Vortrag Dr. Jordi Garcia, 15. 12. 1997, 17 Uhr

[Maria Cherry]

                          UNIVERSITAET WIEN 
          INSTITUT FUER SOFTWARETECHNIK UND PARALLELE SYSTEME
                            gemeinsam mit 
                                VCPC 
           EUROPEAN CENTRE FOR PARALLEL COMPUTING AT VIENNA 


      EINLADUNG ZU EINEM VORTRAG IM RAHMEN DES INSTITUTS-KOLLOQUIUMS:
                
         
              A Framework for Optimal Data Mapping in DMM
 
 			   
                            Dr. Jordi Garcia
                     Computer Architecture Department
              Universitat Politecnica de Catalunya, Barcelona
                                 

                 ZEIT: Montag, 15. 12. 1997, 17 Uhr c.t.
       ORT: Institut fuer Softwaretechnik und Parallele Systeme
                  1090 Wien, Liechtensteinstrasse 22, 
                         Seminarraum, Mezzanin


Abstract

Massively Parallel Processor systems provide the required computational
power to solve most large scale High Performance Computing applications.
Machines with physically distributed memory allow a cost-effective way
to achieve this performance, however, these systems are very difficult
to program and tune. In a distributed-memory organization each processor
has direct access to its local memory, and indirect access to the remote
memories of other processors. But the cost of accessing a local memory
location can be more than one order of magnitude faster than accessing a
remote memory location. In these systems, the choice of a good data
distribution strategy can dramatically improve performance, although
different parts of the data distribution problem have been proved to be
NP-complete.

The selection of an optimal data placement depends on the program
structure, the program's data sizes, the compiler capabilities, and some
characteristics of the target machine. In addition, there is often a
trade-off between minimizing interprocessor data movement and load
balancing on processors.
Automatic data distribution tools can assist the programmer in the
selection of a good data layout strategy. These use to be
source-to-source tools which annotate the original program with data
distribution directives. Crucial aspects such as data movement,
parallelism, and load balance have to be taken into consideration in a
unified way to efficiently solve the data distribution problem.

In this talk, our proposal oriented to provide an optimal solution to the
data distribution problem will be presented. The solution provided is
optimal for a given problem size and architectura, and according to our
cost and compilation model. The applications considered for parallelization
are usually regular problems, in which data structures are dense arrays.
The data mapping capabilities provided by the tool includes alignment
of the arrays, one or two-dimensional distribution with BLOCK or
CYCLIC fashion, a set of remapping actions to be performed between
phases if profitable, plus the associated parallelization strategy.
The effects of control flow statements between phases are taken into
account in order to improve the accuracy of the model.
The novelty of the approach resides in handling all stages of the data
distribution problem, that traditionally have been treated in several
independent phases, in a single step, and providing an optimal solution.