Plan: treat this as a staged, test-driven port with measurable gates: correctness, numerical fidelity, performance parity, and robust fallbacks.
- Discovery & scope
- Inventory kernels, inputs/outputs, precision (fp32/fp16/bf16), dependencies, and performance-critical paths.
- Capture baseline: representative inputs, unit test vectors, trained weights, runtimes, memory/use patterns on original hardware.
- Port strategy
- Start with a reference CPU or slower GPU implementation (e.g., CUDA->thoroughly tested CUDA on new framework) then map to new framework's kernel API (e.g., Triton, ROCm, SYCL, or vendor-supplied primitives).
- Prefer reuse of high-level ops where possible; implement custom kernels only when necessary.
- Correctness & numerical stability testing
- Unit tests: deterministic inputs, compare outputs to baseline within strict tolerances. Use bitwise equality where expected, otherwise relative/absolute tolerances per-tensor.
- Statistical tests: run randomized fuzz tests across input distributions; compute max error, mean error, and error histograms.
- Sensitivity tests: vary precision (fp32→fp16→bf16) and scale inputs to expose under/overflow; run gradient checks (finite differences) for backward kernels.
- Stability checks: long-run accumulation tests, stochastic rounding behavior, and condition-number-based tests for reductions.
- Profiling & benchmarking
- Instrument with nvprof/nsys/NSight and framework profilers. Measure kernel time, memory bandwidth, occupancy, warp efficiency, and L2/cache hit rates.
- Use representative end-to-end workloads and microbenchmarks. Track memory allocations and copies.
- Optimization for new architecture
- Target memory access patterns: coalescing, shared memory, vectorized loads/stores, and reduce bank conflicts.
- Tune thread/block sizes by occupancy models; leverage tensor cores / matrix units (wmma/ptx) when precision allows; use mixed precision with loss scaling.
- Exploit new intrinsics and asynchronous copy primitives (cp.async) and L2/shared memory hierarchies.
- Iterate: profile → hotspot → micro-optimize → regression test.
- Fallbacks & graceful degradation
- Implement multi-path execution: preferred fast kernel, safe kernel (more portable, slower), and reference CPU path. Detect capabilities at runtime (SM version, drivers, supported intrinsics).
- Auto-select based on hardware/precision or fall back to JIT-compiled kernel or vendor BLAS/graph primitives.
- Feature flags and telemetry for field failures; circuit-breaker to avoid repeated failing kernels.
- CI & deployment
- Integrate tests in CI across target architectures (CI runners with different GPUs/driver versions).
- Add performance regression checks with acceptable deltas.
- Monitor in production with sampling, health metrics, and automated rollback.
This approach minimizes risk by prioritizing correctness first, then performance, and ensures resilient behavior across heterogeneous hardware.
