🧠 STARKLANG — AI Concurrency Primitives Specification

STARKLANG's concurrency model is designed specifically for AI workloads—batch processing, parallel inference, and multi-model serving.

This spec defines the core concurrency primitives optimized for tensor operations, model serving, and AI pipeline execution.

📌 AI-Focused Design Philosophy

Goal	Mechanism
Batch processing	Parallel tensor operations
Model serving	Async inference with batching
Multi-model workflows	Actor-based model isolation
Memory safety	Tensor ownership in concurrent contexts
Performance	GPU-aware scheduling, zero-copy operations

🔹 Core Concurrency Constructs

✅ 1️⃣ async Functions

async fn fetch_data(url: String) -> Response:
    let result = http.get(url)
    return result

✅ 2️⃣ await Expression

let response = await fetch_data("https://api.stark.ai")

✅ 3️⃣ Future<T>

let future: Future<Int> = async fn compute(): return 99

Methods:

• .await()
• .then(fn)
• .catch(fn)

✅ 4️⃣ spawn — Lightweight Task Creation

spawn fn worker(task_id: Int):
    compute(task_id)

let handle = spawn compute_task()
let result = handle.join()

🔀 AI Parallel Patterns

✅ parallel_inference(model, batches)

let batches: Vec<Tensor<Float32>[32, 128]> = dataset.batch(32)
let results = parallel_inference(model, batches)

✅ parallel_tensor_map(fn, tensor)

let normalized = parallel_tensor_map(normalize_fn, large_tensor)

Other AI patterns:

• parallel_model_ensemble(models, input)
• parallel_preprocess(data_loader, transforms)

🔄 Concurrency Control

✅ Channels

let ch: Channel<Int> = channel()

spawn fn producer():
    ch.send(42)

let result = ch.recv()

Channel Types:

• Channel<T>
• BroadcastChannel<T>
• Select support (planned)

🧠 AI Model Actor System

✅ Defining a Model Actor

actor ModelServer:
    model: Model
    mut request_count: Int = 0

    fn on_receive(input: Tensor<Float32>) -> Tensor<Float32>:
        request_count += 1
        return model.predict(input)

✅ Multi-Model Serving

let classifier = spawn_actor(ModelServer::new(load_model("classifier.pt")))
let generator = spawn_actor(ModelServer::new(load_model("generator.pt")))

let classification = classifier.ask(features).await
let generation = generator.ask(prompt_embeddings).await

✅ ModelActorRef<Input, Output> API

• ask(input: Input) -> Future<Output>
• batch_ask(inputs: Vec<Input>) -> Future<Vec<Output>>
• get_stats() -> ModelStats

⚠ Error Handling & Fault Isolation

• Actor supervision trees (planned)
• Try/Catch inside async blocks
• Actor panics are isolated

🔒 Shared Memory Zones (Advanced / Optional)

shared let zone: SharedMap<String, Float32>

Access via .lock(), .read(), .write().

📊 Execution Runtime Architecture

Component	Role
Task Scheduler	Runs async tasks
Actor Registry	Tracks actor lifecycles
Mailbox Engine	Routes messages
Channel Multiplexer	Manages select logic
Runtime Profiler	Tracks task/actor metrics

⚙ Compiler Safety Checks

Feature	Enforcement
Shared Memory Mutation	Borrow checker
Concurrent &mut Access	Compile-time rejection
Unawaited Future	Warning/Error
Deadlocks	Static analysis (planned)

📌 Full Example: AI Inference Pipeline

actor InferenceService:
    classifier: Model
    embedder: Model
    
    fn on_receive(text: String) -> ClassificationResult:
        let embeddings = embedder.predict(tokenize(text))
        let logits = classifier.predict(embeddings)
        return ClassificationResult::new(softmax(logits))

let service = spawn_actor(InferenceService::new(
    load_model("embedder.pt"),
    load_model("classifier.pt")
))

let texts: Vec<String> = load_text_batch()
let results = parallel_map(texts, |text| service.ask(text).await)

🛠 AI Concurrency Tools (Planned)

Tool	Description
Model Profiler	Track inference latency and throughput
Batch Optimizer	Suggest optimal batch sizes
Memory Visualizer	Tensor memory usage across actors
GPU Utilization Monitor	Track device usage

✅ Summary: AI Concurrency Features

Feature	Supported
async/await for model serving	✅
parallel_inference	✅
parallel_tensor_map	✅
Model Actor system	✅
Batch processing	✅
Multi-model workflows	✅
GPU-aware scheduling	⏳ Planned
Dynamic batching	⏳ Planned

STARKLANG is now equipped to deliver safe, scalable, AI-optimized concurrency—ready for production ML serving, batch processing, and intelligent inference pipelines.