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docs/gpu-architecture.md — roadmap for GPU lambda execution: Phase 1: map/reduce (CUDA thread per element) Phase 2: trampolining (recursive lambdas without stack) Phase 3: interaction combinators (Bend/HVM approach, 74K MIPS) c/jit.h — x86_64 JIT header: JitBlock, JitFunc typedef, jit_compile/jit_free API. Uses mmap for executable memory. System V AMD64 ABI calling convention. jit.c implementation in progress (x86 instruction encoding).
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GPU Lambda Execution — Architecture Notes
Path to Hyper-Lambdas
Phase 1: Map/Reduce on GPU (immediate)
(map f list)where f is pure → one CUDA thread per element(fold-left + 0 list)→ tree-based parallel reduction in shared memory- NaN-boxed values work natively as CUDA
uint64_t - Pre-allocate memory pools (no malloc in kernels)
Phase 2: Trampolining for Recursive Lambdas
- Convert recursive calls to thunks (unevaluated computations)
- GPU kernel loops over thunks instead of recursing
- Eliminates stack overflow risk on GPU
- Each CUDA thread processes one thunk chain
Phase 3: Interaction Combinators (Bend/HVM approach)
- Lambda terms → interaction net graphs
- Rewrite agents in parallel across CUDA cores
- 74,000 MIPS on RTX 4090 demonstrated by HVM2
- Requires compilation to intermediate representation
Key Design Decisions
- Keep NaN-boxing (native CUDA uint64_t)
- Memory pools for cons cells (atomicDec on free list)
- Closures as flat struct: {code_id, env_ptr, env_size}
- Environments as flat arrays (better memory coalescing)
- Warp size 32, block size 256, grid = (N+255)/256
References
- Bend/HVM: github.com/HigherOrderCO/Bend
- cl-cuda: github.com/takagi/cl-cuda
- Harlan (Scheme→OpenCL): github.com/eholk/harlan
- Futhark (functional GPU): futhark-lang.org