GPU-Based Parallelism for ASP-Solving

Dovier, Agostino; Formisano, Andrea; Vella, Flavio

doi:10.1007/978-3-030-46714-2_1

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Finally, the study of irregular applications and hence the cost of feature extraction remains an open challenge. As an example, the extraction of the sparsity pattern might be costly when dealing with sparse matrix multiplication, and this aspect is crucial for predicting the best storage format, algorithm [6,7,19,58,72,74], and other optimization parameters [17].…”

Section: Discussion and Future Workmentioning

confidence: 99%

On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and Beyond

Labini

Cianfriglia

Perri

et al. 2021

ACM Trans. Archit. Code Optim.

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Efficient HPC libraries often expose multiple tunable parameters, algorithmic implementations, or a combination of them, to provide optimized routines. The optimal parameters and algorithmic choices may depend on input properties such as the shapes of the matrices involved in the operation. Traditionally, these parameters are manually tuned or set by auto-tuners. In emerging applications such as deep learning, this approach is not effective across the wide range of inputs and architectures used in practice. In this work, we analyze different machine learning techniques and predictive models to accelerate the convolution operator and GEMM. Moreover, we address the problem of dataset generation, and we study the performance, accuracy, and generalization ability of the models. Our insights allow us to improve the performance of computationally expensive deep learning primitives on high-end GPUs as well as low-power embedded GPU architectures on three different libraries. Experimental results show significant improvement in the target applications from 50% up to 300% compared to auto-tuned and high-optimized vendor-based heuristics by using simple decision tree- and MLP-based models.

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Section: Discussion and Future Workmentioning

confidence: 99%

On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and Beyond

Labini

Cianfriglia

Perri

et al. 2021

ACM Trans. Archit. Code Optim.

Self Cite

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show abstract

“…However, to harness such computing power, it is crucial to employ approaches and algorithms that align with the underlying architecture of the GPU. Recent studies indicate that GPUs can be used for computational logic, including applications like SAT [8,7], ASP [11,12], and CP [34,35].…”

Section: General-purpose Computing On Graphics Processing Unitsmentioning

confidence: 99%

Constraint propagation on GPU: A case study for the AllDifferent constraint

Tardivo

Dovier

Formisano

et al. 2023

Journal of Logic and Computation

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The AllDifferent constraint is a fundamental tool in Constraint Programming. It naturally arises in many problems, from puzzles to scheduling and routing applications. Such popularity has prompted an extensive literature on filtering and propagation for this constraint. This paper investigates the use of General Processing Units (GPUs) to accelerate filtering and propagation. In particular, the paper presents an efficient parallelization of the AllDifferent constraint on GPU, along with an analysis of different design and implementation choices and evaluation of the performance of the resulting system on several benchmarks.

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