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Graphs are a powerful tool for data representation in a wide range of domains like social, biological, informational, etc. But their extremely large sizes often makes it computationally infeasible to study the entire graphs. Graph sampling provides a solution by generating smaller subgraphs which are computationally feasible to analyze and can be used to infer the properties of the entire graph. In this work, we develop a high throughput parallel implementation of Totally Induced Edge Sampling (TIES) algorithm on FPGA. Prior research has shown that TIES performs better than other sampling techniques in terms of preserving the topological properties of the original graph, and thus generates better quality subgraphs. The algorithm randomly samples the edges and inserts the corresponding vertices into the sampled vertex set until the desired number of vertices are sampled. Then, the edges connecting the sampled vertices are included in the sampled subgraph. We use multiple parallel pipelines to achieve high throughput and faster graph sampling. The parallel pipelines need to access a global dynamic data structure which contains the vertices sampled thus far. To support this, we develop a novel dynamic hash table data structure which supports parallel accesses in each clock cycle. We vary the number of pipelines, the size of the sampled subgraph and analyze the performance of the design in terms of on-chip FPGA resource utilization, throughput and total execution time. Our design achieves a throughput as high as 2471 Million Edges Per Second (MEPS) and performs 3.6x better than the state-of-the-art multi-core design.

Parallel Totally Induced Edge Sampling on FPGAs1