machine-dialect-vm 0.1.0a2

High-performance Rust VM for Machine Dialect™

Machine Dialect™ Rust VM

High-performance register-based virtual machine for executing Machine Dialect™ bytecode.

Overview

This is the Phase 0 implementation of the Machine Dialect™ Rust VM, providing:

• 256 general-purpose registers
• Type-safe value system (Empty, Bool, Int, Float, String, Function, URL)
• Register-based instruction set
• MIR support (SSA phi nodes, assertions, scopes)
• Runtime operations (arithmetic, logic, comparisons, strings)
• PyO3 bindings for Python integration
• Reference counting memory management

Building

Prerequisites

• Rust 1.70+
• Python 3.9+
• maturin (install via uv sync --all-groups)

Build Instructions

From project root:

./build_vm.sh

Or manually:

cd machine_dialect_vm
maturin develop --features pyo3

Architecture

The VM uses a register-based architecture with:

• Register File: 256 registers with type tracking
• Instruction Set: ~40 register-based instructions
• Value System: Tagged union for efficient value representation
• Runtime Operations: Type-safe arithmetic and string operations
• Memory Management: Reference counting for strings and objects

Integration

The VM integrates with the Python frontend via PyO3 bindings, allowing:

1. Python compiler generates MIR
2. MIR is optimized
3. Register-based bytecode is generated
4. Rust VM executes bytecode
5. Results returned to Python

Performance

Measured performance (via Criterion benchmarks):

• Simple addition: ~1.02 µs per operation
• VM creation: ~3.49 µs
• Target: 5-10x speedup over Python interpreter

Run benchmarks:

cargo bench --bench basic_benchmark

Status

Phase 0 (MVP) implementation 92% complete:

Completed ✅

• Core VM engine with 256 registers
• Full value and type system (including arrays)
• Complete instruction set (38 opcodes)
• All runtime operations (arithmetic, logic, string, array)
• Binary bytecode loader (.mdbc format)
• PyO3 bindings with Python integration
• Python bytecode generator and serializer
• Performance benchmarks
• REPL integration (with fallback)

Remaining Work

• End-to-end execution validation
• Expanded test coverage
• Production hardening

Next Steps

Phase 1 will add:

• Type-specialized instructions
• Advanced memory management
• Performance optimizations
• Collection types (arrays, maps, sets)