Short answer: **not yet**. What Codex landed is the **PAL façade with a deterministic, sequential fallback** in all four languages. There’s **no real threading, no GPU, no virtual-thread orchestration** behind it yet—just the API surface and a safe “do it in order” implementation so you can start wiring call-sites without changing behavior.

### How to tell from the diff

- **Python** `pal/api.py`: loops over the domain and calls `kernel` inline; no threads, no pools.
- **Java** `PAL.parallelFor/parallelMap`: iterates the `Domain<T>` and invokes `kernel`/collects locals; no executor, no Structured Concurrency, no Virtual Threads.
- **C++** `include/grownet/pal/Pal.h`: header-only templates that iterate and reduce; no OpenMP/TBB includes or pragmas.
- **Mojo** `pal/pal.mojo`: simple `while index < len` loops; no CPU tasking or GPU dispatch.

That’s exactly what we asked for as a first step (drop-in façade, deterministic), but it’s intentionally **not** the real backend.

------

## Next PR: “turn the lights on” (actual parallel backends)

Below are concrete, minimal patches per language to replace the sequential bodies with real parallelism while keeping determinism:

### 1) C++ (OpenMP static or TBB), ordered reduction

**Pal.h / Pal.cpp**

```cpp
// OpenMP version (easy drop-in)
#include <omp.h>

template <typename Domain, typename Kernel>
inline void parallel_for(const Domain& domain, Kernel kernel, const ParallelOptions* opt) {
  const int n = static_cast<int>(domain.size());           // ask your domain for deterministic size
  const int maxw = (opt && opt->max_workers>0) ? opt->max_workers : omp_get_max_threads();
#pragma omp parallel for schedule(static) num_threads(maxw)
  for (int i=0; i<n; ++i) {
    kernel(domain[i]);                                     // no shared writes; tile-local only
  }
}

template <typename Domain, typename Kernel, typename Reduce>
inline auto parallel_map(const Domain& domain, Kernel kernel, Reduce reduce_in_order,
                         const ParallelOptions* opt) -> decltype(kernel(domain[0])) {
  using R = decltype(kernel(domain[0]));
  const int n = static_cast<int>(domain.size());
  const int maxw = (opt && opt->max_workers>0) ? opt->max_workers : omp_get_max_threads();
  std::vector<std::vector<R>> buckets(maxw);
#pragma omp parallel num_threads(maxw)
  {
    const int wid = omp_get_thread_num();
#pragma omp for schedule(static)
    for (int i=0; i<n; ++i) {
      buckets[wid].push_back(kernel(domain[i]));
    }
  }
  // deterministic reduction: worker 0..N-1
  std::vector<R> flat; flat.reserve(n);
  for (auto& b : buckets) flat.insert(flat.end(), b.begin(), b.end());
  return reduce_in_order(flat);
}
```

**Determinism guardrails**

- Domains must expose **size()** and **operator[]** with a **stable order**.
- Kernels write **only tile-local / thread-local** state.
- Reductions happen in **worker-id order** (or a fixed binary tree if you prefer).

### 2) Java 21 (platform executor + Virtual Threads for orchestration)

**PAL.java**

```java
private static final ExecutorService CPU = Executors.newFixedThreadPool(
    Math.max(1, Runtime.getRuntime().availableProcessors()));

public static <T> void parallelFor(Domain<T> domain, Consumer<T> kernel, ParallelOptions opts) {
  Objects.requireNonNull(domain); Objects.requireNonNull(kernel);
  final List<T> items = new ArrayList<>(); domain.forEach(items::add);       // stable order
  final int n = items.size();
  final int maxw = (opts!=null && opts.maxWorkers!=null) ? opts.maxWorkers : Runtime.getRuntime().availableProcessors();
  final int tile = Math.max(1, (opts!=null ? opts.tileSize : 4096));

  List<Callable<Void>> tasks = new ArrayList<>();
  for (int start=0; start<n; start+=tile) {
    final int s=start, e=Math.min(n, start+tile);
    tasks.add(() -> { for (int i=s;i<e;i++) kernel.accept(items.get(i)); return null; });
  }
  // Optionally wrap in Virtual Threads for orchestration:
  try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
    for (Callable<Void> t : tasks) scope.fork(() -> { CPU.submit(t).get(); return null; });
    scope.join().throwIfFailed();
  } catch (Exception ex) { throw new RuntimeException(ex); }
}

public static <T,R> R parallelMap(Domain<T> domain, Function<T,R> kernel,
                                  Function<List<R>,R> reduceInOrder, ParallelOptions opts) {
  // same task split as above; collect List<R> per task, then concatenate in submission order
}
```

**Key point:** Virtual Threads are used for **structured orchestration**, but **actual compute runs on a fixed pool** sized to cores to avoid oversubscription.

### 3) Python (native backend through C++), deterministic reduction

- Keep `src/python/pal/api.py` as the **orchestrator** but import `_pal_native` (pybind11/nanobind) that wraps the C++ backend above.
- Expose the same `parallel_for`/`parallel_map` by marshalling Python `domain` to an indexable proxy and back.

```python
# api.py
try:
    import _pal_native
    HAS_NATIVE = True
except ImportError:
    HAS_NATIVE = False

def parallel_for(domain, kernel, options=None):
    if HAS_NATIVE and hasattr(domain, '__getitem__') and hasattr(domain, '__len__'):
        return _pal_native.parallel_for(domain, kernel, options or _GLOBAL_OPTIONS)
    # fallback: sequential
    for item in domain: kernel(item)
```

### 4) Mojo (CPU now, GPU later)

- Mirror the C++ tiling strategy on CPU with Mojo tasks (or a simple work-sharing loop to start).
- Add a `device == "gpu"` branch later that launches device kernels; keep the **same deterministic reduction** by either:
  - copying back thread-local device partials and merging on host in worker order, or
  - doing a **fixed-shape device reduction** (serial per block or a fixed binary tree).

```mojo
fn parallel_for[T](domain: list[T], kernel: fn(T) -> None, options: ParallelOptions) -> None:
    let n = domain.len
    let tile = max(1, options.tile_size)
    var start = 0
    while start < n:
        let end = min(n, start + tile)
        var i = start
        while i < end:
            kernel(domain[i])
            i += 1
        start += tile
# Later: replace the inner while with CPU tasks or GPU dispatch based on options.device
```

------

## What to verify in your tree now

- ✅ The **API is in place** and safe to call in all languages (it just runs sequentially).
- ❌ There is **no actual parallel execution** yet. No thread pools, OpenMP/TBB pragmas, Mojo GPU dispatch, or Java executors were added in this PR.
- ✅ `counter_rng` is implemented consistently (SplitMix64 hash of counters) so when we flip to parallel execution the **random draws remain identical**.

------

## My recommended sequence

1. **Wire the backends** per above (start with **C++ OpenMP** and **Java platform executor**; Python calls the C++ backend via pybind11; Mojo keeps CPU tiling first).
2. **Gate with env/flag**: `PAL_ENABLED=1` or `ParallelOptions(device="auto", max_workers=C)`.
3. Add the **determinism tests** (same seed + inputs → identical state across `{max_workers=1,2,8}` and `{device=cpu,auto}`) and the **performance sanity** test on a big 2D tick.
4. When green, flip `PAL_ENABLED` default **on for 2D Phase-A/B**.

