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In this paper we introduce LDBC Graphalytics, a new industrial grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds(More)
With the emergence of data science, graph computing is becoming a crucial tool for processing big connected data. Although efficient implementations of specific graph applications exist, the behavior of full-spectrum graph computing remains unknown. To understand graph computing, we must consider multiple graph computation types, graph frameworks, data(More)
Processing in Memory (PIM) was first proposed decades ago for reducing the overhead of data movement between core and memory. With the advances in 3D-stacking technologies, recently PIM architectures have regained researchers' attentions. Several fully-programmable PIM architectures as well as programming models were proposed in previous literature.(More)
Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the(More)
Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing systems have emerged targeting various perspectives of a graph application such as in memory and on disk representations, persistent storage, database capability, runtimes and(More)
Architecture simulation for GPGPU kernels can take a significant amount of time, especially for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on profiling-based sampling for GPGPU kernels to reduce the cycle-level simulation time. Compared to existing approaches, TBPoint provides a flexible and architecture-independent way(More)
Recommendation systems using graph collaborative filtering often require responses in real time and high throughput. Therefore, besides recommendation accuracy, it is critical to study high performance concurrent collaborative filtering on modern platforms. To achieve high performance, we study the graph data locality characteristics of collaborative(More)
—Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the(More)