A bridging model for parallel computation
Communications of the ACM
BSPlib: The BSP programming library
Parallel Computing
Submachine Locality in the Bulk Synchronous Setting (Extended Abstract)
Euro-Par '96 Proceedings of the Second International Euro-Par Conference on Parallel Processing-Volume II
Parallel composition and bulk synchronous parallel functional programming
Selected papers from the 2nd Scottish Functional Programming Workshop (SFP00)
ESA '95 Proceedings of the Third Annual European Symposium on Algorithms
The Paderborn University BSP (PUB) library
Parallel Computing
Interactive Theorem Proving and Program Development
Interactive Theorem Proving and Program Development
Parallel Scientific Computation: A Structured Approach Using BSP and MPI
Parallel Scientific Computation: A Structured Approach Using BSP and MPI
A static analysis for Bulk Synchronous Parallel ML to avoid parallel nesting
Future Generation Computer Systems - Special issue: Parallel computing technologies
Parallel superposition for bulk synchronous parallel ML
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Bulk synchronous parallel ML: modular implementation and performance prediction
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Snow: a parallel computing framework for the R system
International Journal of Parallel Programming
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The design of parallel programs and parallel programming languages is a trade-off. On one hand the programs should be efficient. But the efficiency should not come at the price of non portability and unpredictability of performances. The portability of code is needed to allow code reuse on a wide variety of architectures and to allow the existence of legacy code. The predictability of performances is needed to guarantee that the efficiency will always be achieved, whatever is the used architecture.