- Chun Waan Loke
- Peter Chong
- Clarice Osmond
- Zhilong Li
Tree Boosting In A Nutshell
Split Finding Algorithms
Column Block for Parallel Learning
The proposed block structure optimizes the computation complexity but requires indirect fetches of gradient statistics by row index. XGBoost optimizes the process by using the following methods.
- For the exact greedy algorithm, XGBoost uses a cache-aware prefetching algorithm
- It stores Gradient and Hessians in the cache to make calculations fast
- It runs twice as fast as the naive method when the dataset is large
- For approximate algorithms, XGBoost chooses a specific block size
- Choosing a small block size results in inefficient parallelization
- Choosing a large block size results in cache misses
- Various choices of block size are compared and the results are shown in Figure 9
- XGBoost chose 2^16 as the block size
Blocks for Out-of-core Computation
When the dataset is too large for the cache and main memory, XGBoost utilizes disk spaces as well. Since reading and writing data to the disks are slow, XGBoost optimizes the process by using the following two methods.
- Block Compression
- Blocks are compressed by columns
- Although decompressing takes time, it is still faster than reading from the disks
- Block Sharding
- If multiple disks are available, data is split into those disks
- When the CPU needs data, all the disks can be read at the same time
End To End Evaluations
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