A SIMD Interpreter for Genetic Programming on GPU Graphics Cards

Langdon, William B.; Banzhaf, Wolfgang

doi:10.1007/978-3-540-78671-9_7

Cited by 104 publications

(82 citation statements)

References 14 publications

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“…The efficiency of rule interpreters is often reported by means of the number of primitives interpreted by the system per second, similarly to Genetic Programming interpreters, which determine the number of Genetic Programming operations per second (GPops/s) [31,53,54].…”

Section: Rule Interpreter Performancementioning

confidence: 99%

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Parallel evaluation of Pittsburgh rule-based classifiers on GPUs

Cano¹,

Zafra²,

Ventura³

2014

Neurocomputing

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Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speeded up of up to 3.461× when compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptable.

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Section: Rule Interpreter Performancementioning

confidence: 99%

“…Rule specification can be formally defined by means of a context-free grammar [52] as the shown in Figure 1. [53,54]. Traditional stack-based interpreters perform push and pop operations on a stack, involving the operator and operands found in the rule.…”

Section: Pittsburgh Individual Encodingmentioning

confidence: 99%

Parallel evaluation of Pittsburgh rule-based classifiers on GPUs

Cano¹,

Zafra²,

Ventura³

2014

Neurocomputing

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show abstract

“…The efficiency of rules interpreters is often reported using the number of primitives interpreted by the system per second, similarly to Genetic Programming (GP) interpreters, which determine the number of GP operations per second (GPops/s) [3,[30][31][32]. GP interpreters evaluate expression trees, which represent solutions to perform a user-defined task.…”

Section: Rules Interpreter Performancementioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

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Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second.

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“…Our GPU SIMD interpreter approach [49] took the opposite approach. It avoids the (surprisingly high) cost of compiling GP individuals by using an interpreter and secondly it processes the whole population in parallel.…”

Section: Interpreted Parallel Populationsmentioning

confidence: 99%

“…There are good reasons for using CUDA. For example, in [49] we used an early version of RapidMind (version 2), Robilliard et al [59] reimplemented it using an up to date version of CUDA and reported an (up to) 92% speed increase.…”

Section: Future Of Genetic Programming On Gpumentioning

confidence: 99%

Graphics processing units and genetic programming: an overview

Langdon

2011

Soft Comput

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A top end graphics card (GPU) plus a suitable SIMD interpreter, can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular we survey genetic programming (GP) use with GPU. We give several applications from Bioinformatics and show how the fastest GP is based on an interpreter rather than compilation. Finally using GP to generate GPU CUDA kernel C++ code is sketched.

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A SIMD Interpreter for Genetic Programming on GPU Graphics Cards

Cited by 104 publications

References 14 publications

Parallel evaluation of Pittsburgh rule-based classifiers on GPUs

Parallel evaluation of Pittsburgh rule-based classifiers on GPUs

Speeding up multiple instance learning classification rules on GPUs

Graphics processing units and genetic programming: an overview

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