Increasingly heterogeneous and hierarchical parallel architectures are now mainstream,
however, most of the traditional programming models are lowlevel and explicit, limiting …

Embedded languages are often compiled at application runtime; thus, embedded compile-
time errors become application runtime errors. We argue that advanced type system …

A fundamental challenge of parallel programming is to ensure that the observable outcome
of a program remains deterministic in spite of parallel execution. Language-level …

In data-intensive cluster computing platforms such as Hadoop YARN, efficiency and fairness
are two important factors for system design and optimizations. Previous studies are either for …

The statelessness of functional computations facilitates both parallelism and fault recovery.
Faults and non-uniform communication topologies are key challenges for emergent large …

High-level domain-specific languages for array processing on the GPU are increasingly
common, but they typically only run on a single GPU. As computational power is distributed …

Over time, several competing approaches to parallel Haskell programming have emerged.
Different approaches support parallelism at various different scales, ranging from small …

Conventional parallel programming is complex and error prone. To improve programmer
productivity, we need to raise the level of abstraction with a higher-level programming model …

Event-driven programming has become a major paradigm in developing concurrent,
distributed systems. Its benefits are often informally captured by the key tenet of …

The most widely available high performance platforms today are hierarchical, with shared
memory leaves, eg clusters of multi-cores, or NUMA with multiple regions. The Glasgow …