Data-only flattening for nested data parallelism

Work-Efficient Nested Data-Parallelism†

An apply-to-all construct is the key mechanism for expressing data-parallelism, but data-parallel programming languages like HPF and C* significantly restrict which operations can appear in the construct. Allowing arbitrary operations substantially simplifies the expression of irregular and nested data-parallel computations. The technique of flattening nested parallelism introduced by Blelloch,...

A Calculational Approach to Flattening Nested Data Parallelism in Functional Languages

The data-parallel programming model is currently the most successful model for programming massively parallel computers. Unfortunately, it is, in its present form, restricted to exploitingflat data parallelism, which is not suitable for some classes of algorithms, e.g. those operating on irregular structures. Recently, some effort has been made to implement nested data-parallel programs efficie...

How Portable is Nested Data Parallelism?

Nepal - Nested Data Parallelism in Haskell

This paper dis usses an extension of Haskell by support for nested data-parallel programming in the style of the spe ialpurpose language Nesl. More pre isely, the extension onsists of a parallel array type, array omprehensions, and a set of primitive parallel array operations. This extension brings a hitherto unsupported style of parallel programming to Haskell. Moreover, nested data parallelis...

Functional programming for nested data parallelism on GPUs

Recent advances in general purpose GPU computing technology allow new data parallel kernel jobs to be dispatched dynamically during kernel execution. This enables significantly more expressive programming using nested data parallelism (NDP), where the restrictive need for flat data structures and computation has been lifted. Functional programming is fundamentally well suited for expressing dat...