HRPF: A parallel programming framework for recursive algorithms on heterogeneous CPU–GPU systems

Yizhuo Wang; Bowen Liu; Senhao Shao; Jianhua Gao; Weixing Ji; Hongbo Xing

doi:10.1016/j.parco.2026.103187

February 6, 2026Parallel Computing

HRPF: A parallel programming framework for recursive algorithms on heterogeneous CPU–GPU systems

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Key Points

The framework enhances parallel programming capabilities with recursive algorithms, improving computational efficiency.
Key evidence shows that HRPF significantly reduces execution time, especially for complex tasks involving large datasets.
Analysis of heterogeneous systems comprising CPU and GPU resources highlights effective resource utilization.
This approach may enable faster computational solutions, but external validation in real-world applications is necessary.

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Implication

Observational analysis reveals the efficiency of recursive algorithms in heterogeneous CPU–GPU systems, indicating potential for improved performance.

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Wang et al. (2026) studied this question.

synapsesocial.com/papers/69a767a5badf0bb9e87e1c3f https://doi.org/10.1016/j.parco.2026.103187

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HRPF: A parallel programming framework for recursive algorithms on heterogeneous CPU–GPU systems

Key Points

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Implication

Discussion

Cite This Study

HRPF: A parallel programming framework for recursive algorithms on heterogeneous CPU–GPU systems

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

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