Merge pull request #8 from mivertowski/claude/expand-cuda-intrinsics-015QNYA1vEFCNZoLMdLafWQ8

Expand CUDA intrinsics coverage to 120+ operations

Author: mivertowski

CLAUDE.md

@@ -192,14 +192,15 @@ let cuda_code = transpile_ring_kernel(&handler, &config)?;
 ```
 
 **DSL Features:**
-- Block/grid indices: `block_idx_x()`, `thread_idx_x()`, `block_dim_x()`, `grid_dim_x()`, etc.
+- Block/grid indices: `block_idx_x()`, `thread_idx_x()`, `block_dim_x()`, `grid_dim_x()`, `warp_size()`, etc.
 - Control flow: `if/else`, `match` → switch/case, early `return`
 - Loops: `for i in 0..n`, `while cond`, `loop` with `break`/`continue`
-- Stencil intrinsics: `pos.north(buf)`, `pos.south(buf)`, `pos.east(buf)`, `pos.west(buf)`, `pos.at(buf, dx, dy)`
+- Stencil intrinsics (2D): `pos.north(buf)`, `pos.south(buf)`, `pos.east(buf)`, `pos.west(buf)`, `pos.at(buf, dx, dy)`
+- Stencil intrinsics (3D): `pos.up(buf)`, `pos.down(buf)`, `pos.at(buf, dx, dy, dz)` for volumetric kernels
 - Shared memory: `__shared__` arrays and tiles with `SharedMemoryConfig`
 - Struct literals: `Point { x: 1.0, y: 2.0 }` → C compound literals
 - Reference expressions: `&arr[idx]` → pointer to element with automatic `->` operator for field access
-- 45+ GPU intrinsics (atomics, warp ops, sync, math)
+- 120+ GPU intrinsics across 13 categories (synchronization, atomics, math, trig, hyperbolic, exponential, classification, warp, bit manipulation, memory, special, index, timing)
 
 **Ring Kernel Features:**
 - Persistent message loop with ControlBlock lifecycle management
@@ -282,7 +283,7 @@ Main crate (`ringkernel`) features:
 - ringkernel-core: 65 tests
 - ringkernel-cpu: 11 tests
 - ringkernel-cuda: 6 GPU execution tests
-- ringkernel-cuda-codegen: 143 tests (loops, shared memory, ring kernels, K2K, reference expressions)
+- ringkernel-cuda-codegen: 171 tests (loops, shared memory, ring kernels, K2K, reference expressions, 120+ GPU intrinsics)
 - ringkernel-wgpu-codegen: 50 tests (types, intrinsics, transpiler, validation)
 - ringkernel-derive: 14 macro tests
 - ringkernel-wavesim: 49 tests (including educational modes)

crates/ringkernel-cuda-codegen/README.md

@@ -7,7 +7,7 @@ Rust-to-CUDA transpiler for RingKernel GPU kernels.
 This crate enables writing GPU kernels in a restricted Rust DSL and transpiling them to CUDA C code. It supports three kernel types:
 
 1. **Global Kernels** - Standard CUDA `__global__` functions
-2. **Stencil Kernels** - Tile-based kernels with `GridPos` abstraction
+2. **Stencil Kernels** - Tile-based kernels with `GridPos` abstraction (2D and 3D)
 3. **Ring Kernels** - Persistent actor kernels with message loops
 
 ## Installation
@@ -39,11 +39,12 @@ let cuda_code = transpile_global_kernel(&func)?;
 
 ## Stencil Kernels
 
-For grid-based computations with neighbor access:
+For grid-based computations with neighbor access (2D and 3D):
 
 ```rust
-use ringkernel_cuda_codegen::{transpile_stencil_kernel, StencilConfig};
+use ringkernel_cuda_codegen::{transpile_stencil_kernel, StencilConfig, Grid};
 
+// 2D stencil
 let func: syn::ItemFn = parse_quote! {
     fn fdtd(p: &[f32], p_prev: &mut [f32], c2: f32, pos: GridPos) {
         let lap = pos.north(p) + pos.south(p) + pos.east(p) + pos.west(p)
@@ -53,10 +54,25 @@ let func: syn::ItemFn = parse_quote! {
 };
 
 let config = StencilConfig::new("fdtd")
+    .with_grid(Grid::Grid2D)
     .with_tile_size(16, 16)
     .with_halo(1);
 
 let cuda_code = transpile_stencil_kernel(&func, &config)?;
+
+// 3D stencil with up/down neighbors
+let func_3d: syn::ItemFn = parse_quote! {
+    fn laplacian_3d(p: &[f32], out: &mut [f32], pos: GridPos) {
+        let lap = pos.north(p) + pos.south(p) + pos.east(p) + pos.west(p)
+                  + pos.up(p) + pos.down(p) - 6.0 * p[pos.idx()];
+        out[pos.idx()] = lap;
+    }
+};
+
+let config_3d = StencilConfig::new("laplacian")
+    .with_grid(Grid::Grid3D)
+    .with_tile_size(8, 8)
+    .with_halo(1);
 ```
 
 ## Ring Kernels
@@ -86,35 +102,149 @@ let cuda_code = transpile_ring_kernel(&handler, &config)?;
 ## DSL Reference
 
 ### Thread/Block Indices
-- `thread_idx_x()`, `thread_idx_y()`, `thread_idx_z()`
-- `block_idx_x()`, `block_idx_y()`, `block_idx_z()`
-- `block_dim_x()`, `block_dim_y()`, `block_dim_z()`
-- `grid_dim_x()`, `grid_dim_y()`, `grid_dim_z()`
+- `thread_idx_x()`, `thread_idx_y()`, `thread_idx_z()` → `threadIdx.x/y/z`
+- `block_idx_x()`, `block_idx_y()`, `block_idx_z()` → `blockIdx.x/y/z`
+- `block_dim_x()`, `block_dim_y()`, `block_dim_z()` → `blockDim.x/y/z`
+- `grid_dim_x()`, `grid_dim_y()`, `grid_dim_z()` → `gridDim.x/y/z`
+- `warp_size()` → `warpSize`
 
-### Stencil Intrinsics
+### Stencil Intrinsics (2D)
 - `pos.idx()` - Linear index
-- `pos.north(buf)`, `pos.south(buf)`, `pos.east(buf)`, `pos.west(buf)`
+- `pos.north(buf)`, `pos.south(buf)` - Y-axis neighbors
+- `pos.east(buf)`, `pos.west(buf)` - X-axis neighbors
 - `pos.at(buf, dx, dy)` - Relative offset access
 
-### Synchronization
-- `sync_threads()` - Block-level barrier
-- `thread_fence()` - Device memory fence
-- `thread_fence_block()` - Block memory fence
-
-### Atomics
-- `atomic_add(ptr, val)`, `atomic_sub(ptr, val)`
-- `atomic_min(ptr, val)`, `atomic_max(ptr, val)`
-- `atomic_exchange(ptr, val)`, `atomic_cas(ptr, compare, val)`
+### Stencil Intrinsics (3D)
+- `pos.up(buf)`, `pos.down(buf)` - Z-axis neighbors
+- `pos.at(buf, dx, dy, dz)` - 3D relative offset access
 
-### Math Functions
-- `sqrt()`, `abs()`, `floor()`, `ceil()`, `round()`
-- `sin()`, `cos()`, `tan()`, `exp()`, `log()`
-- `powf()`, `min()`, `max()`, `mul_add()`
+### Synchronization
+- `sync_threads()` → `__syncthreads()` - Block-level barrier
+- `sync_threads_count(pred)` → `__syncthreads_count()` - Count threads with predicate
+- `sync_threads_and(pred)` → `__syncthreads_and()` - AND of predicate
+- `sync_threads_or(pred)` → `__syncthreads_or()` - OR of predicate
+- `thread_fence()` → `__threadfence()` - Device memory fence
+- `thread_fence_block()` → `__threadfence_block()` - Block memory fence
+- `thread_fence_system()` → `__threadfence_system()` - System memory fence
+
+### Atomic Operations (Integer)
+- `atomic_add(ptr, val)` → `atomicAdd`
+- `atomic_sub(ptr, val)` → `atomicSub`
+- `atomic_min(ptr, val)` → `atomicMin`
+- `atomic_max(ptr, val)` → `atomicMax`
+- `atomic_exchange(ptr, val)` → `atomicExch`
+- `atomic_cas(ptr, compare, val)` → `atomicCAS`
+- `atomic_and(ptr, val)` → `atomicAnd`
+- `atomic_or(ptr, val)` → `atomicOr`
+- `atomic_xor(ptr, val)` → `atomicXor`
+- `atomic_inc(ptr, val)` → `atomicInc` (increment with wrap)
+- `atomic_dec(ptr, val)` → `atomicDec` (decrement with wrap)
+
+### Basic Math Functions
+- `sqrt()`, `rsqrt()` - Square root, reciprocal sqrt
+- `abs()`, `fabs()` - Absolute value
+- `floor()`, `ceil()`, `round()`, `trunc()` - Rounding
+- `fma()`, `mul_add()` - Fused multiply-add
+- `fmin()`, `fmax()` - Minimum, maximum
+- `fmod()`, `remainder()` - Modulo operations
+- `copysign()` - Copy sign
+- `cbrt()` - Cube root
+- `hypot()` - Hypotenuse
+
+### Trigonometric Functions
+- `sin()`, `cos()`, `tan()` - Basic trig
+- `asin()`, `acos()`, `atan()`, `atan2()` - Inverse trig
+- `sincos()` - Combined sine and cosine
+- `sinpi()`, `cospi()` - Sin/cos of π*x
+
+### Hyperbolic Functions
+- `sinh()`, `cosh()`, `tanh()` - Hyperbolic
+- `asinh()`, `acosh()`, `atanh()` - Inverse hyperbolic
+
+### Exponential and Logarithmic Functions
+- `exp()`, `exp2()`, `exp10()`, `expm1()` - Exponentials
+- `log()`, `ln()`, `log2()`, `log10()`, `log1p()` - Logarithms
+- `pow()`, `powf()`, `powi()` - Power
+- `ldexp()`, `scalbn()` - Load/scale exponent
+- `ilogb()` - Extract exponent
+- `erf()`, `erfc()`, `erfinv()`, `erfcinv()` - Error functions
+- `lgamma()`, `tgamma()` - Gamma functions
+
+### Classification Functions
+- `is_nan()`, `isnan()` → `isnan`
+- `is_infinite()`, `isinf()` → `isinf`
+- `is_finite()`, `isfinite()` → `isfinite`
+- `is_normal()`, `isnormal()` → `isnormal`
+- `signbit()` - Check sign bit
+- `nextafter()` - Next representable value
+- `fdim()` - Positive difference
 
 ### Warp Operations
-- `warp_shuffle(val, lane)`, `warp_shuffle_up(val, delta)`
-- `warp_shuffle_down(val, delta)`, `warp_shuffle_xor(val, mask)`
-- `warp_ballot(pred)`, `warp_all(pred)`, `warp_any(pred)`
+- `warp_active_mask()` → `__activemask()` - Active lane mask
+- `warp_shfl(mask, val, lane)` → `__shfl_sync` - Shuffle
+- `warp_shfl_up(mask, val, delta)` → `__shfl_up_sync`
+- `warp_shfl_down(mask, val, delta)` → `__shfl_down_sync`
+- `warp_shfl_xor(mask, val, lane_mask)` → `__shfl_xor_sync`
+- `warp_ballot(mask, pred)` → `__ballot_sync`
+- `warp_all(mask, pred)` → `__all_sync`
+- `warp_any(mask, pred)` → `__any_sync`
+
+### Warp Match Operations (Volta+)
+- `warp_match_any(mask, val)` → `__match_any_sync`
+- `warp_match_all(mask, val)` → `__match_all_sync`
+
+### Warp Reduce Operations (SM 8.0+)
+- `warp_reduce_add(mask, val)` → `__reduce_add_sync`
+- `warp_reduce_min(mask, val)` → `__reduce_min_sync`
+- `warp_reduce_max(mask, val)` → `__reduce_max_sync`
+- `warp_reduce_and(mask, val)` → `__reduce_and_sync`
+- `warp_reduce_or(mask, val)` → `__reduce_or_sync`
+- `warp_reduce_xor(mask, val)` → `__reduce_xor_sync`
+
+### Bit Manipulation
+- `popc()`, `popcount()`, `count_ones()` → `__popc` - Population count
+- `clz()`, `leading_zeros()` → `__clz` - Count leading zeros
+- `ctz()`, `trailing_zeros()` → `__ffs - 1` - Count trailing zeros
+- `ffs()` → `__ffs` - Find first set
+- `brev()`, `reverse_bits()` → `__brev` - Bit reverse
+- `byte_perm()` → `__byte_perm` - Byte permutation
+- `funnel_shift_left()` → `__funnelshift_l`
+- `funnel_shift_right()` → `__funnelshift_r`
+
+### Memory Operations
+- `ldg(ptr)`, `load_global(ptr)` → `__ldg` - Read-only cache load
+- `prefetch_l1(ptr)` → `__prefetch_l1` - L1 prefetch
+- `prefetch_l2(ptr)` → `__prefetch_l2` - L2 prefetch
+
+### Special Functions
+- `rcp()`, `recip()` → `__frcp_rn` - Fast reciprocal
+- `fast_div()` → `__fdividef` - Fast division
+- `saturate()`, `clamp_01()` → `__saturatef` - Saturate to [0,1]
+- `j0()`, `j1()`, `jn()` - Bessel functions of first kind
+- `y0()`, `y1()`, `yn()` - Bessel functions of second kind
+- `normcdf()`, `normcdfinv()` - Normal CDF
+- `cyl_bessel_i0()`, `cyl_bessel_i1()` - Cylindrical Bessel functions
+
+### Clock and Timing
+- `clock()` → `clock()` - 32-bit clock counter
+- `clock64()` → `clock64()` - 64-bit clock counter
+- `nanosleep(ns)` → `__nanosleep` - Sleep for nanoseconds
+
+### RingContext Methods
+- `ctx.thread_id()` → `threadIdx.x`
+- `ctx.block_id()` → `blockIdx.x`
+- `ctx.global_thread_id()` → `(blockIdx.x * blockDim.x + threadIdx.x)`
+- `ctx.sync_threads()` → `__syncthreads()`
+- `ctx.lane_id()` → `(threadIdx.x % 32)`
+- `ctx.warp_id()` → `(threadIdx.x / 32)`
+
+### Ring Kernel Intrinsics
+- `is_active()`, `should_terminate()`, `mark_terminated()`
+- `messages_processed()`, `input_queue_size()`, `output_queue_size()`
+- `input_queue_empty()`, `output_queue_empty()`, `enqueue_response(&resp)`
+- `hlc_tick()`, `hlc_update(ts)`, `hlc_now()` - HLC operations
+- `k2k_send(target, &msg)`, `k2k_try_recv()` - K2K messaging
+- `k2k_has_message()`, `k2k_peek()`, `k2k_pending_count()`
 
 ## Type Mapping
 
@@ -130,13 +260,33 @@ let cuda_code = transpile_ring_kernel(&handler, &config)?;
 | `&[T]` | `const T* __restrict__` |
 | `&mut [T]` | `T* __restrict__` |
 
+## Intrinsic Count
+
+The transpiler supports **120+ GPU intrinsics** across 13 categories:
+
+| Category | Count | Examples |
+|----------|-------|----------|
+| Synchronization | 7 | `sync_threads`, `thread_fence` |
+| Atomics | 11 | `atomic_add`, `atomic_cas`, `atomic_and` |
+| Math | 16 | `sqrt`, `fma`, `cbrt`, `hypot` |
+| Trigonometric | 11 | `sin`, `asin`, `atan2`, `sincos` |
+| Hyperbolic | 6 | `sinh`, `asinh` |
+| Exponential | 18 | `exp`, `log2`, `erf`, `gamma` |
+| Classification | 8 | `isnan`, `isfinite`, `signbit` |
+| Warp | 16 | `warp_shfl`, `warp_reduce_add`, `warp_match_any` |
+| Bit Manipulation | 8 | `popc`, `clz`, `brev`, `funnel_shift_left` |
+| Memory | 3 | `ldg`, `prefetch_l1` |
+| Special | 13 | `rcp`, `saturate`, `normcdf` |
+| Index | 13 | `thread_idx_x`, `warp_size` |
+| Timing | 3 | `clock`, `clock64`, `nanosleep` |
+
 ## Testing
 
 ```bash
 cargo test -p ringkernel-cuda-codegen
 ```
 
-The crate includes 143 tests covering all kernel types and language features.
+The crate includes 171 tests covering all kernel types, intrinsics, and language features.
 
 ## License