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- Alexandru Iosup, Tim Hegeman, +11 authors Peter A. Boncz
- PVLDB
- 2016

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)

- Yinglong Xia, Ilie Gabriel Tanase, +4 authors Ching-Yung Lin
- 2014 IEEE International Conference on Big Data…
- 2014

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)

- Lifeng Nai, Hyesoon Kim
- MEMSYS
- 2015

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)

- Ilie Gabriel Tanase, Yinglong Xia, +4 authors Ching-Yung Lin
- GRADES
- 2014

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)

- Jen-Cheng Huang, Lifeng Nai, Hyesoon Kim, Hsien-Hsin S. Lee
- 2014 IEEE 28th International Parallel and…
- 2014

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)

- Lifeng Nai, Yinglong Xia, Ching-Yung Lin, Bo Hong, Hsien-Hsin S. Lee
- Conf. Computing Frontiers
- 2014

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)

- Yinglong Xia, Jui-Hsin Lai, Lifeng Nai, Ching-Yung Lin
- ICME Workshops
- 2014

- Lifeng Nai, Ramyad Hadidi, Jaewoong Sim, Hyojong Kim, Pranith Kumar, Hyesoon Kim
- 2017 IEEE International Symposium on High…
- 2017

With the emergence of data science, graph computing has become increasingly important these days. Unfortunately, graph computing typically suffers from poor performance when mapped to modern computing systems because of the overhead of executing atomic operations and inefficient utilization of the memory subsystem. Meanwhile, emerging technologies, such as… (More)

- Yinglong Xia, Ilie Gabriel Tanase, +4 authors Ching-Yung Lin
- 2014

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)