HIP/ROCm AMD GPU Programming: CUDA Portability and MI300X — enabling GPU-agnostic code and AMD CDNA acceleration

HIP (Heterogeneous Interface for Portability) enables single-source GPU code compiling to both NVIDIA (via CUDA) and AMD (via HIP runtime) backends. ROCm is AMD's open-source GPU compute stack, providing compilers, libraries, and runtime.

HIP Language and CUDA Compatibility

HIP shares CUDA's syntax and semantics: kernels, shared memory, atomic operations, and synchronization primitives are nearly identical. hipify-perl and hipify-clang automate CUDA→HIP porting via string replacement and AST transformation. Successful conversion rate exceeds 95% for CUDA codebases. hipMemcpy, hipMemset, and stream operations correspond directly to CUDA equivalents, enabling straightforward library porting.

ROCm Software Stack

ROCm includes: HIPCC compiler (HIP→AMDGPU ISA), rocBLAS (dense linear algebra), rocFFT (FFT), rocSPARSE (sparse operations), MIOpen (deep learning kernels), HIP runtime (kernel execution, memory management), rocProfiler (performance analysis), rocDEBUG (debugger). Open-source nature enables community contributions and modifications unavailable in NVIDIA's proprietary stack.

AMD GPU Architecture: RDNA vs CDNA

RDNA (Radeon NAVI, compute-focused consumer GPUs) features compute units (CUs) with 64-wide wave64 execution and 256 KB LDS per CU. CDNA (MI100, MI200, MI300X—datacenter) emphasizes matrix operations: 4-wide matrix units (bf16, fp32), enhanced cache hierarchies (32 MB L2), higher memory bandwidth (HBM3). MI300X (2025) provides 192 GB HBM3 (Instinct GPU) or 256 GB HBM3e system (CPU+GPU combined die).

Roofline Model for AMD

AMD MI300X theoretical peak: 383 TFLOPS (fp32), 766 TFLOPS (mixed precision), 192 GB/s HBM bandwidth. Arithmetic intensity (flops/byte) determines compute-vs-memory-bound: intensive kernels (matrix ops, convolutions) utilize peak flops; bandwidth-limited kernels (reduction, sparse ops) peak at 192 GB/s theoretical max.

Ecosystem and Adoption

rocDNN enables deep learning portability via HIP. Major frameworks (PyTorch, TensorFlow) support ROCm via HIP. HIP adoption remains smaller than CUDA—NVIDIA's dominance and closed ecosystem create lock-in. Academic and national lab efforts drive HIP adoption (ORNL, LLNL, LANL).