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Dynamic Buffer Management in Massively Parallel Systems: The Power of Randomness
Minh Pham1, Yongke Yuan2, Hao Li1
1University of South Florida, USA.
ACM Transactions on Parallel Computing
|February 24, 2025
Summary
This study introduces a novel dynamic memory allocation method for Graphics Processing Units (GPUs) using random search, significantly reducing latency and improving performance for parallel computing tasks.
Area of Science:
- Computer Science
- High-Performance Computing
- System Software
Background:
- Massively parallel systems like Graphics Processing Units (GPUs) are vital for data-intensive computing.
- Developing efficient system software for numerous parallel threads presents unique challenges.
- Traditional dynamic memory allocators struggle with global data structure bottlenecks in parallel environments.
Purpose of the Study:
- To propose a novel dynamic memory allocation approach for massively parallel systems that avoids centralized data structures.
- To enhance memory allocation efficiency and reduce latency in GPU computing.
- To improve the performance of GPU algorithms through advanced memory management.
Main Methods:
- Developed a dynamic memory allocation strategy utilizing random search procedures for threads to locate free memory pages.
- Designed advanced techniques to address warp divergence and optimize performance under limited free memory conditions.
- Integrated and evaluated the proposed memory management techniques within GPU algorithms like hash join and group-by.
Main Results:
- The basic random search design demonstrated lower latency compared to existing solutions in most scenarios.
- Advanced techniques achieved an order of magnitude improvement over the basic design.
- The state-of-the-art was consistently outperformed by up to two orders of magnitude in performance gains.
- Case studies showed pronounced performance improvements in GPU hash join and group-by algorithms.
Conclusions:
- The proposed random search-based dynamic memory allocation effectively eliminates centralized data structure bottlenecks in massively parallel systems.
- Advanced techniques offer substantial performance enhancements, significantly outperforming current state-of-the-art memory management solutions.
- The practical integration into GPU algorithms validates the approach's effectiveness and broad applicability in high-performance computing.
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