ggml-org/llama.cpp • PR #29245Score: 9/10
sycl: add grouped MoE XMX GEMM
Language: C+++1358 -16performance
concepts demonstrated
Grouped matrix multiplicationData dequantizationSYCL joint_matrix APIHardware-accelerated tensor operations
The Issue & Context
MoE models process each expert with a separate GEMM operation, but these small, narrow activations underutilize compute units and cause inefficient dequantization.
Root Cause Analysis
Handling each expert individually leads to poor hardware utilization and overhead due to many small GEMM calls and dequantization steps.
Solution Strategy & Architectural Pattern
Implemented a grouped GEMM kernel that batches multiple experts into a single operation using XMX joint_matrix, dequantizing weights into f16 tiles and packing activations efficiently.
Key Takeaway for Contributors
Batching small compute tasks into grouped kernels can significantly boost throughput on specialized hardware by improving utilization and reducing overhead.