support– Compiler
CMTMDP-ToolboxMatlabMPIMATmarksMPITB/PVMTBMultiMATLABParallel Toolbox for MATLAB
DistributePPMATLAB Parallelization ToolkitMULTI ToolboxParalizeParmatlabPlabPMI
DlabParamatPLAPACKMatparMATLAB*PNetsolve
CONLAB CompilerFALCONMATCHMenhirOtterParALRTExpress
Parallel MATLABs
CornellRostock, GermanyLincoln LabsUIUCGranada, SpainCornellWake Forest
WUStLLinkopings, SwedenPurdueChalmers, SwedenNortheasternTUDenmarkLucent
UIUCAlpha Data UKUTAustinJPLMITUTK
Umea, SwedenUIUCAccelchipIRISA, FranceOSUSydney, AustraliaISI
Parallel MATLABs
CMTMDP-ToolboxMatlabMPIMATmarksMPITB/PVMTBMultiMATLABParallel Toolbox for MATLAB
DistributePPMATLAB Parallelization ToolkitMULTI ToolboxParalizeParmatlabPlabPMI
DlabParamatPLAPACKMatparMATLAB*PNetsolve
CONLAB CompilerFALCONMATCHMenhirOtterParALRTExpress
Parallel MATLABs
CMTMDP-ToolboxMatlabMPIMATmarksMPITB/PVMTBMultiMATLABParallel Toolbox for MATLAB
DistributePPMATLAB Parallelization ToolkitMULTI ToolboxParalizeParmatlabPlabPMI
DlabParamatPLAPACKMatparMATLAB*PNetsolve
CONLAB CompilerFALCONMATCHMenhirOtterParALRTExpress
Parallel MATLABs
Embarrassingly Parallel
• Multiple MATLABs needed• Scatter/Gather at the beginning and end• No lateral communication
Message Passing
• Multiple MATLABs required• MATLABs talk to each other in a general
MPI fashion• Superset of embarrassingly parallel
Backend support
• Requires 1 MATLAB• MATLAB is used as front end to a parallel
computation engine
Compiler
Compiler (2)
• Requires zero to one MATLAB• Compiles MATLAB scripts into native code
and link with parallel numerical libraries
What do we learn from the survey?
• The projects themselves are ‘existential proofs’ for what users want from a parallel MATLAB
• There are way too many parallel MATLAB projects
Wouldn’t it be nice to have a single project
that covers it all, and does it better than the rest?
Star-P
• Goal: To provide a unified parallel MATLAB for user-friendly parallel computing
• Uses MATLAB as a front end to a parallel linear algebra server
• Encompasses the 4 approaches in the survey
Star-P (2)
• UI idea based on MATLAB*P• Substantially different from MATLAB*P
– New code base– Unified vs. backend support
Star-P (3)
• Server leverages on popular parallel numerical libraries:– ScaLAPACK– FFTW– PARPACK– …
• As well as custom-made parallel routines– Sorting– Sparse Matrix routines
Star-P Example
A = rand(4000*p, 4000*p); x = randn(4000*p, 1); y = zeros(size(x));while norm(x-y) / norm(x) > 1e-11
y = x;x = A*x;x = x / norm(x);
end;
Structure of the System
Package Manager
MatrixManager
Proxy
Client
MATLABMatrix 1
Matrix 2
Matrix 3
PBlas FFTW Scalapack PBase
Server #0
Client Manager
Server #1 Server #2 Server #3 Server #4
ServerManager
Structure of the system (2)
• Client (C++) – Attaches to MATLAB through MEX interface– Relays commands to server and process returns
• Server (C++/MPI)– Manipulates/processes data (matrices)– Performs the actual parallel computation by
calling the appropriate library routines
Functionalities provided
• PBLAS, ScaLAPACK– solve, eig, svd, chol, matmul, +, -, …
• FFTW– 1D and 2D FFT
• PARPACK– eigs, svds
• Sparse Matrix Routines• ‘MultiMATLAB’ mode
Focus
• Requires minimal learning on user’s part• Reuses of existing scripts• Mimics MATLAB behaviour• Data stay on backend until explicitly
retrieved by user• Extendable backend• Be a system that fits all users
Comparison
Extendable through ‘Packages’ and ‘Toolboxes’
Extendable through ‘Toolboxes’
Focus is on usability not performance
Focus is on usability not performance
Frontend to PBLAS, ScaLAPACK, …
Frontend to LINPACK, EISPACK
Star-PMATLAB
What is *p?
• Star-P creates a new MATLAB class called dlayout
• By providing overloaded functions for this new class, parallelism is achieved transparently
• *p is dlayout(1)
Example
• A = randn(1024*p,1024*p);• E = eig(A);• e = pp2matlab(E);• plot(e,’*’);
Example 2
• H = hilb(n) % H = hilb(8000*p)– J = 1:n;– J = J(ones(n,1),:);– I = J’;– E = ones(n,n);– H = E./(I+J-1);
Built-in MATLAB functions
• Hilb is a MATLAB function that get parallelized automatically
• Another example of a MATLAB function that works is cgs
• We do not know how many of them works
Where is the data?
• One often-asked question in parallel computing is: where are the data residing?
• Star-P supports 3 data distributions: – A = randn(m*p,n) – row distribution– B = randn(m,n*p) – column distribution– C = randn(m*p,n*p) – 2D block cyclic
distribution
MultiMATLAB mode
• Similar to MultiMATLAB from Cornell• Starts up a MATLAB process on each node,
and runs MATLAB scripts on them in parallel
• Interesting feature is that the data can come from the ‘regular’ mode of Star-P
Example 3
% FFT2 in 4 lines• A = randn(10000,10000*p);• B = mm('fft',A);• C = B.';• D = mm('fft',C);• F = D.';
MPI in MATLAB
• User can use MPI-like commands inside MultiMATLAB mode
function result = simpletest(X)import mpi.*;MPI.init;if MPI.COMM_WORLD.rank == 0;
data = [4 2];MPI.COMM_WORLD.send(data, 2, 1, 13);result = 9;
elseif MPI.COMM_WORLD.rank == 1got = MPI.COMM_WORLD.recv(2, 0, 13);result = got(1) + got(2);
endMPI.fnlz;
Compilation
• MATLAB R14 (prerelease) provides the ability to compile MATLAB m-files that uses Star-P functionalities into binaries.
• The binaries combined with a running Star-P server enable compiled m-files to be run in parallel.
Types of parallel problems
• Collection of small problems– MultiMATLAB mode– MPI within MultiMATLAB mode
• Large problems– ‘Regular mode’ – ScaLAPACK, FFTW, …
• We even allow a mixture of the two modes to give additional flexibility – FFT2
Visualization package
• A term project done by a group of students in a parallel computing class at MIT
• Provides the equivalent of mesh, surf and spy to distributed matrices
• Visualization of very large matrix is now possible in MATLAB
Why is Star-P interesting?
• First parallel MATLAB system that encompasses the 4 approaches in the survey
• First system that allows seamless (to the user) integration between a large number of parallel numerical libraries
More
• Processor maps – ScaLAPACK magic?• Dynamic addition of processors - Singapore• Star-P on a grid – UCSB, Parry• Pipelining - ??• Fault Tolerance - UCSB