Dynamic memory management is critical for efficiently porting modern data processing pipelines to GPUs. However, building a general-purpose dynamic memory manager on GPUs is challenging due to the massive parallelism and weak memory coherence. Existing state-of-the-art GPU memory managers, Ouroboros and Reg-Eff, employ traditional data structures such as arrays and linked lists to manage memory objects. They build specialized pipelines to achieve performance for a fixed set of allocation sizes and fall back to the CUDA allocator for allocating large sizes. In the process, they lose general-purpose usability and fail to support critical applications such as streaming graph processing.
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McCoy et al. (2024) studied this question.
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