HPC Simulations of Information Propagation Over Social Networks

Jin, Jiangming; Turner, Stephen J.; Lee, Bu-Sung; Zhong, Jianlong; He, Bingsheng

doi:10.1016/j.procs.2012.04.031

Cited by 13 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More details on this adaptation can be found in our paper [11]. We also experiment with synthetic and real world graphs with varying sizes and obtained consistent speedups [11,13].…”

Section: Implementations and Evaluationsmentioning

confidence: 63%

“…In contrast, the cost of a E-Loop stays stable in different iterations. Compared to the CPU serial simulation performance, the C2050 GPU based simulation shows 12.5x, 13.1x, 15.6x speedup with CLoop, T-Loop, and E-Loop, respectively [11].…”

Section: Implementations and Evaluationsmentioning

confidence: 97%

“…Based on the ICM and LTM models (with adaptions to our application scenarios [11]), we first introduce two types of simulation algorithms named as C-Loop and T-Loop. 1) C-Loop: Starting from the active vertices in the network, each active vertex goes through its neighbors at each simulation step and tests whether it can propagate the information to the inactive neighbors with a specific probability.…”

Section: Implementations and Evaluationsmentioning

confidence: 99%

“…This paper gives an overview of Medusa based on work reported in [20,18,11,12,13]. We first present the design of the main components of Medusa's system architecture and evaluation results.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Medusa

Zhong

2014

SIGMOD Rec.

Self Cite

View full text Add to dashboard Cite

Medusa is a parallel graph processing system on graphics processors (GPUs). The core design of Medusa is to enable developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime system of Medusa automatically executes the user-defined APIs in parallel on the GPU, with a series of optimizations based on the architecture features of GPUs and characteristics of graph applications. In this paper, we present an overview of the Medusa system and a case study of adopting Medusa to a research project on social network simulations. With Medusa, users without GPU programming experience can quickly implement their graph operations on the GPU, which accelerates the discovery and findings of domain-specific applications.

show abstract

“…More details on this adaptation can be found in our paper [11]. We also experiment with synthetic and real world graphs with varying sizes and obtained consistent speedups [11,13].…”

Section: Implementations and Evaluationsmentioning

confidence: 63%

Section: Implementations and Evaluationsmentioning

confidence: 97%

Section: Implementations and Evaluationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Medusa

Zhong

2014

SIGMOD Rec.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We show how the optimizations and APIs of Medusa facilitated the study in terms of improving simulation performance and reducing the programming effort. Results and findings of the case study are published in [40][41][42].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Parallel graph processing on graphics processing units

Zhong¹

Self Cite

View full text Add to dashboard Cite

First, I would like to express my sincere thanks to my supervisor Dr. HE Bingsheng for his remarkable guidance and generous help over the past four years. His expertise and invaluable advice has left a profound impact on my research career, especially the way I think of and solve new problems. His self-discipline and diligence has taught me how to bring out the best of myself under limited resources. I'm especially grateful for the great patience he showed when I had little progress, made mistakes, or even worked against him. I appreciate my colleagues of Xtra Group for their help and companionship along the way. I'm honored to be the first student member of Xtra group and work with the following

show abstract

A Social Endorsing Mechanism for Mobile Coupons

Liou

2017

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

HPC Simulations of Information Propagation Over Social Networks

Cited by 13 publications

References 13 publications

Medusa

Medusa

Parallel graph processing on graphics processing units

A Social Endorsing Mechanism for Mobile Coupons

Contact Info

Product

Resources

About