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4 changes: 2 additions & 2 deletions docs/algo/sona/pagerank_on_sona_en.md
Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,8 @@ We implemented large-scale PageRank calculation based on Spark On Angel, where p

### Resource parameters

- Angel PS number and memory: The product of ps.instance and ps.memory is the total configuration memory of ps. In order to ensure that Angel does not hang, you need to configure memory about twice the size of the model. For PageRank, the calculation formula of the model size is: number of nodes * 3 * 4 Byte, according to which you can estimate the size of ps memory that needs to be configured under Graph input of different sizes
- Spark的资源配置:The product of num-executors and executor-memory is the total configuration memory of executors, and it is best to store twice the input data. If the memory is tight, 1 times is also acceptable, but relatively slower. For example, a 10 billion edge set is about 160G in size, and a 20G * 20 configuration is sufficient. In a situation where resources are really tight, try to increase the number of partitions!
- Angel configurations of PS number and memory: The product of ps.instance and ps.memory is the total configuration memory of ps. In order to ensure that Angel does not hang, you need to configure memory about twice the size of the model. For PageRank, the calculation formula of the model size is: number of nodes * 3 * 4 Byte, according to which you can estimate the size of ps memory that needs to be configured under Graph input of different sizes
- Spark configurations:The product of num-executors and executor-memory is the total configuration memory of executors, and it is best to store twice the input data. If the memory is tight, 1 times is also acceptable, but relatively slower. For example, a 10 billion edge set is about 160G in size, and a 20G * 20 configuration is sufficient. In a situation where resources are really tight, try to increase the number of partitions!

### Submitting scripts
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