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The SIMT execution model is commonly used for general GPU development. CUDAand OpenCL developers write scalar code that is implicitly parallelized bycompiler and hardware. On Intel GPUs, however, this abstraction has profoundperformance implications as the underlying ISA is SIMD and important hardwarecapabilities cannot be fully utilized. To close this performance gap weintroduce C-For-Metal (CM), an explicit SIMD programming framework designed todeliver close-to-the-metal performance on Intel GPUs. The CM programminglanguage and its vector/matrix types provide an intuitive interface to exploitthe underlying hardware features, allowing fine-grained register management,SIMD size control and cross-lane data sharing. Experimental results show thatCM applications from different domains outperform the best-known SIMT-basedOpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.

C-for-Metal: High Performance SIMD Programming on Intel GPUs