Graphics processing units and genetic programming: an overview

Langdon, William B.

doi:10.1007/s00500-011-0695-2

Cited by 60 publications

(33 citation statements)

References 64 publications

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“…[16], have advocated writing highly parametrised parallel code which can then be automatically tuned. Unfortunately this throws the load back on to the coder [17]. Here we demonstrate that genetic programming can work with an auto-tuner to adapt human written code to new circumstances and different hardware.…”

Section: Namementioning

confidence: 94%

Genetically Improved CUDA C++ Software

Langdon

Harman

2014

Lecture Notes in Computer Science

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Section: Namementioning

confidence: 94%

Genetically Improved CUDA C++ Software

Langdon

Harman

2014

Lecture Notes in Computer Science

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“…This performance has been utilised in many computational intelligence applications, including neural networks and ant colony optimisation algorithms [15]. Delevacq et al [5] created a solution that used GPUs to speed up an ant colony optimisation algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Moving on to parallelism in genetic programming application, Langdon [15] describes a method of using stacks and RPN (Reverse Polish Notation) to create a stack based SIMD (Single Instruction, Multiple Data) interpreter, which will leave the results on the top of Figure 4: The process involves flattening and copying data from host to GPU, then the evaluation can be performed. The second kernel is required to reduce the amount of data returned from the kernel.…”

Section: Related Workmentioning

confidence: 99%

Working with OpenCL to speed up a genetic programming financial forecasting algorithm

Brookhouse

Otero

Kampouridis

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device.

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“…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.

show abstract

Graphics processing units and genetic programming: an overview

Cited by 60 publications

References 64 publications

Genetically Improved CUDA C++ Software

Genetically Improved CUDA C++ Software

Working with OpenCL to speed up a genetic programming financial forecasting algorithm

Speeding up multiple instance learning classification rules on GPUs

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