feat: batched complex GEMM (C = alpha*A*B + beta*C)

[shinaoka]

Summary

Add batched complex GEMM support using 4-real decomposition: Cr = Ar*Br - Ai*Bi, Ci = Ar*Bi + Ai*Br.

Background

Complex GEMM is essential for scientific computing (quantum mechanics, signal processing). cuBLAS provides cgemm/zgemm with Tensor Core support, but a fallback is needed for portability.

Approach

• 4-real decomposition: Complex GEMM → 4 real GEMMs, each using existing CMMA/MMA tile pipeline
• Tile-level implementation in cubek-matmul (separate repo): decompose complex tiles into real tiles
• Scalar register fallback for hardware without Tensor Core complex support
• Batched: support [B, M, K] × [B, K, N] → [B, M, N] with broadcasting

Scope

• cubek-matmul: Add MatmulPrecision impl for Complex types, complex-aware tile routines
• cubecl (this repo): Ensure CMMA load/store work with interleaved complex layout
• Alpha/beta scaling support

References

• Design doc: docs/plans/2026-04-14-complex-design.md
• PyTorch uses interleaved complex + cuBLAS cgemm/zgemm directly
• cuBLAS complex GEMM: 1 call with Tensor Cores vs 4 real GEMMs

Dependencies

• feat: add interleaved complex number support (Complex32/64) #2 (complex number IR + CUDA backend + frontend)