RT info:eu-repo/semantics/conferenceObject
T1 Trasgo 2.0: Code generation for parallel distributed- and shared-memory hierarchical systems
A1 Moretón Fernández, Ana
A1 González Escribano, Arturo
A1 Llanos Ferraris, Diego Rafael
AB Current multicomputers are typically built as interconnected clusters of shared-memory multicore computers. A common programming approach for these clusters is to simply use a message-passing paradigm, launching as many processes as cores available. Nevertheless, to better exploit the scalability of these clusters  and  highly-parallel  multicore  systems,  it  is  needed  to  efficiently  use  their distributed- and shared-memory hierarchies. This implies to combine different programming paradigms and tools at different levels of the program design.Programming  in  this  kind  of  environment  is  challenging.  Many  successful parallel programming models and tools have been proposed for specific environments.  However,  the  application  programmer  still  faces  many  important  decisions not related with the parallel algorithms, but with implementation issues that are key for obtaining efficient programs. For example, decisions about partition and locality vs. synchronization/communication costs; grain selection and tiling; proper parallelization strategies for each grain level; or mapping, layout,and scheduling details. Moreover, many of these decisions may change for different machine details or structure, or even with data sizes. This paper presents an automatic code generation system for mixed distributed- and shared-memory parallel multicomputers. We present an extension of the Trasgo programming model. This extended model supports a wider range of  parallel  structures  and  applications  where  coordination  is  expressed  at  an abstract level. Transparent modular objects are invoked to guide the partition and mapping of both data and processes, across the whole system. We present a technique that, for affine expressions, compute exact aggregated communications at the distributed level. It uses intersection of remote and local footprints in terms of the mapping policies selected. Moreover, Trasgo 2.0 integrates polyhedral analysis tools to obtain optimizations inside each shared-memory parallel node at the shared level. This approach allows to automatically generate mul-tilevel parallel programs that adapt their communication and synchronization structures to the target machine. Our experimental results for both, shared- and distributed-memory  environments,  show  how  this  approach  can  automatically produce  efficient  codes  when  compared  with  manually-optimized  codes  using MPI or OpenMP models.
PB Universidad de Valladolid, Escuela de Ingeniería Informática
YR 2015
FD 2015
LK http://uvadoc.uva.es/handle/10324/29114
UL http://uvadoc.uva.es/handle/10324/29114
LA eng
NO Producción Científica
DS UVaDOC
RD 23-nov-2024