Engineering Paradigms for Executing CUDA Code on Non-Nvidia Hardware

Porting CUDA workloads to non-Nvidia hardware requires navigating deep architectural differences in instruction sets, memory models, and runtime APIs. Systems engineers must evaluate the trade-offs between source-to-source translation, intermediate representation compilation, and runtime API emulation.

Architectural Challenges of CUDA Portability

Executing CUDA code on non-Nvidia hardware requires bridging the gap between a proprietary, vertically integrated ecosystem and alternative silicon architectures. Because CUDA is tightly coupled with Nvidia's hardware-specific instruction set and execution model, cross-platform execution demands robust software translation layers. Systems engineers must address compatibility at different levels of the compilation and execution stack, balancing performance overhead against implementation complexity.

Source-to-Source Translation Mechanisms

One primary method for achieving compatibility is source-to-source translation. This approach parses the original CUDA C++ source code and converts it into code compatible with open standards or alternative target runtimes. By performing this translation ahead of time, developers can compile the resulting code directly using the target hardware's native compiler toolchain.

The main engineering obstacle in source-to-source translation is the mapping of hardware-specific intrinsics and warp-level primitives. CUDA-specific execution configurations, such as cooperative groups and warp shuffle operations, do not always have direct equivalents on alternative hardware. Translating these features requires the synthesis of complex emulation sequences, which can introduce performance bottlenecks if the target hardware lacks corresponding low-level instructions.

Intermediate Representation Compilation

Another approach operates further down the compilation pipeline by intercepting CUDA code at the intermediate representation level. Instead of translating high-level source code, compile-time translation layers ingest intermediate bytecodes, such as LLVM IR or SPIR-V, and compile them directly to the target machine's instruction set architecture.

This paradigm preserves compiler optimization passes and allows the backend to generate native machine code tailored to the target hardware. However, this method requires a comprehensive runtime translation library to support device-side API calls, dynamic memory allocation, and host-to-device synchronization. The complexity of this runtime library increases significantly when supporting advanced CUDA specifications.

Runtime API and Binary Translation Layers

The most transparent but technically demanding alternative involves runtime API emulation or binary translation. This strategy intercepts CUDA driver and runtime API calls at the library boundary, mapping them dynamically to the native runtime APIs of the target hardware.

For pre-compiled binaries, this approach requires translating Nvidia's parallel thread execution virtual instruction set into the native machine instructions of the target processor. While this preserves existing workflows by eliminating the need for source code or recompilation, runtime translation introduces latency overhead and requires a highly complex emulation layer to ensure correctness across diverse execution models.