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Matrix Core Programming on AMD CDNA3 and CDNA4 Architecture

a day ago
https://salykova.github.io/matrix-cores-cdna
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  • #AMD CDNA
  • #Low-Precision Computing
  • #Matrix Cores
  • Matrix Cores in AMD CDNA3 and CDNA4 architectures accelerate matrix fused-multiply-add (MFMA) operations, essential for AI and HPC workloads.
  • Performance gains are significant in mixed-precision mode, with lower-precision input matrices (FP16, FP8, FP6, FP4) and FP32 output to minimize accuracy loss.
  • AMD CDNA4 introduces new low-precision data types (FP6, FP4) and block exponent scaling, enabling up to 64x performance gain compared to FP32.
  • Compiler intrinsics like __builtin_amdgcn_mfma_f32_32x32x16_fp8_fp8 allow programming Matrix Cores in HIP kernels, with operands distributed across wavefront threads.
  • Data layouts for MFMA instructions vary by matrix dimensions and precision types, requiring specific memory access patterns for optimal performance.
  • Examples include HIP kernel implementations for FP32, FP16, FP8, and FP4 MFMA operations, demonstrating operand distribution and scaling factors.
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