The llama.cpp project has updated its SYCL backend to fuse RMS norm, multiplication, and addition operations under the GGML_SYCL_ENABLE_FUSION flag. ^[1] KernelFoundry achieved an average speedup of 2.3 on KernelBench for SYCL kernels compared to prior work. ^[2] The authors introduce KernelFoundry, an evolutionary framework designed to optimize GPU kernels by understanding hardware architecture and parallel computing strategies. ^[3] Supported data types for the fused operations include f32, f16, f16/f32, i32, i16, and bf16, covering both broadcast and non-contiguous memory layouts. ^[4] The optimization extends to fusing ADD+ADD residual chains using the same binbcast indexing and type matrix as the standalone add() function. ^[5] KernelFoundry includes a template-based parameter optimization approach to tune kernels to specific inputs and hardware configurations. ^[6] KernelFoundry utilizes MAP-Elites quality diversity search with kernel-specific behavioral dimensions to sustain exploration of the GPU kernel space. ^[7] Unsupported combinations of operations or data types will automatically fall back to launching two separate add() functions. ^[8]