ReefVM/IDEAS.md

ReefVM Architectural Improvement Ideas

This document contains architectural ideas for improving ReefVM. These focus on enhancing the VM's capabilities through structural improvements rather than just adding new opcodes.

1. Scope Resolution Optimization

Current Issue: Variable lookups are O(n) through the scope chain on every LOAD. This becomes expensive in deeply nested closures.

Architectural Solution: Implement static scope analysis with lexical addressing:

// Instead of: LOAD x  (runtime scope chain walk)
// Compile to: LOAD_FAST 2 1  (scope depth 2, slot 1 - O(1) lookup)

class Scope {
  locals: Map<string, Value>
  parent?: Scope

  // NEW: Add indexed slots for fast access
  slots: Value[]  // Direct array access
  nameToSlot: Map<string, number>  // Compile-time mapping
}

Benefits:

• O(1) variable access instead of O(n)
• Critical for hot loops and deeply nested functions
• Compiler can still fall back to named lookup for dynamic cases

---

2. Module System Architecture

Current Gap: No way to organize code across multiple files or create reusable libraries.

Architectural Solution: Add first-class module support:

// New opcodes: IMPORT, EXPORT, MAKE_MODULE
// New bytecode structure:
type Bytecode = {
  instructions: Instruction[]
  constants: Constant[]
  exports?: Map<string, number>  // Exported symbols
  imports?: Import[]               // Import declarations
}

type Import = {
  modulePath: string
  symbols: string[]  // [] means import all
  alias?: string
}

Pattern:

MAKE_MODULE .module_body
EXPORT add
EXPORT subtract
HALT

.module_body:
  MAKE_FUNCTION (x y) .add_impl
  RETURN

Benefits:

• Code organization and reusability
• Circular dependency detection at load time
• Natural namespace isolation
• Enables standard library architecture

---

3. Source Map Integration

Current Issue: Runtime errors show bytecode addresses, not source locations.

Architectural Solution: Add source mapping layer:

type Bytecode = {
  instructions: Instruction[]
  constants: Constant[]
  sourceMap?: SourceMap  // NEW
}

type SourceMap = {
  file?: string
  mappings: SourceMapping[]  // Instruction index → source location
}

type SourceMapping = {
  instruction: number
  line: number
  column: number
  source?: string  // Original source text
}

Benefits:

• Meaningful error messages with line/column
• Debugger can show original source
• Stack traces map to source code
• Critical for production debugging

---

4. Debugger Hook Architecture

Current Gap: No way to pause execution, inspect state, or step through code.

Architectural Solution: Add debug event system:

class VM {
  debugger?: Debugger

  async execute(instruction: Instruction) {
    // Before execution
    await this.debugger?.onInstruction(this.pc, instruction, this)

    // Execute
    switch (instruction.op) { ... }

    // After execution
    await this.debugger?.afterInstruction(this.pc, this)
  }
}

interface Debugger {
  breakpoints: Set<number>
  onInstruction(pc: number, instruction: Instruction, vm: VM): Promise<void>
  afterInstruction(pc: number, vm: VM): Promise<void>
  onCall(fn: Value, args: Value[]): Promise<void>
  onReturn(value: Value): Promise<void>
  onException(error: Value): Promise<void>
}

Benefits:

• Step-through debugging
• Breakpoints at any instruction
• State inspection at any point
• Non-invasive (no bytecode modification)
• Can build IDE integrations

---

5. Bytecode Optimization Pass Framework

Current Gap: Bytecode is emitted directly, no optimization.

Architectural Solution: Add optimization pipeline:

type Optimizer = (bytecode: Bytecode) => Bytecode

// Framework for composable optimization passes
class BytecodeOptimizer {
  passes: Optimizer[] = []

  add(pass: Optimizer): this {
    this.passes.push(pass)
    return this
  }

  optimize(bytecode: Bytecode): Bytecode {
    return this.passes.reduce((bc, pass) => pass(bc), bytecode)
  }
}

// Example passes:
const optimizer = new BytecodeOptimizer()
  .add(constantFolding)      // PUSH 2; PUSH 3; ADD → PUSH 5
  .add(deadCodeElimination)  // Remove unreachable code after HALT/RETURN
  .add(jumpChaining)         // JUMP .a → .a: JUMP .b → JUMP .b directly
  .add(peepholeOptimization) // DUP; POP → (nothing)

Benefits:

• Faster execution without changing compiler
• Can add passes without modifying VM
• Composable and testable
• Enables aggressive optimizations (inlining, constant folding, etc.)

---

6. Value Memory Management Architecture

Current Issue: No tracking of memory usage, no GC hooks, unbounded growth.

Architectural Solution: Add memory management layer:

class MemoryManager {
  allocatedBytes: number = 0
  maxBytes?: number

  allocateValue(value: Value): Value {
    const size = this.sizeOf(value)
    if (this.maxBytes && this.allocatedBytes + size > this.maxBytes) {
      throw new Error('Out of memory')
    }
    this.allocatedBytes += size
    return value
  }

  sizeOf(value: Value): number {
    // Estimate memory footprint
  }

  // Hook for custom GC
  gc?: () => void
}

class VM {
  memory: MemoryManager

  // All value-creating operations check memory
  push(value: Value) {
    this.memory.allocateValue(value)
    this.stack.push(value)
  }
}

Benefits:

• Memory limits for sandboxing
• Memory profiling
• Custom GC strategies
• Prevents runaway memory usage

---

7. Instruction Profiler Architecture

Current Gap: No way to identify performance bottlenecks in bytecode.

Architectural Solution: Add instrumentation layer:

class Profiler {
  instructionCounts: Map<number, number> = new Map()
  instructionTime: Map<number, number> = new Map()
  hotFunctions: Map<number, FunctionProfile> = new Map()

  recordInstruction(pc: number, duration: number) {
    this.instructionCounts.set(pc, (this.instructionCounts.get(pc) || 0) + 1)
    this.instructionTime.set(pc, (this.instructionTime.get(pc) || 0) + duration)
  }

  getHotSpots(): HotSpot[] {
    // Identify most-executed instructions
  }

  generateReport(): ProfileReport {
    // Human-readable performance report
  }
}

class VM {
  profiler?: Profiler

  async execute(instruction: Instruction) {
    const start = performance.now()
    // ... execute ...
    const duration = performance.now() - start
    this.profiler?.recordInstruction(this.pc, duration)
  }
}

Benefits:

• Identify hot loops and functions
• Guide optimization efforts
• Measure impact of changes
• Can feed into JIT compiler (future)

---

8. Standard Library Plugin Architecture

Current Issue: Native functions registered manually, no standard library structure.

Architectural Solution: Module-based native libraries:

interface NativeModule {
  name: string
  exports: Record<string, any>
  init?(vm: VM): void
}

class VM {
  modules: Map<string, NativeModule> = new Map()

  registerModule(module: NativeModule) {
    this.modules.set(module.name, module)
    module.init?.(this)

    // Auto-register exports to global scope
    for (const [name, value] of Object.entries(module.exports)) {
      this.set(name, value)
    }
  }

  loadModule(name: string): NativeModule {
    return this.modules.get(name) || throw new Error(`Module ${name} not found`)
  }
}

// Example usage:
const mathModule: NativeModule = {
  name: 'math',
  exports: {
    sin: Math.sin,
    cos: Math.cos,
    sqrt: Math.sqrt,
    PI: Math.PI
  }
}

vm.registerModule(mathModule)

Benefits:

• Organized standard library
• Lazy loading of modules
• Third-party plugin system
• Clear namespace boundaries

---

9. Streaming Bytecode Execution

Current Limitation: Must load entire bytecode before execution.

Architectural Solution: Incremental bytecode loading:

class StreamingBytecode {
  chunks: BytecodeChunk[] = []

  append(chunk: BytecodeChunk) {
    // Remap addresses, merge constants
    this.chunks.push(chunk)
  }

  getInstruction(pc: number): Instruction | undefined {
    // Resolve across chunks
  }
}

class VM {
  async runStreaming(stream: ReadableStream<BytecodeChunk>) {
    for await (const chunk of stream) {
      this.bytecode.append(chunk)
      await this.continue()  // Execute new chunk
    }
  }
}

Benefits:

• Execute before full load (faster startup)
• Network streaming of bytecode
• Incremental compilation
• Better REPL experience

---

10. Type Annotation System (Optional Runtime Types)

Current Gap: All values dynamically typed, no way to enforce types.

Architectural Solution: Optional type metadata:

type TypedValue = Value & {
  typeAnnotation?: TypeAnnotation
}

type TypeAnnotation =
  | { kind: 'number' }
  | { kind: 'string' }
  | { kind: 'array', elementType?: TypeAnnotation }
  | { kind: 'dict', valueType?: TypeAnnotation }
  | { kind: 'function', params: TypeAnnotation[], return: TypeAnnotation }

// New opcodes: TYPE_CHECK, TYPE_ASSERT
// Functions can declare parameter types:
MAKE_FUNCTION (x:number y:string) .body

Benefits:

• Catch type errors earlier
• Self-documenting code
• Enables static analysis tools
• Optional (doesn't break existing code)
• Can enable optimizations (known number type → skip toNumber())

---

11. VM State Serialization

Current Gap: Can't save/restore VM execution state.

Architectural Solution: Serializable VM state:

class VM {
  serialize(): SerializedState {
    return {
      instructions: this.instructions,
      constants: this.constants,
      pc: this.pc,
      stack: this.stack.map(serializeValue),
      callStack: this.callStack.map(serializeFrame),
      scope: serializeScope(this.scope),
      handlers: this.handlers
    }
  }

  static deserialize(state: SerializedState): VM {
    const vm = new VM(/* ... */)
    vm.restore(state)
    return vm
  }
}

Benefits:

• Save/restore execution state
• Distributed computing (send state to workers)
• Crash recovery
• Time-travel debugging
• Checkpoint/restart

---

12. Async Iterator Support

Current Gap: Iterators work via break, but no async iteration.

Architectural Solution: First-class async iteration:

// New value type:
type Value = ... | { type: 'async_iterator', value: AsyncIterableIterator<Value> }

// New opcodes: MAKE_ASYNC_ITERATOR, AWAIT_NEXT, YIELD_ASYNC

// Pattern:
for_await (item in asyncIterable) {
  // Compiles to AWAIT_NEXT loop
}

Benefits:

• Stream processing
• Async I/O without blocking
• Natural async patterns
• Matches JavaScript async iterators

---

Priority Recommendations

Tier 1 (Highest Impact):

1. Source Map Integration - Critical for usability
2. Module System - Essential for scaling beyond toy programs
3. Scope Resolution Optimization - Performance multiplier

Tier 2 (High Value):

4. Debugger Hook Architecture - Developer experience game-changer
5. Standard Library Plugin Architecture - Enables ecosystem
6. Bytecode Optimization Framework - Performance without complexity

Tier 3 (Nice to Have):

7. Instruction Profiler - Guides future optimization
8. Memory Management - Important for production use
9. VM State Serialization - Enables advanced use cases

Tier 4 (Future/Experimental):

10. Type Annotations - Optional, doesn't break existing code
11. Streaming Bytecode - Mostly useful for large programs
12. Async Iterators - Specialized use case

---

Design Principles

These improvements focus on:

• Performance (scope optimization, bytecode optimization)
• Developer Experience (source maps, debugger, profiler)
• Scalability (modules, standard library architecture)
• Production Readiness (memory management, serialization)

All ideas maintain ReefVM's core design philosophy of simplicity, orthogonality, and explicit behavior.