A Binding Scope Analysis for Generic Programs on Arrays

Grelck, Clemens; Scholz, Sven-Bodo; Shafarenko, Alex

doi:10.1007/11964681_13

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…The application of ⊕ works as follows: ⊕([1, 2, 3], [4, 5, 6]) = [ [1,4], [2,5], [3,6]]. It works similarly to the concatenation, but adds an extra dimension and does concatenation on the last shape component rather than on the first one.…”

Section: Correctness Criteriamentioning

confidence: 97%

“…The language is a stripped-down version of SAC [2] very similar to the core language introduced in [3]. In essence, the language constitutes an applied λ-calculus with a C flavour when it comes to the syntax for function definitions and function applications.…”

Section: Settingmentioning

confidence: 99%

“…Both shape and data are vectors, which are denoted in square brackets, e.g. [1,2,3]. Scalar constants are represented as arrays with empty shapes.…”

Section: Semantics Of Sac-λmentioning

confidence: 99%

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Semantics-preserving data layout transformations for improved vectorisation

Šinkarovs

Scholz

2013

Proceedings of the 2nd ACM SIGPLAN Workshop on Functional High-Performance Computing

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Data-Layouts that are favourable from an algorithmic perspective often are less suitable for vectorisation, i.e., for an effective use of modern processor's vector instructions. This paper presents work on a compiler driven approach towards automatically transforming data layouts into a form that is suitable for vectorisation. In particular, we present a program transformation for a first-order functional array programming language that systematically modifies they layouts of all data structures. At the same time, the transformation also adjusts the code that operates on these structures so that the overall computation remains unchanged. We define a correctness criterion for layout modifying program transformations and we show that our transformation abides to this criterion.

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Section: Correctness Criteriamentioning

confidence: 97%

Section: Settingmentioning

confidence: 99%

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Semantics-preserving data layout transformations for improved vectorisation

Šinkarovs

Scholz

2013

Proceedings of the 2nd ACM SIGPLAN Workshop on Functional High-Performance Computing

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“…A compilation scheme for shape-generic SaC programs has been devised that takes the array types into account to avoid creating run time descriptors whenever possible [17]. To improve the static shape information, the SaC compiler uses a combination of partial evaluation and function specialisation [18]. Recently, we proposed symbolic array attributes as a uniform scheme to infer and represent structural information in shape-generic array programs such that it may be used by optimisations [19].…”

Section: Related Workmentioning

confidence: 99%

Descriptor-Free Representation of Arrays with Dependent Types

Trojahner

Grelck

2011

Implementation and Application of Functional Languages

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Abstract. Besides element type and values, a multidimensional array is characterized by the number of axes (rank) and their respective lengths (shape vector). Both properties are essential to do bound checking and to compute linear offsets into heap memory at run time. In order to have an array's rank and shape available at any time during program execution both are typically kept in an array descriptor that is maintained at run time in addition to the array itself. In this paper, we propose a different approach: we treat array rank and shape as first-class citizens themselves. Firstly, we use dependent types to reflect structural properties of arrays in the type system. Secondly, we annotate a program with the array explicit array properties wherever necessary. This choice not only renders implicit run time array descriptors obsolete, but exposing all rank and shape computations explicitly in intermediate code also allows us to perform extensive compile time optimisation on them. We have implemented the proposed approach in our experimental array language Qube; preliminary experimental results indicate the suitability of the proposed approach.

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“…Over recent years we have developed several analysis and program transformation techniques that aim at identifying these properties. Amongst these are partial evaluation techniques such as those described in [36,59,10,39], as well as code restructuring techniques [56,34,37], and multi-threaded code generation techniques [33,35]. However, the interplay of such optimisations combined with the complexity and variety of target platforms often renders static mapping approaches far less effective than the mappings that can easily be achieved manually.…”

Section: Related Workmentioning

confidence: 99%

The ParaPhrase Project: Parallel Patterns for Adaptive Heterogeneous Multicore Systems

Hammond

Aldinucci²,

Brown

et al. 2013

Formal Methods for Components and Objects

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Abstract. This paper describes the ParaPhrase project, a new 3-year targeted research project funded under EU Framework 7 Objective 3.4 (Computer Systems), starting in October 2011. ParaPhrase aims to follow a new approach to introducing parallelism using advanced refactoring techniques coupled with high-level parallel design patterns. The refactoring approach will use these design patterns to restructure programs defined as networks of software components into other forms that are more suited to parallel execution. The programmer will be aided by high-level cost information that will be integrated into the refactoring tools. The implementation of these patterns will then use a wellunderstood algorithmic skeleton approach to achieve good parallelism.A key ParaPhrase design goal is that parallel components are intended to match heterogeneous architectures, defined in terms of CPU/GPU combinations, for example. In order to achieve this, the ParaPhrase approach will map components at link time to the available hardware, and will then re-map them during program execution, taking account of multiple applications, changes in hardware resource availability, the desire to reduce communication costs etc. In this way, we aim to develop a new approach to programming that will be able to produce software that can adapt to dynamic changes in the system environment. Moreover, by using a strong component basis for parallelism, we can achieve potentially significant gains in terms of reducing sharing at a high level of abstraction, and so in reducing or even eliminating the costs that are usually associated with cache management, locking, and synchronisation.

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A Binding Scope Analysis for Generic Programs on Arrays

Cited by 9 publications

References 12 publications

Semantics-preserving data layout transformations for improved vectorisation

Semantics-preserving data layout transformations for improved vectorisation

Descriptor-Free Representation of Arrays with Dependent Types

The ParaPhrase Project: Parallel Patterns for Adaptive Heterogeneous Multicore Systems

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