Nikola: embedding compiled GPU functions in Haskell
Proceedings of the third ACM Haskell symposium on Haskell
Oracle scheduling: controlling granularity in implicitly parallel languages
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
Program parallelization using synchronized pipelining
LOPSTR'09 Proceedings of the 19th international conference on Logic-Based Program Synthesis and Transformation
Riposte: a trace-driven compiler and parallel VM for vector code in R
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Data-only flattening for nested data parallelism
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
Exploiting vector instructions with generalized stream fusio
Proceedings of the 18th ACM SIGPLAN international conference on Functional programming
Automatic SIMD vectorization for Haskell
Proceedings of the 18th ACM SIGPLAN international conference on Functional programming
Towards a functional run-time for dense NLA domain
Proceedings of the 2nd ACM SIGPLAN workshop on Functional high-performance computing
Freeze after writing: quasi-deterministic parallel programming with LVars
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
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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, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is to use data parallelism. Blelloch's pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this talk I will describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. I will focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism, and I hope to present performance numbers.