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Futhark: purely functional GPU-programming with nested parallelism and in-place array updates
TLDR
Futhark is a purely functional data-parallel array language that offers a machine-neutral programming model and an optimising compiler that generates OpenCL code for GPUs. Expand
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A T2 graph-reduction approach to fusion
Fusion is one of the most important code transformations as it has the potential to substantially optimize both the memory hierarchy time overhead and, sometimes asymptotically, the spaceExpand
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Size slicing: a hybrid approach to size inference in futhark
TLDR
We present a shape inference analysis for a purely-functional language, named Futhark, that supports nested parallelism via array combinators such as map, reduce, filter}, and scan}. Expand
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Incremental flattening for nested data parallelism
TLDR
This paper proposes a compiler-driven analysis that systematically clusters datasets to code versions, which are discriminated at runtime by statically generated predicates, whose threshold values are autotuned to hardware characteristics. Expand
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Data-parallel flattening by expansion
TLDR
We present a higher-order programmer-level technique for compiling particular kinds of irregular data-parallel problems to parallel hardware. Expand
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FinPar: A Parallel Financial Benchmark
TLDR
We present a benchmark suite for optimizing compilers and parallel DSLs that comprises three array-based applications from the financial domain that are suitable for gpgpu execution. Expand
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Bounds Checking: An Instance of Hybrid Analysis
TLDR
This paper presents an analysis for bounds checking of array subscripts that lifts checking assertions to program level under the form of an arbitrarily-complex predicate, whose runtime evaluation guards the execution of the code of interest. Expand
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Design and GPGPU performance of Futhark's redomap construct
TLDR
This paper presents and evaluates a novel second-order operator, named 'redomap', that stems from 'map'-'reduce' compositions in the context of the purely-functional array language Futhark, which is aimed at efficient GPGPU execution. Expand
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APL on GPUs: a TAIL from the past, scribbled in Futhark
TLDR
This paper demonstrates translation schemes by which programs written in a functional subset of APL can be compiled to code that is run efficiently on general purpose graphical processing units (GPGPUs). Expand
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