Speeding up the evaluation phase of GP classification algorithms on GPUs

Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

doi:10.1007/s00500-011-0713-4

Cited by 38 publications

(28 citation statements)

References 18 publications

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“…The use of GPUs for the evaluation of individuals in evolutionary computation has demonstrated high performance in many studies. These studies include using genetic programming for stock trading [36], classification rules [5,18], differential evolution [11,42], image clustering [29], or optimization problems [14]. However, to the best of our knowledge there are no GPU-based implementations of multi-instance classification rules algorithms to date.…”

Section: Rule-based Modelsmentioning

confidence: 99%

“…This process is iteratively repeated along a given number of generations. However, it is well-known and it has been demonstrated in several studies [5,18,33] that the evaluation phase is the one that demands most of the computational cost of the algorithm, requiring from 90% to 99% of the execution time, which increases as the data set becomes bigger. Thereby, significant effort should be focus on speeding up this stage.…”

Section: Rule-based Modelsmentioning

confidence: 99%

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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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Section: Rule-based Modelsmentioning

confidence: 99%

Section: Rule-based Modelsmentioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

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“…Specifically, there are GPU-accelerated genetic rule-based systems for individual = rule approaches, which have been shown to achieve high performance [29,30,31]. Franco et al [29] reported a speedup of up to 58× using the BioHEL system.…”

Section: Introductionmentioning

confidence: 99%

“…Franco et al [29] reported a speedup of up to 58× using the BioHEL system. Cano et al [30] reported a speedup of up to 820×, considering a scalable model using multiple GPU devices. Augusto [31] reported a speedup of up to 100× compared to a single-threaded model and delivering almost 10× the throughput of a twelve-core CPU.…”

Section: Introductionmentioning

confidence: 99%

“…For these accesses to be fully coalesced, both the width of the thread block and the width of the array must be a multiple of the warp size. Therefore, the results array employs intra-array padding to align the memory addresses to the memory transfer segment sizes [30,55]. Since the number of threads per block is said to be 192, the results array intra-array padding forces the memory alignment to 192 float values, i.e, 768 bytes.…”

Section: Rule Coverage Kernelmentioning

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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A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.

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Speeding up the evaluation phase of GP classification algorithms on GPUs

Cited by 38 publications

References 18 publications

Speeding up multiple instance learning classification rules on GPUs

Speeding up multiple instance learning classification rules on GPUs

Parallel evaluation of Pittsburgh rule-based classifiers on GPUs

A survey on graphic processing unit computing for large‐scale data mining

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