How pradyumna-chacham/gespmm-retuned works
GE-SpMM Retuned is a work-in-progress project for benchmarking and retuning CSR sparse–dense matrix multiplication for graph workloads. The README describes a planned workflow: run SpMM across GPU and CPU implementations, compare against library baselines, and study performance across matrix density and degree skew. It also plans per-matrix autotuning, a learned selector, and a PyTorch integration. The tree contains only a GPU hello-world source file; implementation paths and wiring for the planned capabilities are not present, so those relationships are not inferred.
Components 11
GPU SpMM
- CUDA kernels
- cuSPARSE baseline
- GPU hello demo
gpu/hello_gpu.cu
CPU SpMM
- OpenMP SpMM
- MKL baseline
Tuning and Study
- Synthetic matrices
- Kernel autotuning
- Learned selector
Integrations
- GCN benchmark
- PyTorch custom op
Other components
- GNN workload