cpu.als: CPU usage metrics for distributed ALS algorithm
In jairsonrodrigues/gama: Genetic Approach to Maximize Clustering Criterion
Description
Usage
Format
Source
References
Description
Consumption metrics gathered during an execution of the Distributed Machine Learning algorithm Alternating Least Squares (ALS) in an eigth-node cluster, by using the Spark framework.
Usage
1
Format
A data frame containing 308 observations and four dimensions:
user: CPU usage by the algorithm
system: CPU usage spent by Operating System (O.S.)
iowait: waiting time for Input/Output (I/O) operations
softirq: CPU time spent by software interrupt requests
The values comprise the domain from 0 to 100, for all dimensions. The dataset contains zero-values, however there is no missing or null values.
** A spark cluster of N nodes has 1 (one) master node and N-1 slave nodes.
Source
The data was measured and collected by the author by using Intel HiBench benchmark framework in a eigth-node Spark cluster hosted in Google Cloud DataProc engine. Each node had 16-core CPU annd 106 GB RAM. The algorithm Alternating Least Squares had consumed 3.7 min of runtime to execute over a sinteticaly generated dataset totalizing 1.68 Gigabytes.
References
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Commun. ACM, vol. 35, no. 12, pp. 61???70, 1992.
Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer (Long. Beach. Calif)., vol. 42, no. 8, 2009.
Y. Hu, Y. Koren, and C. Volinsky, Collaborative filtering for implicit feedback datasets, in Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, 2008, pp. 263???272.
S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang, The HiBench benchmark suite: Characterization of the MapReduce-based data analysis, in 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010), 2010, pp. 41???51.
jairsonrodrigues/gama documentation built on May 17, 2019, 3:12 a.m.
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Description Usage Format Source References
Description
Consumption metrics gathered during an execution of the Distributed Machine Learning algorithm Alternating Least Squares (ALS) in an eigth-node cluster, by using the Spark framework.
Usage
1 |
Format
A data frame containing 308 observations and four dimensions:
user: CPU usage by the algorithm
system: CPU usage spent by Operating System (O.S.)
iowait: waiting time for Input/Output (I/O) operations
softirq: CPU time spent by software interrupt requests
The values comprise the domain from 0 to 100, for all dimensions. The dataset contains zero-values, however there is no missing or null values.
** A spark cluster of N nodes has 1 (one) master node and N-1 slave nodes.
Source
The data was measured and collected by the author by using Intel HiBench benchmark framework in a eigth-node Spark cluster hosted in Google Cloud DataProc engine. Each node had 16-core CPU annd 106 GB RAM. The algorithm Alternating Least Squares had consumed 3.7 min of runtime to execute over a sinteticaly generated dataset totalizing 1.68 Gigabytes.
References
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Commun. ACM, vol. 35, no. 12, pp. 61???70, 1992.
Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer (Long. Beach. Calif)., vol. 42, no. 8, 2009.
Y. Hu, Y. Koren, and C. Volinsky, Collaborative filtering for implicit feedback datasets, in Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, 2008, pp. 263???272.
S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang, The HiBench benchmark suite: Characterization of the MapReduce-based data analysis, in 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010), 2010, pp. 41???51.
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