Nested data-parallelism on the gpu

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,...

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...

Harnessing the Multicores: Nested Data Parallelism in Haskell

If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn’t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming...