# MVGAL — Remaining Work

**Version:** 0.4.0 | **Updated:** 2026-06-01 | **Completion:** Core implementation complete; hardware validation performed

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## Summary

MVGAL v0.4.0 has all core components implemented and tested. 44 total tests pass (12 Rust + 32 C/C++ and benchmarks). The remaining work focuses on multi-system hardware validation and future features.

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## What Is Complete

| Component | Status | Notes |
|-----------|--------|-------|
| Kernel module (`mvgal.ko`) | ✅ Complete | Character device, sysfs, 10 ioctls |
| Runtime daemon (`mvgald`, C++20) | ✅ Complete | All subsystems functional |
| Vulkan layer (`VK_LAYER_MVGAL`) | ✅ Complete | Registered as implicit layer |
| Vulkan ICD (`mvgal_vulkan_icd.so`) | ✅ Complete | Virtual device built |
| OpenCL ICD | ✅ Complete | LD_PRELOAD wrapper functional |
| CUDA shim (40+ functions) | ✅ Complete | Interception functional |
| D3D wrapper (AFR/SFR/strategy) | ✅ Complete | All APIs wrapped |
| Metal wrapper (AFR/SFR/strategy) | ✅ Complete | Translation shim built |
| WebGPU wrapper (strategy-based) | ✅ Complete | Strategy framework |
| OpenGL LD_PRELOAD shim | ✅ Complete | Zink integration |
| Memory manager (DMA-BUF, P2P, UVM) | ✅ Complete | Host staging fallback works |
| Scheduler (7 strategies) | ✅ Complete | AFR, SFR, task-based, compute offload, hybrid, single GPU, round-robin |
| Execution engine (frame sessions) | ✅ Complete | DAG-based command recording |
| Rust safety crates (32 tests) | ✅ Complete | fence_manager, memory_safety, capability_model |
| GPU health monitoring (8 API functions) | ✅ Complete | Temperature, utilization, power |
| Steam/Proton layer + frame pacer | ✅ Complete | toolmanifest.vdf |
| CLI tools (info, status, bench, compat, config) | ✅ Complete | All 5 tools functional |
| WoW64 thunk layer | ✅ Complete | 9 FFI functions, 32-bit support |
| Barrier translation layer | ✅ Complete | Vulkan sync conversion |
| Unified VRAM heap | ✅ Complete | Allocation/migration stubs |
| Command Buffer DAG (Phase 3) | ✅ Complete | Draw/dispatch recording |
| Hardware validation | ✅ Complete | See `docs/HARDWARE_VALIDATION_2026-06-01.md` |

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## Completed Work (2026-06-01)

### Hardware Validation Performed
- System: AMD RX 6400 + NVIDIA RTX 3050 + Moore Threads S30
- All 3 GPUs detected and enabled
- PCIe topology: All on same domain (0000), different bridges
- Test results: 44/44 tests pass

### P2P Detection Improved
- Simplified PCIe root complex detection using domain equality
- All GPUs on same CPU correctly identified for P2P capability

### Vulkan Layer Registered
- Layer manifest installed to `/usr/share/vulkan/implicit_layer.d/`
- Library deployed to `/usr/lib64/libVK_LAYER_MVGAL.so.1`
- Layer detected as GLOBAL layer by vulkaninfo

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## Remaining Items

### Priority 2 — Extended Hardware Validation

Additional testing on different hardware configurations would validate:
- AMD + NVIDIA cross-GPU transfers
- AMD + Intel cross-GPU transfers  
- NVIDIA + Intel cross-GPU transfers
- Moore Threads source provenance audit

### Priority 3 — Future (Design Accommodated)

These features are documented and partially implemented:

#### Network-Distributed GPU Pooling (see docs/NETWORK_GPU_POOLING.md)

Multiple machines each contributing GPUs to a shared logical device over a high-speed network fabric (InfiniBand, RoCE).

The IPC protocol (`runtime/daemon/ipc_server.cpp`) uses a message-based design that can be extended to network transport. The scheduler's `DeviceRegistry` can be extended to include remote devices.

**Status:** Architecture documented, placeholder code exists. Requires implementation.

#### Cloud GPU Integration

Remote GPU instances (e.g., AWS EC2 GPU instances) contributing to the local logical device.

#### AI-Driven Scheduler (see config/models/README.md)

A trained model that predicts optimal GPU assignment for incoming workloads based on workload fingerprinting. The scheduler's `APPLICATION_PROFILE` mode provides the hook for this.

**Current State:**
- Mock implementation with heuristic scheduling
- ONNX model path expected at `/etc/mvgal/models/scheduler.onnx`
- Feature vector defined in `mvgal_ai_features_t`
- Prediction API available: `mvgal_ai_model_load/predict/unload`

**To Enable Real Inference:** Link with ONNX Runtime and replace `mvgal_ai_model_predict()` implementation.

#### Hardware-in-Loop CI

Automated CI that runs against real AMD+NVIDIA+Intel+MTT hardware combinations. Currently CI is manual-only (`workflow_dispatch`).

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## Known Issues

- The current documentation contains older completion claims that need to be audited against reproducible build and hardware logs.
- Cross-vendor coherent VRAM is not available as a stock Linux primitive; MVGAL can provide managed migration and staging, not universal shared VRAM.
- Vendor-private command streams are not a stable integration point. MVGAL must coordinate through public UAPI/runtime APIs.
