Reviews: Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale

The paper is well written, and its structure is adapted to the content. Upon reading the paper, one might think that the contribution resides in the vertical splitting of the data over the workers, but the state of the art study presented later on shows that this idea by itself is not new. The novelty comes from associating it with data also distributed vertically, sparse bit vectors for inter-node communications, feature compression with custom data structures and training on compressed data. The paper shows formally and experimentally how the proposed heuristics significantly improve the communication between the nodes and speed up training. The remark that using run-length encoding for the features allows them to hold in the L3 cache, thus decreasing the number of DRAM accesses, doesn't seem to always be true. The paper should explain in which conditions this is true (size of the cache, size of the data, number and type of features, etc.).