
torch.sparse.mm with reduce flag: performance hotspot in message-passing when the edge index is stored in compressed sparse row (CSR). Supported reduce flag for: sum, mean, amax, amin.

End-to-end performance benchmark results for both inference and training on 3rd Gen Intel® Xeon® Scalable processors 8380 platform and on 4th Gen 8480+ platform are discussed in Accelerating PyG on Intel CPUs.
Optimize int8 Inference with Unified Quantization Backend for x86 CPU Platforms
The new X86 quantization backend is a combination of FBGEMM (Facebook General Matrix-Matrix Multiplication) and oneAPI Deep Neural Network Library (oneDNN) backends and replaces FBGEMM as the default quantization backend for x86 platforms. The result: better end-to-end int8 inference performance than FBGEMM.