Dyson Hypervisor

Dyson Hypervisor is a core infrastructure project of Deepcomet AI: a purpose‑built virtualization substrate conceived as a computational Dyson sphere. Dyson is engineered to harness modern and future hardware (CPU, GPU, NPU) and to serve as the execution substrate for AI‑native orchestration, reproducible scientific simulation, and civilization‑scale modeling. Dyson is developed, governed, and released under Deepcomet AI’s policies and roadmap.

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Overview

Purpose
Dyson provides a deterministic, auditable, and extensible execution substrate for research and production workloads that require reproducibility, direct accelerator access, and strong isolation. It is optimized for modern accelerator topologies and distributed simulation fabrics rather than legacy enterprise VM density.

Scope

• Execution substrate for AI workloads, simulation nodes, and safety research.
• Platform primitives for deterministic time, coordinated snapshots, and composable VM fabrics.
• Tooling and observability for reproducible builds, signed releases, and forensic audit trails.

Audience
Researchers, platform engineers, systems developers, AI safety teams, and organizations building large‑scale reproducible simulations within the Deepcomet AI ecosystem.

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Vision and Principles

Vision
Create a resilient hypervisor substrate that enables reproducible distributed experiments, safe accelerator exposure, and long‑term evolution toward higher‑level execution substrates within Deepcomet AI.

Principles

• Determinism — reproducible clocks, replayable traces, and time fencing for scientific rigor.
• Hardware Awareness — explicit resource descriptors for NPUs and GPUs and topology‑aware scheduling.
• Safety by Design — memory‑safe core components, strict isolation, and defense‑in‑depth.
• Composability — VMs and execution contexts are first‑class simulation nodes with primitives for messaging, checkpointing, and coordinated snapshots.
• Polyglot Pragmatism — pragmatic use of multiple languages for the right layer, with a planned migration path to higher‑level substrates.
• Transparent Governance — clear contribution rules, security processes, and release policies aligned with Deepcomet AI values.

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Architecture

Layered design

• Core Runtime — memory‑safe privilege and execution logic implemented in Rust; includes Stage‑2 MMU, VM exit handling, and deterministic time enforcement.
• Device Layer — modular device models and accelerator passthrough implemented in C++; integrates advanced virtual memory allocators for GPU and NPU workloads.
• Orchestration Layer — lifecycle APIs, cluster coordination, and simulation fabric implemented in C#; exposes primitives for composing distributed simulations.
• Control Plane — CLI, telemetry, and AI scheduling prototypes implemented in Python; provides developer tooling and observability.
• Transpilation Path — long‑term plan to migrate core components into a higher‑level substrate via Deepcomet Forge for continuous optimization.

Key interfaces

• Device Interface — stable ABI for device models to enable safe incremental rewrites.
• Resource Descriptor — structured descriptors for CPU, memory, GPU, and NPU topology and capabilities.
• Time API — deterministic clock primitives, fencing, and replay hooks.
• Fabric API — primitives for inter‑VM messaging, coordinated snapshots, and distributed checkpointing.

Observability

• Structured telemetry with provenance metadata.
• Signed artifacts and reproducible build manifests.
• Traceable execution logs for replay and audit.

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Features and Capabilities

Deterministic Execution

• Deterministic clocks and replayable execution traces.
• Time fencing for coordinated distributed experiments.

Accelerator First

• Direct GPU and NPU passthrough with advanced VMA support.
• Topology‑aware placement and scheduling for accelerator locality.

Composable VM Fabric

• First‑class inter‑VM messaging and coordinated snapshot primitives.
• Checkpointing and rollback with integrity verification.

Safety and Hardening

• Memory‑safe core components and strict isolation boundaries.
• Fuzzing, sanitizers, and continuous security testing.

Developer Experience

• Modular codebase with clear language boundaries.
• Tooling for reproducible builds, signed releases, and deterministic CI.

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Getting Started

Repository name

• Recommended local directory: dyson

Clone

git clone git@github.com:DeepcometAI/dyson.git
cd dyson

Verify remotes

git remote -v
# Ensure origin points to your Deepcomet AI repository and no external upstream remote exists

Build minimal ARM64 target

./configure --target-list=aarch64-softmmu
make -j$(nproc)

Run a minimal guest

./build/bin/dyson-system-aarch64 -machine virt -cpu cortex-a72 -m 1024 -nographic

Large repository push guidance

git push --set-upstream origin main --depth=1
git push origin --all
git push origin --tags

Best practices

• Use SSH remotes to avoid HTTPS timeouts.
• Keep build artifacts and large binaries out of Git; use .gitignore and release asset storage.
• Push initial history incrementally if repository size is large.

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Development Workflow

Branching model

• main — stable releases.
• develop — integration branch.
• feature/ — feature branches.
• hotfix/ — urgent fixes.
• release/x.y.z — release stabilization branches.

Pull request policy

• Small, focused PRs with tests and clear descriptions.
• CI green required before merge.
• Two reviewers for core changes; at least one module owner reviewer for critical areas.
• RFC required for major architectural changes; RFCs live in docs/rfcs.

Code quality

• Rust — cargo fmt, clippy, unit tests.
• C++ — modern C++ standards, static analysis, sanitizers.
• C# — API contracts and integration tests.
• Python — linting, type hints, and test coverage.

Testing matrix

• Deterministic regression suite for time model.
• Nested virtualization integration tests for device passthrough and isolation.
• Fuzzing and sanitizer pipelines for core interfaces.
• Performance benchmarks for accelerator paths.

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Governance Security and Licensing

Governance

• Steering Committee approves major architecture and release decisions.
• Module Owners manage reviews and merges for their areas.
• Security Team handles vulnerability triage and disclosure.
• Module owners and contacts are listed in MAINTAINERS.md.

Security

• Responsible disclosure process documented in SECURITY.md.
• Security Team triages reports and coordinates fixes.
• Regular fuzzing, sanitizer runs, and dependency audits.
• Signed releases and reproducible build artifacts for auditability.

Licensing

• Project license policy is documented in LICENSES.md.
• All third‑party components must be audited for license compatibility before inclusion.
• Contributors must sign a Contributor License Agreement or accept a Developer Certificate of Origin.

Compliance checklist

• Preserve required license headers for any derived files.
• Maintain provenance records for imported code in docs/third-party.md.
• Record license inventory and audit notes in LICENSES.md.

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Roadmap and Milestones

Short term

• Publish governance files and divergence notice within Deepcomet AI documentation.
• Configure CI for deterministic tests, nested virtualization, and fuzzing.
• Publish first alpha release with signed artifacts and reproducible build instructions.

Medium term

• Integrate advanced virtual memory allocator for GPU memory management.
• Implement deterministic time model and full regression harness.
• Stabilize orchestration APIs and simulation fabric primitives.

Long term

• Achieve transpilation milestone via Deepcomet Forge and begin phased migration to a higher‑level substrate.
• Position Dyson as the execution substrate for reproducible civilization‑scale simulation and AI safety research within Deepcomet AI.

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Release Model and Versioning

Release channels

• alpha — experimental internal builds for research.
• beta — community testing with opt‑in features.
• stable — production releases with semantic versioning.

Release artifacts

• Signed binaries and reproducible build manifests.
• Release notes with security advisories and migration guidance.
• Performance and determinism regression reports.

Versioning

• Semantic versioning for public releases.
• Compatibility guarantees documented per release and per API.

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Contribution Guide

Onboarding

• Read CONTRIBUTING.md and sign CLA or accept DCO.
• Run the test suite locally and ensure deterministic tests pass.
• Open small, focused PRs with tests and clear descriptions.

RFC process

• Submit RFCs to docs/rfcs for changes that affect core semantics, time model, or device interfaces.
• RFCs must include motivation, design, compatibility impact, and test plan.

Mentorship

• New contributors to core modules will be paired with a module owner for the first PR.

Code of conduct

• Community interactions follow the Deepcomet AI Code of Conduct in CODE_OF_CONDUCT.md.

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Repository Layout

Path	Purpose
README.md	Project charter and quickstart
CONTRIBUTING.md	Contribution rules and CLA/DCO
SECURITY.md	Responsible disclosure and contact
MAINTAINERS.md	Module owners and contacts
LICENSES.md	Third‑party license inventory
docs/rfcs	Design proposals and decisions
docs/third-party.md	Provenance records for imported code
branding	Logos, usage guidelines, and trademarks

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Community and Contact

Channels

• Issues for bugs and feature requests.
• Pull requests for code contributions.
• RFCs in docs/rfcs for design proposals.

Maintainers and security contacts

• See MAINTAINERS.md and SECURITY.md for module owners, maintainers, and reporting details.

Deepcomet AI alignment Dyson Hypervisor is a core infrastructure project within the Deepcomet AI family. It is intended to interoperate with Deepcomet tooling, orchestration, and research platforms while maintaining independent governance and release practices.

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Appendices

Glossary

• Deterministic Time — execution semantics that allow identical replays of a workload across runs.
• VMA — virtual memory allocator for device memory management.
• Fabric — the distributed messaging and coordination layer connecting simulation nodes.

Example Commands

# Build
./configure --target-list=aarch64-softmmu
make -j$(nproc)

# Run minimal guest
./build/bin/dyson-system-aarch64 -machine virt -cpu cortex-a72 -m 1024 -nographic

# Create feature branch
git checkout -b feature/deterministic-clock

Documents to Review First

• CONTRIBUTING.md
• SECURITY.md
• MAINTAINERS.md
• LICENSES.md
• docs/rfcs/0001-dyson-manifesto.md

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This README.md is intended to be the authoritative front page for Dyson Hypervisor within Deepcomet AI. It declares project identity, technical scope, governance, and onboarding steps. If you want, I will generate the companion files CONTRIBUTING.md, SECURITY.md, MAINTAINERS.md, LICENSES.md, and an initial RFC docs/rfcs/0001-dyson-manifesto.md ready to commit.