Here’s a **ready‑to‑apply PR** that “turns the lights on” for the PAL in **all four languages** while preserving determinism and your public API. It adds real threading for **Java (Virtual Threads)** and **Python (thread pool)**, keeps **C++ OpenMP** as the backend (runtime‑controlled via `max_workers`), and introduces a **Mojo `device` switch** (`"cpu" | "gpu" | "auto"`) so you have the knob for GPU now (CPU fallback for the moment).

**Important Note:** Because you have reviewed the latest Mojo documents, if any of the Mojo code in this PR is incorrect, feel free to correct it.

------

## PR Title

**PAL v2 — Deterministic Parallel Backends Across Languages (Java Virtual Threads, Python thread pool, C++ OpenMP) + Mojo GPU device knob**

## Summary (what changes, what stays the same)

- **API unchanged** everywhere:
  - `configure(options)`
  - `parallel_for(domain, kernel, options?)`
  - `parallel_map(domain, kernel, reduce_in_order, options?)`
  - `counter_rng(...) -> float/double`
- **Determinism preserved** by design:
  - Stable domain order (tile submission in canonical order).
  - Results reduced in **submission order**, independent of completion order.
  - Per‑language counter‑based RNG is unchanged.
- **Backends**
  - **C++** (OpenMP): already implemented; keeps ordered reduction; honors `options.max_workers`.
  - **Java**: **Virtual Threads** via `Executors.newVirtualThreadPerTaskExecutor()`, bounded by `maxWorkers` (Semaphore). Deterministic join in submission order.
  - **Python**: **ThreadPoolExecutor**; deterministic submit + join order; good speedups if kernels release the GIL (e.g., NumPy or native).
  - **Mojo**: adds **`device` switch** with `"gpu"` supported as a **knob** now; falls back to CPU until GPU kernels are wired.
- **Runtime knobs**
  - `ParallelOptions.max_workers` (all languages).
  - `ParallelOptions.tile_size` (all languages).
  - `ParallelOptions.device` (`"cpu" | "gpu" | "auto"`) respected in Mojo now.
  - Optional env: `GROWNET_PAL_MAX_WORKERS` (Python/Java), used if `options.max_workers` not set.

------

## Diff / Patches (full‑file replacements where shown)

> If your tree already contains these files, replace their contents with the versions below. Filenames and packages match what you showed earlier.

### 1) **Python** — `src/python/pal/api.py`

```python
# src/python/pal/api.py
from __future__ import annotations

from dataclasses import dataclass
from typing import Callable, Iterable, Any, List, Optional, TypeVar, Sequence
import os
from concurrent.futures import ThreadPoolExecutor
import math

T = TypeVar("T")
R = TypeVar("R")


@dataclass
class ParallelOptions:
    max_workers: Optional[int] = None
    tile_size: int = 4096
    reduction_mode: str = "ordered"   # "ordered" | "pairwise_tree" (ordered used)
    device: str = "cpu"               # "cpu" | "gpu" | "auto" (no-op in Python for now)
    vectorization_enabled: bool = True


GLOBAL_OPTIONS = ParallelOptions()


def configure(options: ParallelOptions) -> None:
    global GLOBAL_OPTIONS
    GLOBAL_OPTIONS = options


def coerce_items(domain: Iterable[T]) -> List[T]:
    if isinstance(domain, list):
        return domain
    return list(domain)


def resolve_max_workers(opt: Optional[ParallelOptions]) -> int:
    if opt and opt.max_workers:
        return max(1, int(opt.max_workers))
    env = os.getenv("GROWNET_PAL_MAX_WORKERS")
    if env:
        try:
            return max(1, int(env))
        except ValueError:
            pass
    return max(1, (os.cpu_count() or 1))


def resolve_tile_size(opt: Optional[ParallelOptions]) -> int:
    ts = (opt.tile_size if opt else GLOBAL_OPTIONS.tile_size)
    return max(1, int(ts))


def run_chunk_for(chunk: Sequence[T], kernel: Callable[[T], None]) -> None:
    for item in chunk:
        kernel(item)


def run_chunk_map(chunk: Sequence[T], kernel: Callable[[T], R]) -> List[R]:
    out: List[R] = []
    for item in chunk:
        out.append(kernel(item))
    return out


def parallel_for(domain: Iterable[T],
                 kernel: Callable[[T], None],
                 options: Optional[ParallelOptions] = None) -> None:
    items = coerce_items(domain)
    n = len(items)
    if n == 0:
        return
    tile = resolve_tile_size(options or GLOBAL_OPTIONS)
    max_workers = resolve_max_workers(options or GLOBAL_OPTIONS)

    with ThreadPoolExecutor(max_workers=max_workers) as pool:
        futures = []
        # Submit tiles in canonical order for determinism
        for start in range(0, n, tile):
            end = min(n, start + tile)
            chunk = items[start:end]
            futures.append(pool.submit(run_chunk_for, chunk, kernel))
        # Join in submission order (deterministic)
        for f in futures:
            f.result()


def parallel_map(domain: Iterable[T],
                 kernel: Callable[[T], R],
                 reduce_in_order: Callable[[List[R]], R],
                 options: Optional[ParallelOptions] = None) -> R:
    items = coerce_items(domain)
    n = len(items)
    if n == 0:
        return reduce_in_order([])
    tile = resolve_tile_size(options or GLOBAL_OPTIONS)
    max_workers = resolve_max_workers(options or GLOBAL_OPTIONS)

    partials: List[List[R]] = []
    with ThreadPoolExecutor(max_workers=max_workers) as pool:
        futures = []
        for start in range(0, n, tile):
            end = min(n, start + tile)
            chunk = items[start:end]
            futures.append(pool.submit(run_chunk_map, chunk, kernel))
        # Deterministic flatten by submission order
        for f in futures:
            partials.append(f.result())

    flat: List[R] = []
    for part in partials:
        flat.extend(part)
    return reduce_in_order(flat)


# Counter-based deterministic RNG (SplitMix64 mixing)
def mix64(x: int) -> int:
    x = (x + 0x9E3779B97F4A7C15) & 0xFFFFFFFFFFFFFFFF
    z = x
    z = (z ^ (z >> 30)) * 0xBF58476D1CE4E5B9 & 0xFFFFFFFFFFFFFFFF
    z = (z ^ (z >> 27)) * 0x94D049BB133111EB & 0xFFFFFFFFFFFFFFFF
    z = z ^ (z >> 31)
    return z & 0xFFFFFFFFFFFFFFFF


def counter_rng(seed: int, step: int, draw_kind: int, layer_index: int, unit_index: int, draw_index: int) -> float:
    key = (seed & 0xFFFFFFFFFFFFFFFF)
    for v in (step, draw_kind, layer_index, unit_index, draw_index):
        key = mix64((key ^ (v & 0xFFFFFFFFFFFFFFFF)) & 0xFFFFFFFFFFFFFFFF)
    mantissa = (key >> 11) & ((1 << 53) - 1)
    return mantissa / float(1 << 53)
```

> **Notes**
>
> - No identifiers start with `_` (matches your style).
> - Threads give real parallelism when kernels release the GIL (NumPy/native). For pure‑Python CPU kernels, it’s still correct/deterministic, just limited by the GIL.

------

### 2) **Java** — `src/java/ai/nektron/grownet/pal/PAL.java`

```java
// src/java/ai/nektron/grownet/pal/PAL.java
package ai.nektron.grownet.pal;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.concurrent.*;
import java.util.function.Consumer;
import java.util.function.Function;

/**
 * PAL v2: Virtual Threads + deterministic submission-order reduction.
 */
public final class PAL {
  private static ParallelOptions GLOBAL = new ParallelOptions();

  private PAL() {}

  public static void configure(ParallelOptions options) {
    GLOBAL = (options == null ? new ParallelOptions() : options);
  }

  private static <T> List<T> toStableList(Iterable<T> domain) {
    ArrayList<T> items = new ArrayList<>();
    for (T t : domain) items.add(t);
    return items;
  }

  private static int resolveMaxWorkers(ParallelOptions opts) {
    if (opts != null && opts.maxWorkers != null && opts.maxWorkers > 0) return opts.maxWorkers;
    String env = System.getenv("GROWNET_PAL_MAX_WORKERS");
    if (env != null) {
      try {
        int v = Integer.parseInt(env);
        if (v > 0) return v;
      } catch (NumberFormatException ignored) {}
    }
    return Math.max(1, Runtime.getRuntime().availableProcessors());
  }

  private static int resolveTileSize(ParallelOptions opts) {
    int ts = (opts != null ? opts.tileSize : GLOBAL.tileSize);
    return Math.max(1, ts);
  }

  public static <T> void parallelFor(Domain<T> domain, Consumer<T> kernel, ParallelOptions opts) {
    Objects.requireNonNull(domain, "domain");
    Objects.requireNonNull(kernel, "kernel");
    final List<T> items = toStableList(domain);
    final int n = items.size();
    if (n == 0) return;

    final int tile = resolveTileSize(opts);
    final int maxWorkers = resolveMaxWorkers(opts);

    try (ExecutorService vt = Executors.newVirtualThreadPerTaskExecutor()) {
      final Semaphore permits = new Semaphore(maxWorkers);
      final List<Future<Void>> futures = new ArrayList<>();
      for (int start = 0; start < n; start += tile) {
        final int s = start;
        final int e = Math.min(n, start + tile);
        futures.add(vt.submit(() -> {
          permits.acquireUninterruptibly();
          try {
            for (int i = s; i < e; i++) {
              kernel.accept(items.get(i));
            }
            return null;
          } finally {
            permits.release();
          }
        }));
      }
      // Deterministic join in submission order
      for (Future<Void> f : futures) {
        try {
          f.get();
        } catch (InterruptedException ie) {
          Thread.currentThread().interrupt();
          throw new RuntimeException(ie);
        } catch (ExecutionException ee) {
          throw new RuntimeException(ee.getCause());
        }
      }
    }
  }

  public static <T, R> R parallelMap(Domain<T> domain,
                                     Function<T, R> kernel,
                                     Function<List<R>, R> reduceInOrder,
                                     ParallelOptions opts) {
    Objects.requireNonNull(domain, "domain");
    Objects.requireNonNull(kernel, "kernel");
    Objects.requireNonNull(reduceInOrder, "reduceInOrder");
    final List<T> items = toStableList(domain);
    final int n = items.size();
    if (n == 0) return reduceInOrder.apply(Collections.emptyList());

    final int tile = resolveTileSize(opts);
    final int maxWorkers = resolveMaxWorkers(opts);

    final List<Future<List<R>>> futures = new ArrayList<>();
    try (ExecutorService vt = Executors.newVirtualThreadPerTaskExecutor()) {
      final Semaphore permits = new Semaphore(maxWorkers);
      for (int start = 0; start < n; start += tile) {
        final int s = start;
        final int e = Math.min(n, start + tile);
        futures.add(vt.submit(() -> {
          permits.acquireUninterruptibly();
          try {
            ArrayList<R> local = new ArrayList<>(e - s);
            for (int i = s; i < e; i++) local.add(kernel.apply(items.get(i)));
            return local;
          } finally {
            permits.release();
          }
        }));
      }
      ArrayList<R> flat = new ArrayList<>(n);
      // Deterministic flatten: submission order
      for (Future<List<R>> f : futures) {
        try {
          flat.addAll(f.get());
        } catch (InterruptedException ie) {
          Thread.currentThread().interrupt();
          throw new RuntimeException(ie);
        } catch (ExecutionException ee) {
          throw new RuntimeException(ee.getCause());
        }
      }
      return reduceInOrder.apply(flat);
    }
  }

  public static double counterRng(long seed,
                                  long step,
                                  int drawKind,
                                  int layerIndex,
                                  int unitIndex,
                                  int drawIndex) {
    long key = seed;
    key = mix64(key ^ step);
    key = mix64(key ^ drawKind);
    key = mix64(key ^ layerIndex);
    key = mix64(key ^ unitIndex);
    key = mix64(key ^ drawIndex);
    long mantissa = (key >>> 11) & ((1L << 53) - 1L);
    return mantissa / (double) (1L << 53);
  }

  private static long mix64(long x) {
    long z = x + 0x9E3779B97F4A7C15L;
    z = (z ^ (z >>> 30)) * 0xBF58476D1CE4E5B9L;
    z = (z ^ (z >>> 27)) * 0x94D049BB133111EBL;
    z = z ^ (z >>> 31);
    return z;
  }
}
```

> **Requirements:** JDK 21+ (`Executors.newVirtualThreadPerTaskExecutor()` is GA). No incubator modules used.

------

### 3) **C++** — `src/cpp/include/grownet/pal/Pal.h` (OpenMP runtime‑bounded)

```cpp
// src/cpp/include/grownet/pal/Pal.h
// PAL v2: OpenMP-backed parallel_for/map with deterministic submission-order reduction.
// Falls back to sequential if OpenMP is not available.
#pragma once
#include <cstdint>
#include <vector>
#include <type_traits>

#if defined(_OPENMP)
  #include <omp.h>
#endif

namespace grownet { namespace pal {

struct ParallelOptions {
  int  max_workers = 0;    // 0 => auto
  int  tile_size   = 4096;
  enum class Reduction { Ordered, PairwiseTree } reduction = Reduction::Ordered;
  enum class Device    { Cpu, Gpu, Auto } device = Device::Cpu;
  bool vectorization_enabled = true;
};

inline void configure(const ParallelOptions& /*options*/) {}

template <typename Domain, typename Kernel>
inline void parallel_for(const Domain& domain, Kernel kernel, const ParallelOptions* opt = nullptr) {
  const std::size_t n = domain.size();
  if (n == 0) return;
#if defined(_OPENMP)
  const int requested = (opt && opt->max_workers > 0) ? opt->max_workers : omp_get_max_threads();
  #pragma omp parallel for schedule(static) num_threads(requested)
  for (std::int64_t i = 0; i < static_cast<std::int64_t>(n); ++i) {
    kernel(domain[static_cast<std::size_t>(i)]);
  }
#else
  (void)opt;
  for (std::size_t i = 0; i < n; ++i) kernel(domain[i]);
#endif
}

// Domain must support size() and operator[](i)
template <typename Domain, typename Kernel, typename Reduce>
inline auto parallel_map(const Domain& domain, Kernel kernel, Reduce reduce_in_order, const ParallelOptions* opt = nullptr)
    -> decltype(kernel(domain[0])) {
  using R = decltype(kernel(domain[0]));
  const std::size_t n = domain.size();
  if (n == 0) {
    std::vector<R> empty;
    return reduce_in_order(empty);
  }
#if defined(_OPENMP)
  const int requested = (opt && opt->max_workers > 0) ? opt->max_workers : omp_get_max_threads();
  std::vector<std::vector<R>> buckets(static_cast<std::size_t>(requested));
  for (auto& b : buckets) b.reserve((n / static_cast<std::size_t>(requested)) + 1);

  #pragma omp parallel num_threads(requested)
  {
    const int wid = omp_get_thread_num();
    auto& local = buckets[static_cast<std::size_t>(wid)];
    #pragma omp for schedule(static)
    for (std::int64_t i = 0; i < static_cast<std::int64_t>(n); ++i) {
      local.push_back(kernel(domain[static_cast<std::size_t>(i)]));
    }
  }
  std::vector<R> flat; flat.reserve(n);
  for (auto& b : buckets) flat.insert(flat.end(), b.begin(), b.end());
  return reduce_in_order(flat);
#else
  (void)opt;
  std::vector<R> locals; locals.reserve(n);
  for (std::size_t i = 0; i < n; ++i) locals.push_back(kernel(domain[i]));
  return reduce_in_order(locals);
#endif
}

inline std::uint64_t mix64(std::uint64_t x) {
  x += 0x9E3779B97F4A7C15ull;
  std::uint64_t z = x;
  z ^= (z >> 30); z *= 0xBF58476D1CE4E5B9ull;
  z ^= (z >> 27); z *= 0x94D049BB133111EBull;
  z ^= (z >> 31);
  return z;
}

inline double counter_rng(std::uint64_t seed, std::uint64_t step,
                          int draw_kind, int layer_index, int unit_index, int draw_index) {
  std::uint64_t key = seed;
  key = mix64(key ^ static_cast<std::uint64_t>(step));
  key = mix64(key ^ static_cast<std::uint64_t>(draw_kind));
  key = mix64(key ^ static_cast<std::uint64_t>(layer_index));
  key = mix64(key ^ static_cast<std::uint64_t>(unit_index));
  key = mix64(key ^ static_cast<std::uint64_t>(draw_index));
  std::uint64_t mantissa = (key >> 11) & ((1ull << 53) - 1ull);
  return static_cast<double>(mantissa) / static_cast<double>(1ull << 53);
}

}} // namespace grownet::pal
```

> **Build:** keep `-DGROWNET_WITH_OPENMP=ON` and link `OpenMP::OpenMP_CXX` (you already have this wiring; if not, I can include the CMake hunk again).

------

### 4) **Mojo** — `src/mojo/pal/pal.mojo` (adds the GPU device knob)

```mojo
# src/mojo/pal/pal.mojo
struct ParallelOptions:
    var max_workers: Int? = None
    var tile_size: Int = 4096
    var reduction_mode: String = "ordered"
    var device: String = "cpu"               # "cpu" | "gpu" | "auto"
    var vectorization_enabled: Bool = True

fn configure(options: ParallelOptions) -> None:
    # Hook for future backend state; no-op for now.
    _ = options

fn gpu_available() -> Bool:
    # Wire actual detection when GPU kernels are added.
    return False

fn parallel_for[T](domain: list[T], kernel: fn(T) -> None, options: ParallelOptions) -> None:
    if (options.device == "gpu") and gpu_available():
        gpu_parallel_for(domain, kernel, options)
        return
    var index = 0
    while index < domain.len:
        kernel(domain[index])
        index = index + 1

fn parallel_map[T, R](domain: list[T], kernel: fn(T) -> R,
                      reduce_in_order: fn(list[R]) -> R,
                      options: ParallelOptions) -> R:
    if (options.device == "gpu") and gpu_available():
        return gpu_parallel_map(domain, kernel, reduce_in_order, options)
    var locals = [R]()
    var index = 0
    while index < domain.len:
        locals.append(kernel(domain[index]))
        index = index + 1
    return reduce_in_order(locals)

# CPU fallback for future GPU path
fn gpu_parallel_for[T](domain: list[T], kernel: fn(T) -> None, options: ParallelOptions) -> None:
    var index = 0
    while index < domain.len:
        kernel(domain[index])
        index = index + 1

fn gpu_parallel_map[T, R](domain: list[T], kernel: fn(T) -> R,
                          reduce_in_order: fn(list[R]) -> R,
                          options: ParallelOptions) -> R:
    var locals = [R]()
    var index = 0
    while index < domain.len:
        locals.append(kernel(domain[index]))
        index = index + 1
    return reduce_in_order(locals)

# SplitMix64-style counter-based RNG producing Float64 in [0,1)
fn mix64(x_in: UInt64) -> UInt64:
    var z = x_in + 0x9E3779B97F4A7C15
    z = (z ^ (z >> 30)) * 0xBF58476D1CE4E5B9
    z = (z ^ (z >> 27)) * 0x94D049BB133111EB
    z = (z ^ (z >> 31))
    return z

fn counter_rng(seed: Int, step: Int, draw_kind: Int, layer_index: Int, unit_index: Int, draw_index: Int) -> Float64:
    var key: UInt64 = UInt64(seed)
    key = mix64(key ^ UInt64(step))
    key = mix64(key ^ UInt64(draw_kind))
    key = mix64(key ^ UInt64(layer_index))
    key = mix64(key ^ UInt64(unit_index))
    key = mix64(key ^ UInt64(draw_index))
    var mantissa: UInt64 = (key >> 11) & ((UInt64(1) << 53) - 1)
    return Float64(mantissa) / Float64(UInt64(1) << 53)
```

> This adds the **device knob** you asked for (`"gpu"` & `"auto"` accepted now). The GPU branch calls CPU for the moment—safe default with identical results.

------

## How to “switch it on”

- **C++**: ensure OpenMP is detected and linked (you already had the flag).

  ```bash
  cmake -DGROWNET_WITH_OPENMP=ON ..
  cmake --build . -j
  ```

  At runtime set workers in code via `ParallelOptions{.max_workers = N}` or export `OMP_NUM_THREADS=N`.

- **Java (JDK 21+)**:

  - No additional flags needed. Control concurrency with:

    ```java
    ParallelOptions opts = new ParallelOptions();
    opts.maxWorkers = 8;   // or leave null to use core count
    opts.tileSize = 4096;
    PAL.parallelFor(domain, kernel, opts);
    ```

  - Virtual Threads orchestrate work; a `Semaphore` bounds real parallelism to `maxWorkers`.

- **Python**:

  - The PAL now runs **threads** deterministically:

    ```python
    from pal.api import ParallelOptions, parallel_map
    opts = ParallelOptions(max_workers=8, tile_size=4096)
    result = parallel_map(domain, kernel, reducer, options=opts)
    ```

  - You can also export `GROWNET_PAL_MAX_WORKERS=8` to set a default.

- **Mojo**:

  - The device knob is live:

    ```mojo
    var opts = ParallelOptions()
    opts.device = "gpu"   # uses GPU path when available; CPU fallback today
    ```

  - CPU behavior stays deterministic, same as before.

------

## Why this preserves determinism

1. **Stable enumeration order**: domains are consumed in lexicographic order and **tiles are submitted in that order**.
2. **Ordered reduction**: we **join futures in submission order** (Java/Python) and **flatten buckets 0..N‑1** (C++).
3. **Counter RNG**: unchanged; identical streams regardless of worker count.

------

## Optional (if you want me to add in this PR as well)

- A tiny **determinism test** per language (C++ example you can drop into `src/cpp/tests/pal_determinism_test.cpp` is available; I can mirror for Java/Python in your current test harness).
- A short `docs/PAL_README.md` explaining the knobs and invariants.

------

### Commit Message (suggested)

```
PAL v2: Deterministic parallel backends across languages + Mojo GPU knob

- Java: Implement parallelFor/parallelMap with Virtual Threads (JDK 21), bounded by maxWorkers via Semaphore; deterministic submission-order join.
- Python: ThreadPool-backed parallel_for/map with deterministic submission-order reduction; honors max_workers and tile_size.
- C++: Keep OpenMP backend; ensure ordered reduction and runtime control via max_workers.
- Mojo: Add device="cpu|gpu|auto" knob; CPU fallback today while preserving API.
- Determinism preserved (stable domain order + ordered reduction + counter-based RNG).
- No identifiers with leading underscores.
```

