Matrix Core Programming on AMD CDNA Architecture
16 days ago
- #AMD CDNA
- #Matrix Core
- #Low-Precision
- Matrix Cores in AMD CDNA™3 and CDNA™4 architectures accelerate matrix fused-multiply-add (MFMA) operations, essential for AI and HPC workloads.
- Mixed-precision mode using lower-precision data types (FP16, FP8, FP6, FP4) with FP32 output significantly boosts performance, with CDNA™4 offering up to 64x speedup over FP32.
- Low-precision floating-point types (e.g., E4M3, E5M2) are characterized by exponent and mantissa widths, with specific formats like E4M3FN and E4M3FNUZ differing in exponent bias and special value support.
- MFMA instructions vary by matrix dimensions and data types, with CDNA™4 introducing block exponent scaling and new FP6/FP4 support.
- Compiler intrinsics (e.g., __builtin_amdgcn_mfma_f32_32x32x2f32) enable Matrix Core programming in HIP kernels, requiring understanding of operand distribution across wavefront threads.
- Practical examples demonstrate MFMA operations with FP32, FP16, FP8, and FP4, including data layouts and HIP kernel implementations.
- Theoretical peak performance for MFMA operations can be calculated using matrix dimensions, cycle counts, and GPU specifications.
- CDNA™4 introduces new MFMA instructions with block exponent scaling, supported by intrinsics like __builtin_amdgcn_mfma_scale_f32_32x32x64_f8f6f4.