Hi I noticed the PR #5602 has been merged to the main branch. Thank you!

If I were to compare the performance between Li2018 and Anderson2021, how do I configure machine_param=n_threads,cache_size,compute_cache_tradeoff accordingly to equalize the autoscheduler environment?

Similarly, how do I configure the machine_param to compare the performance between Mullapudi2016 and Anderson2021?

To clarify, the current version of Maullapudi2016 can only generate CPU schedules. But, the original paper suggested a manual intervention step to generate GPU schedules. That is, to edit the generated *.schedule.h code to replace all .vectorize(...).parallel(...) with gpu_threads(...).gpu_tiles(...) in accordance to the following rules. In the past, I found it highly effective for algorithms having less than ~5 Halide stages.

We configure the auto-scheduler to target the GPU by setting the PARALLELISM_THRESHOLD to 128, VECTOR_WIDTH to 32, and
CACHE_SIZE to 48 KB. Additionally, we add two new parameters
TARGET_THREADS_PER_BLOCK and MAX_THREADS_PER_BLOCK whose values are set to 128 and 2048 respectively. These parameters enable the
auto-scheduler to avoid tiling configurations that generate too few
or too many threads per GPU thread block. The inlining, tiling, and
grouping processes are otherwise similar to the CPU case. Groups
resulting from merging are mapped to CUDA kernels by designating
the outer tile loops as GPU block grid dimensions and the inner tile
loops as GPU thread block dimensions. All intermediate buffers
within a group are allocated in GPU shared memory.
http://graphics.cs.cmu.edu/projects/halidesched/