Google's MapReduce programming model serves for processing large data sets in a
massively parallel manner. We deliver the first rigorous description of the model including its …

Divide-and-conquer algorithms are suitable for modern parallel machines, tending to have
large amounts of inherent parallelism and working well with caches and deep memory …

Data-parallel computations, such as linear algebra routines and stencil computations,
constitute one of the most relevant classes in parallel computing, eg, due to their importance …

With the advent of high-performance COTS clusters, there is a need for a simple, scalable
and fault-tolerant parallel programming and execution paradigm. In this paper, we show that …

The problems involved in develo** efficient parallel programs have proved harder than
those in develo** efficient sequential ones, both for programmers and for compilers …

Parallel programs on lists have been intensively studied. It is well known that associativity
provides a good characterization for divide-and-conquer parallel programs. In particular, the …

Homomorphisms are functions that match the divide-and-conquer pattern and are widely
used in parallel programming. Two problems are studied for homomorphisms on lists: The …

Homomorphisms are functions that match the divide-and-conquer paradigm and thus can be
computed in parallel. Two problems are studied for homomorphisms on lists:(1) parallelism …

Algorithmic paradigms such as divide-and-conquer (D&C) are proposed to guide
developers in designing efficient algorithms, but it can still be difficult to apply algorithmic …

Parallel primitives (skeletons) intend to encourage programmers to build a parallel program
from ready-made components for which e cient implementations are known to exist, making …