PolarStar: Expanding the Scalability Horizon of Diameter-3 Networks

In this paper, we present PolarStar, a novel family of diameter-3 network topologies derived from the star product of two low-diameter factor graphs. The proposed PolarStar construction gives the largest known diameter-3 network topologies for almost all radixes. When compared to state-of-the-art diameter-3 networks, PolarStar achieves 31% geometric mean increase in scale over Bundlefly, 91% over Dragonfly, and 690% over 3-D HyperX. PolarStar has many other desirable properties including a modular layout, large bisection, high resilience to link failures and a large number of feasible sizes for every radix. Our evaluation shows that it exhibits comparable or better performance than other diameter-3 networks under various traffic patterns.Comment: 13 pages, 13 figures, 4 table

A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network

Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate the first real-world SF installation. We focus on deployment, management, and operational aspects of our test cluster with 200 servers and carefully analyze performance. We demonstrate techniques for simple cabling and cabling validation as well as a novel high-performance routing architecture for InfiniBand-based low-diameter topologies. Our real-world benchmarks show SF's strong performance for many modern workloads such as deep neural network training, graph analytics, or linear algebra kernels. SF outperforms non-blocking Fat Trees in scalability while offering comparable or better performance and lower cost for large network sizes. Our work can facilitate deploying SF while the associated (open-source) routing architecture is fully portable and applicable to accelerate any low-diameter interconnect

High-Performance Graph Databases That Are Portable, Programmable, and Scale to Hundreds of Thousands of Cores

Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness established practices from the HPC landscape to build a system that outperforms all past GDBs presented in the literature by orders of magnitude, for both OLTP and OLAP workloads. For this, we first identify and crystallize performance-critical building blocks in the GDB design, and abstract them into a portable and programmable API specification, called the Graph Database Interface (GDI), inspired by the best practices of MPI. We then use GDI to design a GDB for distributed-memory RDMA architectures. Our implementation harnesses one-sided RDMA communication and collective operations, and it offers architecture-independent theoretical performance guarantees. The resulting design achieves extreme scales of more than a hundred thousand cores. Our work will facilitate the development of next-generation extreme-scale graph databases