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Parallel construction of Random Forest on GPU

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Abstract

There is tremendous growth of data generated from different industries, i.e., health, agriculture, engineering, etc. Consequently, there is demand for more processing power. Compared to computer processing units, general-purpose graphics processing units (GPUs) are rapidly emerging as a promising solution to achieving high performance and energy efficiency in various computing domains. Multiple forms of parallelism and complexity in memory access have posed a challenge in developing Random Forest (RF) GPU-based algorithm. RF is a popular and robust machine learning algorithm. In this paper, coarse-grained and dynamic parallelism approaches on GPU are integrated into RF(dpRFGPU). Experiment results of dpRFGPU are compared with sequential execution of RF(seqRFCPU) and parallelised RF trees on GPU(parRFGPU). Results show an improved average speedup from 1.62 to 3.57 of parRFGPU and dpRFGPU, respectively. Acceleration is also evident when RF is configured with an average of 32 number of trees and above in both dpRFGPU and parRFGPU on low-dimensional datasets. Nonetheless, larger datasets save significant time compared to smaller datasets on GPU (dpRFGPU saves more time compared to parRFGPU). dpRFGPU approach significantly accelerated RF trees on GPU. This approach significantly optimized RF trees parallelization on GPU by reducing its training time.

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Notes

  1. Data independence in RF is facilitated by bagging. In bagging, each tree is built from an independent subset of data. Each subset of data is generated by randomly sampling data from the original dataset with replacement [5].

  2. This is caused by irregular execution paths of threads in a warp.

  3. This is caused by irregular execution paths of warps in a block.

  4. Single-instruction multiple threaded is execution where a set of instructions are dispatched in a multi-threaded manner.

  5. Compute capability determines the general specifications and available features of a GPU.

  6. The selected datasets had a variety of characteristics (e.g., dimensionality and number of records) that could reduce biasness in the experiments. These datasets also worked well with the program prototypes this research developed for experiments.

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Authors and Affiliations

  1. International Centre of Insect Physiology and Ecology, Nairobi, Kenya

    Kennedy Senagi

  2. LIASD, Université Paris8, Paris, France

    Nicolas Jouandeau

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  1. Kennedy Senagi
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  2. Nicolas Jouandeau
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Correspondence to Kennedy Senagi.

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Senagi, K., Jouandeau, N. Parallel construction of Random Forest on GPU. J Supercomput 78, 10480–10500 (2022). https://doi.org/10.1007/s11227-021-04290-6

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