利用HPX运行时系统克服分布式图算法的延迟限制
Overcoming Latency-bound Limitations of Distributed Graph Algorithms using the HPX Runtime System
AI总结：
本文利用HPX运行时系统开发了高效的分布式图算法原型，通过异步执行和细粒度并行性显著提升了性能，克服了传统框架的延迟限制。
摘要：
大规模图处理面临着许多挑战，包括图的不规则结构、图算法的延迟性质以及分布式执行相关的开销。尽管现有的框架如Spark GraphX和并行Boost图库（PBGL）已经引入了分布式图处理的抽象，但它们仍然难以克服固有的问题，如负载不平衡和同步开销。在本工作中，我们提出了一种分布式库原型和三种关键图算法——广度优先搜索（BFS）、PageRank和三角形计数的分布式实现，使用NWgraph库中的C++机制，并利用HPX的分布式容器和异步构造。这些算法涵盖遍历、中心性和模式匹配三大类别，并被选为具有多样化计算特征的代表。我们评估了基于HPX的实现与GraphX和PBGL的对比，显示一个高性能的运行时如HPX能够通过利用异步执行、延迟隐藏和细粒度并行性在共享内存中构建出显著优于传统框架的算法。我们原型中的所有算法都遵循统一的执行模型，在其中局部和远程计算使用相同的编程抽象，异步性由运行时透明管理。这种设计明确地利用了每个局部性内的共享内存并行性，同时在局部性之间重叠通信和计算，为将此方法扩展到更广泛的分布式图算法类提供了实用的基础。
英文摘要：
Graph processing at scale presents many challenges, including the irregular structure of graphs, the latency-bound nature of graph algorithms, and the overhead associated with distributed execution. While existing frameworks such as Spark GraphX and the Parallel Boost Graph Library (PBGL) have introduced abstractions for distributed graph processing, they continue to struggle with inherent issues like load imbalance and synchronization overhead. In this work, we present a distributed library prototype and a distributed implementation of three key graph algorithms - Breadth-First Search (BFS), PageRank, and Triangle Counting, using C++ mechanisms from the NWgraph library and leveraging HPX's distributed containers and asynchronous constructs. These algorithms span the categories of Traversal, centrality, and Pattern matching, and are selected to represent diverse computational characteristics. We evaluate our HPX-based implementations against GraphX, and PBGL, showing that a high-performance runtime such as HPX enables the construction of algorithms that significantly outperform conventional frameworks by exploiting asynchronous execution, latency hiding, and fine-grained parallelism in shared memory. All algorithms in our prototype follow a unified execution model in which local and remote computations are expressed using the same programming abstractions, with asynchrony managed transparently by the runtime. This design explicitly leverages shared-memory parallelism within each locality while overlapping communication and computation across localities, providing a practical foundation for extending this approach to a broader class of distributed graph algorithms.
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