.png){kind=icon}
返回
Optimizing GPU-Based Graph Sampling and Random Walk for Efficiency and Scalability
.png){kind=icon}
PRE
AI
DOI：10.1109/TC.2023.3251860.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph sampling and random walk algorithms are playing increasingly important roles today because they can significantly reduce graph size while preserving structural information, thus enabling computationally intensive tasks on large-scale graphs. Current frameworks designed for graph sampling and random walk tasks are generally not efficient in terms of memory requirement and throughput. Not to mention that some of them result in biased results. To solve the above problems, we introduce Skywalker+, a high-performance graph sampling and random walk framework on multiple GPUs supporting multiple algorithms. Skywalker+ makes four key contributions: First, it realizes highly paralleled alias method on GPUs. Second, it applies finely adjusted workload-balancing techniques and locality-aware execution modes to present a highly efficient execution engine. Third, it optimizes the GPU memory usage with efficient buffering and data compression schemes. Last, it scales to multi-GPU to further enhance the system throughput. Abundant experiments show that Skywalker+ exhibits significant advantage over the baselines both in performance and utility.
Keyword:
Task analysis
Graphics processing units
Throughput
Optimization
Scalability
Parallel processing
Memory management
GPU graph sampling
graph random walk
scalability
throughput
期刊
IF:
3.8
论文数:
5.4K
被引数:
9.8K
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏
查看更多内容