In the technical report (https://scnakandala.github.io/papers/TR_2020_Hummingbird.pdf https://scnakandala.github.io/papers/TR_2020_Hummingbird.pdf) table 8 we c

In the technical report (https://scnakandala.github.io/papers/TR_2020_Hummingbird.pdf https://scnakandala.github.io/papers/TR_2020_Hummingbird.pdf) table 8 we compared against NVIDIA FIL library. From their blogpost (https://medium.com/rapids-ai/rapids-forest-inference-library-prediction-at-100-million-rows-per-second-19558890bc35 https://medium.com/rapids-ai/rapids-forest-inference-library...) they claim to be faster than the original XGB\LGBM GPU implementation, so we decided to pick FIL as GPU baseline. Regarding the GEMM strategy: you are right, it only works for shallow trees. In Figure 7 we compared GEMM against the other strategies implementing typical tree traversal. At some point we had an implementation using sparse GEMM, but only for sparse input feature vectors.