Massive-Scale Gaze Analytics Exploiting High Performance Computing

被引:0
|
作者
Duchowski, Andrew T. [1 ]
Bolte, Takumi [1 ]
Krejtz, Krzysztof [2 ]
机构
[1] Clemson Univ, Sch Comp, Clemson, SC 29634 USA
[2] Univ Social Sci & Human, Natl Informat Proc Inst, Warsaw, Poland
来源
INTELLIGENT DECISION TECHNOLOGIES | 2015年 / 39卷
关键词
High-performance computing; Eye tracking; Gaze analytics; DIFFERENTIATION;
D O I
10.1007/978-3-319-19857-6_13
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Methods for parallelized eye movement analysis on a cluster are detailed. The distributed approach advocates the single-core job programming strategy, assigning processing of eye movement data across as many cluster cores as are available. A foreman-worker distribution algorithm takes care of job assignment via the Message Passing Interface (MPI) available on most high-performance computing clusters. Two versions of the MPI algorithm are presented, the first a straightforward implementation that assumes faultless operation, the second a more fault-tolerant revision that gives nodes an opportunity of communicating failure. Job scheduling is also briefly explained.
引用
收藏
页码:137 / 147
页数:11
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