• Corpus ID: 204904742

Semi-Asymmetric Parallel Graph Algorithms for NVRAMs

@article{Dhulipala2019SemiAsymmetricPG,
  title={Semi-Asymmetric Parallel Graph Algorithms for NVRAMs},
  author={Laxman Dhulipala and Charles McGuffey and Hong Kyu Kang and Yan Gu and Guy E. Blelloch and Phillip B. Gibbons and Julian Shun},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.12310}
}

• Laxman Dhulipala, Charles McGuffey, +4 authors Julian Shun
• Published 27 October 2019
• Computer Science
• ArXiv

Emerging non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density. Systems are likely to have an order of magnitude more NVRAM than traditional memory (DRAM), allowing large graph problems to be solved efficiently at a modest cost on a single machine. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be significantly more expensive than NVRAM reads.
In this paper, we propose an approach to parallel graph analytics in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits.
We present a model, the Parallel Semi-Asymmetric Model (PSAM), to analyze algorithms in the setting, and run experiments on a 48-core NVRAM system to validate the effectiveness of these algorithms. To this end, we study over a dozen graph problems. We develop parallel algorithms for each that are efficient, often work-optimal, in the model. Experimentally, we run all of the algorithms on the largest publicly-available graph and show that our PSAM algorithms outperform the fastest prior algorithms designed for DRAM or NVRAM. We also show that our algorithms running on NVRAM nearly match the fastest prior algorithms running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.