Machine learning (ML) algorithms are commonly applied to big data, using distributed
systems that partition the data across machines and allow each machine to read and update …

In recent years, the infrastructure supporting the execution of situated distributed
computations evolved at a fast pace. Modern collective adaptive applications–as found in …

The Partitioned Global Address Space (PGAS) programming models combine shared and
distributed memory features, providing the basis for high performance and high productivity …

We discuss new developments of a hybrid parallel iterative sparse linear solver framework
focused on petroleum reservoir flow and geomechanical simulation. It runs efficiently on …

Lambda expressions have long been state-of-the-art in the functional programming
paradigm. Especially with regard to the use of higher-order functions, they provide …

A higher-order function takes one or more functions as inputs or outputs to support the
generality of function definitions. In modern programming languages, higher-order functions …

Compared to threads, tasks are a more fine-grained alternative. The task parallel
programming model offers benefits in terms of better performance portability and better load …

Software design and component reuse for heuristic algorithms have gained in relevance;
however, further innovation is needed. In this context, hMod is presented as a software …

Abstraction based programming has been traditionally seen as an approach that improves
software quality at the cost of losing performance. In this paper, we explore the cost of …

Big data developments have been centred mainly on the volume dimension of data, with
frameworks such as Hadoop and Spark, capable of processing very large data sets in …