Appendix B: Ruchy vs Julia - Architecture Deep Dive

Purpose: This appendix provides a comprehensive technical comparison between Ruchy and Julia’s architectures, helping readers understand different approaches to high-performance dynamic languages.

Target Audience: Language designers, performance engineers, and developers curious about compiler/runtime implementation strategies.

Overview: Two Paths to Performance

Both Ruchy and Julia achieve dramatically better performance than Python (15-25x geometric mean speedup), but through fundamentally different architectural approaches. This comparison illuminates the trade-offs between JIT (Just-In-Time) and AOT (Ahead-of-Time) compilation strategies.

Executive Summary

Julia’s Approach:

• Primary Strategy: LLVM-based JIT compilation at runtime
• Philosophy: “Solve the two-language problem” - write high-level code, get C performance
• Performance: 24.79x geometric mean vs Python (Chapter 21 benchmarks)
• Startup: 2.03ms for “Hello World” (including JIT compilation!)
• Runtime: C/C++ core (~310K lines) with LLVM backend
• Memory: ~250MB runtime, 150-200MB AOT binaries
• Maturity: 12+ years, v1.x stable, 9,000+ packages

Ruchy’s Approach:

• Primary Strategy: Four execution modes from interpretation to AOT compilation
• Philosophy: “Python syntax with Rust safety” - flexibility from dev to production
• Performance: 15.12x geometric mean vs Python (transpiled mode)
• Startup: 2.64ms for “Hello World” (pre-compiled binary)
• Runtime: 100% Rust (~8K lines interpreter.rs)
• Memory: ~2MB compiled binaries, no runtime dependency
• Maturity: Experimental, v0.x, growing ecosystem

Key Insight: Julia proves that excellent JIT can beat AOT languages (2.03ms vs C’s 3.02ms in BENCH-012), while Ruchy proves that multiple execution modes provide deployment flexibility while achieving 82% of C performance.

Section 1: Execution Models

Julia’s LLVM-Based JIT Pipeline

Julia’s execution model centers on runtime compilation via LLVM:

User Code → Femtolisp Parser → Lowered AST → Type Inference →
LLVM IR Generation → LLVM Optimization → JIT to Native Code → Cache

Key Characteristics:

1. Type Specialization: Julia generates optimized machine code for each unique combination of argument types
2. Method Dispatch: Multiple dispatch selects the most specific method based on all argument types
3. Caching: Compiled methods cached for reuse (avoids recompilation)
4. LLVM Optimization: Full LLVM optimization pipeline (inlining, vectorization, constant folding)
5. Runtime Overhead: Compilation happens on first call, subsequent calls use cached code

Example:

function add(x, y)
    x + y
end

add(1, 2)       # JIT compiles for (Int64, Int64)
add(1.0, 2.0)   # JIT compiles NEW version for (Float64, Float64)
add("a", "b")   # JIT compiles ANOTHER version for (String, String)

Each type combination gets its own optimized machine code!

Performance Impact:

• First call: ~1-50ms (compilation overhead)
• Subsequent calls: Native C-like speed
• Result: 24.79x geometric mean vs Python (Chapter 21)

Ruchy’s Four Execution Modes

Ruchy provides four distinct execution strategies, each with different trade-offs:

Mode 1: AST Interpreter

ruchy run script.ruchy

• Implementation: runtime/interpreter.rs (317KB, tree-walking)
• Speed: 0.37x Python (slow, but instant startup)
• Use Case: Development, debugging, REPL
• How It Works: Directly evaluates AST nodes recursively

Mode 2: Bytecode VM

ruchy bytecode script.ruchy -o script.bc
ruchy run-bytecode script.bc

• Implementation: runtime/bytecode/vm.rs (43KB stack-based VM)
• Speed: 1.49x-4.69x Python (varies by workload)
• Use Case: Scripts, CLI tools, automation
• How It Works: Compiles AST to bytecode, executes on custom VM

Mode 3: Transpiled to Rust

ruchy transpile script.ruchy > script.rs
rustc -O script.rs -o binary
./binary

• Implementation: backend/transpiler/ directory
• Speed: 15.12x Python (82% of C performance)
• Use Case: Performance-critical applications
• How It Works: Generates Rust source code, compiles via rustc/LLVM

Mode 4: Direct Compilation

ruchy compile script.ruchy -o binary
./binary

• Implementation: backend/compiler.rs (40KB)
• Speed: 14.89x Python (80% of C performance)
• Use Case: Production binaries, deployment
• How It Works: Wraps in Rust runtime, compiles via rustc toolchain

Trade-off Matrix:

Architectural Philosophy Comparison

Julia’s JIT-First Philosophy:

• Optimize for runtime information: “Know the types, generate perfect code”
• Accept compilation overhead on first call for maximum subsequent performance
• Single compilation strategy (JIT) with optional AOT (PackageCompiler)
• Large runtime footprint acceptable for scientific computing workloads

Ruchy’s Multi-Mode Philosophy:

• Optimize for deployment flexibility: “Choose the right tool for the job”
• Provide instant startup (AST) to maximum performance (compiled) spectrum
• Multiple strategies: interpretation → bytecode → AOT compilation
• Small footprint for CLI tools, DevOps scripts, embedded systems

Benchmark Evidence (BENCH-012 “Hello World”):

Julia JIT:        2.03ms  (beats all AOT languages!)
Ruchy Compiled:   2.64ms  (30% slower, no JIT overhead)
Go AOT:           2.78ms
C AOT:            3.02ms  (Ruchy is 12.6% faster!)
Rust AOT:         3.04ms
Ruchy Transpiled: 3.21ms
Ruchy Bytecode:   7.88ms  (2.12x faster than Python)
Python:          16.69ms

Conclusion: Julia’s JIT achieves remarkable performance (2.03ms including compilation!). Ruchy’s compiled mode (2.64ms) is competitive with AOT languages and actually beats C. Both approaches succeed at their goals.

Section 2: Runtime Implementation

Julia’s C/C++ Runtime

Core Components:

// julia/src/ directory structure
src/
├── julia.h            // Main runtime API
├── gc.c               // Generational garbage collector (~8000 lines)
├── task.c             // Coroutines/tasks
├── codegen.cpp        // LLVM IR generation (~15,000 lines)
├── interpreter.c      // Fallback interpreter
├── module.c           // Module system
├── gf.c               // Generic functions / multiple dispatch
└── ...                // ~310,000 total lines of C/C++

Key Implementation Details:

1. Garbage Collector: Generational mark-and-sweep

   • Young generation (nursery): Frequent, fast collections
   • Old generation: Infrequent, comprehensive collections
   • Write barriers track old→young pointers
   • Parallel GC: Multi-threaded collection phases
   • Tunable via GC.gc(), GC.enable(false)

2. Type System: Dynamic with inference

   • Types are Julia objects (first-class)
   • Type lattice: Any at top, Union{} at bottom
   • Type inference during lowering phase
   • Method specialization based on inferred types

3. Memory Model:

   • Julia manages its own heap (not C malloc)
   • Memory pools for different object sizes
   • Bump allocation for young objects
   • Compacting GC reduces fragmentation

4. Threading:

   • Native threads via libuv
   • Task scheduler for lightweight coroutines
   • Thread-local storage for task state
   • Threads.@threads macro for parallel loops

Performance Characteristics:

• GC pause times: ~1-10ms for nursery, ~50-500ms for full GC
• Memory overhead: ~2-3x object size (headers, alignment)
• Type dispatch: ~1-5ns per method call (cached)
• Task switching: ~100ns (lightweight coroutines)

Ruchy’s Rust Runtime

Core Components:

#![allow(unused)]
fn main() {
// ruchy/src/ directory structure
src/
├── lib.rs                      // Main library entry
├── frontend/
│   ├── lexer.rs               // Tokenization (34KB)
│   ├── parser/                // Recursive descent parser
│   └── ast.rs                 // AST definitions (103KB)
├── runtime/
│   ├── interpreter.rs         // Main interpreter (317KB!)
│   ├── builtins.rs            // Built-in functions (65KB)
│   ├── eval_builtin.rs        // Built-in evaluation (135KB)
│   ├── gc_impl.rs             // Garbage collector (14KB)
│   ├── gc.rs                  // GC interface (3KB)
│   ├── bytecode/
│   │   ├── vm.rs              // Bytecode VM (43KB)
│   │   ├── compiler.rs        // AST→bytecode (48KB)
│   │   └── opcode.rs          // Instruction set (10KB)
│   ├── actor.rs               // Actor system (34KB)
│   └── ...                    // ~70 total files
└── backend/
    ├── compiler.rs            // Binary compilation (40KB)
    └── transpiler/            // Rust transpiler
}

Key Implementation Details:

1. Interpreter: Tree-walking + optimizations

   • Value enum: All runtime types (Integer, Float, String, Function, etc.)
   • Pattern matching for expression evaluation
   • Environment chains for scope management
   • Tail-call optimization in some paths

2. Garbage Collector: Mark-and-sweep + reference counting

   • Rust’s ownership prevents many common GC errors
   • Arena-based allocation for performance
   • Conservative GC: Scans stacks for references
   • Integration with Rust’s Drop trait

3. Memory Model:

   • Leverages Rust’s ownership system
   • Arena allocation (arena.rs, safe_arena.rs)
   • Minimal overhead: No separate GC heap
   • Values inline when possible

4. Concurrency (Experimental):

   • Actor model (actor.rs, actor_concurrent.rs)
   • Message passing between actors
   • No shared mutable state
   • Integration with Rust async/await planned

Performance Characteristics:

• GC pause times: ~100μs-1ms (smaller heaps)
• Memory overhead: ~1.5x object size (Rust enums)
• Function call: ~10-50ns interpreted, ~1-5ns compiled
• Bytecode dispatch: ~20-100ns per instruction

Rust Safety Benefits:

• Memory safety: No segfaults, use-after-free eliminated
• Thread safety: Send/Sync traits prevent data races
• Panic safety: Unwind instead of undefined behavior
• Zero-cost abstractions: Rust optimizations preserve performance

Section 3: Type Systems & Optimization

Julia’s Type System with Specialization

Dynamic with Type Inference:

# Types are dynamic but inferred
function compute(x, y)
    result = x + y        # Inferred based on x, y types
    result * 2            # Type flows through operations
end

# Julia generates specialized code for each type combination:
compute(1, 2)            # Specialized for (Int64, Int64)
compute(1.0, 2.0)        # NEW specialization for (Float64, Float64)
compute([1,2], [3,4])    # NEW specialization for (Vector{Int64}, Vector{Int64})

How Julia Type Specialization Works:

1. First Call: Julia analyzes argument types
2. Type Inference: Determines result types for each operation
3. LLVM Codegen: Generates optimized machine code for those types
4. Caching: Stores compiled method with type signature
5. Subsequent Calls: Lookup in cache, execute native code directly

View Optimization Levels:

@code_lowered compute(1, 2)    # AST after macro expansion
@code_typed compute(1, 2)      # With inferred types
@code_llvm compute(1, 2)       # LLVM IR
@code_native compute(1, 2)     # Assembly code

Performance Impact:

• Type-stable functions: Near-C performance
• Type-unstable functions: 2-10x slower (boxing, dynamic dispatch)
• Union types: Efficient for small unions (Union{Int, Float64})
• Abstract types: Slower (can’t specialize as well)

Ruchy’s Runtime Type System

Purely Dynamic:

# All types determined at runtime
fun compute(x, y) {
    let result = x + y      // Type checked at runtime
    result * 2              // Type checked again
}

compute(1, 2)               // Value::Integer operations
compute(1.0, 2.0)           // Value::Float operations
compute("a", "b")           // Value::String operations

Ruchy Value Enum (Simplified):

#![allow(unused)]
fn main() {
pub enum Value {
    Integer(i64),
    Float(f64),
    String(Rc<String>),
    Boolean(bool),
    Array(Rc<Vec<Value>>),
    Function { params: Vec<String>, body: Expr, env: Env },
    // ... many more variants
}
}

No Specialization (Current Implementation):

• Same code path for all types
• Runtime type checks on every operation
• No LLVM-level optimization based on types
• Performance depends on rustc compiler optimizations

Trade-off:

• Simpler implementation: No type inference engine needed
• Predictable performance: No JIT warmup, no specialization overhead
• Lower peak performance: Can’t match Julia’s specialized code
• Still fast: 15.12x Python via AOT compilation (rustc optimizations)

Optimization Strategies Compared

Julia’s Optimization Pipeline:

Source Code
  ↓ Parse
AST
  ↓ Macro Expansion
Lowered AST
  ↓ Type Inference (KEY STEP!)
Typed AST
  ↓ LLVM IR Generation (specialized per type)
LLVM IR
  ↓ LLVM Optimization Passes
Optimized IR
  ↓ JIT Compilation
Native Machine Code (specialized!)

Ruchy Compiled Mode Pipeline:

Source Code
  ↓ Parse (Rust parser)
AST
  ↓ Transpile to Rust (NO type inference)
Rust Source
  ↓ rustc (Rust compiler)
LLVM IR (generic Value enum operations)
  ↓ LLVM Optimization
Optimized IR
  ↓ Compilation
Native Machine Code (not specialized)

Why Julia is Faster (24.79x vs 15.12x):

1. Type specialization: Generates optimal code for each type
2. Inlining: Can inline across type boundaries
3. SIMD: Auto-vectorization with known types
4. Constant propagation: Better with type info
5. Dead code elimination: Removes impossible paths

Why Ruchy is Still Fast:

1. Rust compiler optimizations: rustc/LLVM optimize generic code well
2. No boxing: Values stored efficiently in enum
3. Static compilation: No JIT overhead
4. Monomorphization: Rust compiler specializes generic code
5. Small binaries: ~2MB vs Julia’s 200MB

Section 4: Deployment & Distribution

Julia Deployment Options

1. Standard JIT Mode (Development):

julia script.jl

• Pros: Instant development cycle, interactive REPL
• Cons: Requires Julia runtime (~500MB), JIT warmup on first run
• Startup: 2.03ms for Hello World (including JIT!)
• Use Case: Development, data analysis, Jupyter notebooks

2. System Image (Faster Startup):

using PackageCompiler
create_sysimage([:DataFrames, :Plots, :MyPackage],
                sysimage_path="custom.so")

# Use with:
julia -J custom.so script.jl  # Much faster package loading

• Pros: Packages precompiled, faster startup
• Cons: Still requires Julia runtime, large .so files (50-200MB)
• Startup: ~500ms-1s (vs 5-10s without)
• Use Case: Production servers, repeated script execution

3. Standalone Application (PackageCompiler):

using PackageCompiler
create_app("MyProject", "MyAppCompiled",
           precompile_execution_file="precompile.jl")

# Produces:
MyAppCompiled/
├── bin/
│   └── MyApp           # Executable
└── lib/
    └── julia/          # Runtime libraries (~200MB)

• Pros: No Julia installation needed, single directory
• Cons: Large binaries (150-200MB), includes entire runtime
• Distribution: Must ship entire MyAppCompiled/ directory
• Use Case: End-user applications, non-technical users

Ruchy Deployment Options

1. Source Distribution (Interpreted):

ruchy run script.ruchy

• Pros: No compilation, instant startup
• Cons: Requires ruchy installation, slow execution (0.37x Python)
• Size: Just source code (KB)
• Use Case: Quick scripts, development only

2. Bytecode Distribution:

ruchy bytecode script.ruchy -o script.rbc
# Distribute script.rbc

# User runs:
ruchy run-bytecode script.rbc

• Pros: Faster than interpreted (1.49x-4.69x Python), protects source
• Cons: Requires ruchy installation
• Size: Bytecode file (~KB), smaller than source
• Use Case: Distributing scripts, intellectual property protection

3. Static Binary (Compiled):

ruchy compile script.ruchy -o myapp

# Distribute single binary:
./myapp    # No ruchy needed!

• Pros: No dependencies, ~2MB binary, 14.89x Python speed
• Cons: Must compile per platform
• Distribution: Single 2MB file
• Use Case: CLI tools, production deployments, embedded systems

4. Transpiled Rust (Advanced):

ruchy transpile script.ruchy > app.rs
rustc -O app.rs -o myapp

# Or integrate into Rust project:
# Can use as Rust library!

• Pros: Full Rust ecosystem access, inspectable code, 15.12x Python
• Cons: Two-step build, Rust toolchain required
• Use Case: Integration with Rust codebases, maximum control

Deployment Comparison Table

Real-World Deployment Examples:

Julia Scientific Application:

FROM julia:1.9
COPY . /app
RUN julia --project=/app -e 'using Pkg; Pkg.instantiate()'
CMD ["julia", "--project=/app", "/app/main.jl"]
# Image size: ~1.2GB

Ruchy CLI Tool:

FROM scratch
COPY myapp /myapp
ENTRYPOINT ["/myapp"]
# Image size: 2MB

Section 5: Benchmark Analysis (Chapter 21 Data)

BENCH-012: Startup Time (Hello World)

Complete Results (10 execution modes):

Key Observations:

1. Julia’s Remarkable Achievement: 2.03ms including:

   • Julia runtime initialization
   • Femtolisp parsing
   • LLVM JIT compilation
   • Code execution
   • Runtime shutdown

   This beats all AOT-compiled languages while compiling at runtime!

2. Ruchy Compiled Performance: 2.64ms

   • 30% slower than Julia
   • 12.6% FASTER than C (!)
   • No JIT overhead or warmup
   • Predictable performance

3. Ruchy Bytecode Sweet Spot: 7.88ms

   • 2.12x faster than Python
   • No compilation needed
   • Perfect for scripts

Geometric Mean Performance (7 Benchmarks)

From Chapter 21 comprehensive testing:

Benchmark Suite: String concat, binary tree, array sum, Fibonacci,
                 prime generation, nested loops, startup time

Julia:            24.79x faster than Python
C:                18.51x faster than Python
Rust:             16.49x faster than Python
Ruchy Transpiled: 15.12x faster than Python  (82% of C!)
Ruchy Compiled:   14.89x faster than Python  (80% of C!)
Go:               13.37x faster than Python
Deno:              2.33x faster than Python
Ruchy Bytecode:    1.49x faster than Python
Python:            1.00x (baseline)
Ruchy AST:         0.37x faster than Python

Analysis by Benchmark Type:

String Manipulation (BENCH-003):

• Julia: Excellent (optimized string ops)
• Ruchy Transpiled: Good (Rust String performance)
• Winner: Julia (better string optimizations)

Memory Allocation (BENCH-004 - Binary Trees):

• Julia: Excellent (tuned GC for short-lived objects)
• Ruchy: Good (Rust allocator + GC)
• Winner: Julia (GC optimized for this pattern)

Array Iteration (BENCH-005):

• Julia: Excellent (SIMD vectorization)
• Ruchy Transpiled: Excellent (within 12% of C!)
• Winner: Julia (slightly better SIMD)

Recursive Algorithms (BENCH-007 - Fibonacci):

• Julia: 10.55x Python (type-specialized recursion)
• Ruchy Transpiled: 10.55x Python (matches Julia!)
• Winner: Tie

CPU-Bound Computation (BENCH-008 - Primes):

• Julia: Excellent
• Ruchy Bytecode: Matches C within 0.26%!
• Winner: Ruchy Bytecode (surprisingly!)

Nested Loops (BENCH-011):

• Julia: Excellent (loop unrolling, SIMD)
• Ruchy Transpiled: Beats Rust! (96% of C)
• Winner: Julia (but Ruchy very close)

Startup Time (BENCH-012):

• Julia: 2.03ms (beats all AOT!)
• Ruchy: 2.64ms (beats C!)
• Winner: Julia (remarkable JIT)

Performance Tiers Summary

Tier 1: World-Class (20-25x Python)

• Julia (24.79x) - JIT + LLVM specialization

Tier 2: Native Performance (13-18x Python)

• C, Rust, Ruchy Transpiled, Ruchy Compiled, Go
• All within 30% of each other

Tier 3: High-Performance Interpreted (1.5-3x Python)

• Ruchy Bytecode, Deno (after warmup)

Tier 4: Standard Interpreted (0.4-1x Python)

• Python, Ruchy AST

Section 6: When to Use Each Language

Decision Matrix

Choose Julia When:

✅ Perfect Fit:

• Scientific/numerical computing workloads
• Data analysis, statistics, machine learning
• Linear algebra, differential equations
• Long-running computations (JIT warmup acceptable)
• Interactive exploration (REPL-driven development)
• Need existing Julia packages (DataFrames, Plots, DifferentialEquations)
• Team comfortable with Julia syntax
• ~250MB memory footprint acceptable
• Performance is critical (need that 24.79x speedup)

Example Use Cases:

# Scientific simulation
using DifferentialEquations
using Plots

# High-performance numerical code
function simulate_physics(particles, steps)
    for step in 1:steps
        compute_forces(particles)      # Type-specialized
        update_positions!(particles)    # In-place, fast
    end
end

# Runs at near-C speed after warmup

❌ Not Ideal For:

• CLI tools (200MB distribution too large)
• Docker containers (1GB+ image sizes)
• Embedded systems (no small runtime)
• Quick scripts that run once (JIT warmup wasted)
• Environments with <500MB RAM

Choose Ruchy When:

✅ Perfect Fit:

• CLI tools and utilities (2MB binaries!)
• DevOps scripts and automation
• Deployment without runtime dependencies
• Small Docker images (5MB Alpine + binary)
• Embedded systems or edge computing
• Gradual Python migration (similar syntax)
• Quick scripts (instant startup with bytecode mode)
• Want to inspect/modify generated Rust code
• Integration with existing Rust codebases
• Predictable startup time required (no JIT warmup)

Example Use Cases:

# CLI tool for log processing
fun process_logs(file) {
    let lines = read_file(file).split("\n")
    lines.filter(|l| l.contains("ERROR"))
         .map(|l| parse_log(l))
         .foreach(|log| println(log.format()))
}

# Compiles to 2MB binary
# ruchy compile log-processor.ruchy -o logproc
# Ship single ./logproc file

❌ Not Ideal For:

• Maximum numeric performance (Julia is 1.6x faster)
• Existing Julia ecosystem (9,000+ packages)
• Scientific computing with BLAS/LAPACK needs
• Workloads requiring mature threading (actors are experimental)
• Production stability (Ruchy is v0.x experimental)

Hybrid Approach: Use Both!

Scenario 1: Julia for Analysis, Ruchy for Deployment

# analysis.jl - Exploratory data analysis in Julia
using DataFrames, Plots
model = train_complex_model(data)
save_model(model, "model.json")

# deploy.ruchy - Production inference in Ruchy
let model = parse_json(read_file("model.json"))
fun predict(input) {
    model.apply(input)  // Fast inference
}

// Compile to 2MB binary for edge deployment

Scenario 2: Ruchy for CLI, Julia for Backend

// frontend CLI tool (Ruchy)
fun fetch_data(endpoint) {
    http_get(endpoint)
}

// Processes data locally, sends to Julia server

# backend.jl - Heavy computation in Julia
using Distributed
@everywhere function parallel_analyze(data)
    # Complex numerical analysis
end

Section 7: Future Directions

Julia’s Roadmap

Improving AOT Compilation:

• Better PackageCompiler (smaller binaries, faster)
• Static compilation for embedded systems
• WASM support for web deployment

Performance Improvements:

• Better GC (lower pause times)
• Improved type inference
• More aggressive inlining

Ecosystem Growth:

• More packages (currently 9,000+)
• Better tooling (debugging, profiling)
• Enhanced GPU support

Ruchy’s Roadmap (v0.x → v1.0)

Core Language:

• Type annotations (optional, for documentation)
• Improved error messages
• Pattern matching enhancements
• Trait system (like Rust traits)

Performance:

• JIT mode (via cranelift or LLVM) for long-running processes
• Better bytecode VM optimizations
• Type specialization experiments
• SIMD support in transpiler

Ecosystem:

• Standard library expansion
• Package manager
• FFI (call C/Rust code)
• More built-in data structures

Tooling:

• Language Server Protocol (LSP) completion
• Debugger improvements
• Profiler
• Better REPL

Deployment:

• Cross-compilation support
• Smaller binaries (strip more)
• WASM target (browser support)
• Embedded system targets

Convergence?

Both languages may converge on some features:

Julia → AOT:

• PackageCompiler improvements
• Static binaries under 50MB?
• Embedded system support

Ruchy → JIT:

• Optional JIT via cranelift/LLVM
• Type specialization for hot paths
• Hybrid interpreter/JIT mode

The future may see Julia with better AOT and Ruchy with optional JIT, giving users the best of both worlds!

Conclusion: Two Paths, One Goal

Julia and Ruchy represent two valid and successful approaches to building high-performance dynamic languages. Both dramatically outperform Python (15-25x geometric mean), proving that dynamic syntax doesn’t require slow execution.

What Julia Proves

“Excellent JIT compilation can match or exceed AOT languages.”

Julia’s 2.03ms startup time in BENCH-012 - including runtime initialization, parsing, LLVM JIT compilation, and execution - beating all AOT-compiled languages (Go, Rust, C) is a remarkable achievement. This demonstrates that:

1. Type specialization works: Generating code per type combination yields excellent performance
2. LLVM JIT is fast: Modern JIT compilers eliminate traditional JIT overhead
3. Large runtimes justified: When performance is critical, a 250MB runtime is acceptable
4. Scientific computing: Julia’s 24.79x geometric mean proves the approach for numerics

What Ruchy Proves

“Multiple execution modes provide deployment flexibility while achieving near-native performance.”

Ruchy’s 2.64ms startup (12.6% faster than C!) and 15.12x geometric mean (82% of C performance) with 2MB binaries demonstrates that:

1. AOT compilation still relevant: No JIT warmup, predictable performance, small binaries
2. Mode flexibility valuable: AST for debugging, bytecode for scripts, compiled for production
3. Rust safety works: Memory-safe implementation without performance penalty
4. Small footprint possible: 2MB binaries vs Julia’s 200MB enable new use cases

The Bigger Picture

Both languages validate the core premise: Dynamic languages can be fast.

Julia’s contribution: Proves JIT can beat AOT with type specialization Ruchy’s contribution: Proves AOT with multiple modes enables flexible deployment

Neither approach is “better” - they serve different needs:

• Julia: Maximum performance for scientific computing
• Ruchy: Deployment flexibility for systems programming

The future of high-performance dynamic languages likely includes both approaches, with convergence on:

• Julia: Better AOT story (smaller binaries, embedded targets)
• Ruchy: Optional JIT mode (long-running process optimization)

Final Comparison Table

Recommended Reading

To dive deeper into each language:

Julia:

• Official docs: https://docs.julialang.org
• Julia Performance Tips: https://docs.julialang.org/en/v1/manual/performance-tips/
• Package Compiler: https://github.com/JuliaLang/PackageCompiler.jl
• “Solving the Two-Language Problem” paper

Ruchy:

• Chapter 14: The Ruchy Toolchain
• Chapter 15: Binary Compilation & Deployment
• Chapter 21: Scientific Benchmarking
• test/ch21-benchmarks/ - Complete benchmark suite

Acknowledgments

This appendix is based on:

• Chapter 21 comprehensive benchmarking (7 benchmarks, 10 execution modes)
• Ruchy v3.176.0 codebase analysis
• Julia v1.x documentation and source code
• Real-world performance testing on AMD Ryzen Threadripper 7960X

Benchmarking Methodology:

• bashrs bench v6.29.0 (scientific benchmarking tool)
• 3 warmup iterations + 10 measured iterations
• Determinism verification (identical output)
• Memory tracking, statistical analysis
• Following “Are We Fast Yet?” (DLS 2016) methodology

This appendix demonstrates that the future of dynamic languages is not one approach, but a spectrum of strategies optimized for different needs. Both Julia and Ruchy succeed brilliantly at their goals.