Applying Search in an Automatic Contract-Based Testing Tool

Kolesnichenko, Alexey; Poskitt, Christopher M.; Meyer, Bertrand

doi:10.1007/978-3-642-39742-4_31

Cited by 1 publication

(1 citation statement)

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“…We also plan to investigate its use in larger case studies, in particular, applying the library to speed up embarrassingly-parallel evolutionary algorithms used in test data generation (as outlined in [42]). …”

Section: Resultsmentioning

confidence: 99%

SafeGPU: Contract- and library-based GPGPU for object-oriented languages

Kolesnichenko

Poskitt

Nanz

2017

Computer Languages, Systems & Structures

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Using GPUs as general-purpose processors has revolutionized parallel computing by providing, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to their widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper proposes a programming approach, SafeGPU, that aims to make GPU data-parallel operations accessible through high-level libraries for object-oriented languages, while maintaining the performance benefits of lower-level code. The approach provides data-parallel operations for collections that can be chained and combined to express compound computations, with data synchronization and device management all handled automatically. It also integrates the design-by-contract methodology, which increases confidence in functional program correctness by embedding executable specifications into the program text. We present a prototype of SafeGPU for Eiffel, and show that it leads to modular and concise code that is accessible for GPGPU non-experts, while still providing performance comparable with that of hand-written CUDA code. We also describe our first steps towards porting it to C#, highlighting some challenges, solutions, and insights for implementing the approach in different managed languages. Finally, we show that runtime contract-checking becomes feasible in SafeGPU, as the contracts can be executed on the GPU.

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Section: Resultsmentioning

confidence: 99%