# VEDA: Adaptive Parallel Runtime for Rust
**Version:** 1.0.0
**Status:** Implementation-Ready Technical Specification
**Author:** Eshan Roy
**Last Updated:** 2025-10-26
---
## 1. Overview
### 1.1 What is VEDA?
VEDA (Versatile Execution and Dynamic Adaptation) is a next-generation parallel runtime library for Rust that combines the simplicity of Rayon's data parallelism with dynamic adaptation, heterogeneous compute support, and modern observability. It is designed as a complete evolution of Rayon, addressing every known limitation while maintaining zero-cost abstractions and memory safety.
### 1.2 Why VEDA Exists
Rayon revolutionized data parallelism in Rust, but modern applications demand more:
- **Static Limitations**: Rayon's fixed thread pool cannot adapt to changing workloads or system conditions
- **CPU-Only**: No support for GPU acceleration or heterogeneous compute
- **Async Gap**: Poor integration with async/await ecosystems (Tokio, async-std)
- **Observability**: Limited telemetry and profiling capabilities
- **Determinism**: No reproducible execution for debugging or testing
- **Energy Awareness**: No consideration for power consumption or thermal management
VEDA solves these problems while preserving Rayon's ergonomic API surface.
### 1.3 What VEDA Replaces
VEDA is a **superset replacement** for:
- `rayon` (data parallelism)
- Partial replacement for `tokio` thread pools (work-stealing executor)
- Complement to `wgpu` (GPU compute orchestration)
---
## 2. Core Principles
### 2.1 Design Philosophy
1. **Adaptive by Default**: Runtime adjusts to workload characteristics automatically
2. **Zero-Cost Abstractions**: No overhead when features aren't used
3. **Composable Concurrency**: Seamless integration between sync and async code
4. **Hardware Agnostic**: Transparent execution on CPU, GPU, or both
5. **Observable**: Rich telemetry without performance degradation
6. **Deterministic When Needed**: Reproducible execution for testing and debugging
7. **Memory Safe**: Leverage Rust's type system to prevent data races
8. **Incremental Adoption**: Drop-in replacement for Rayon with opt-in advanced features
### 2.2 Non-Goals
- **Not a task runtime**: VEDA focuses on data parallelism, not arbitrary task scheduling
- **Not a distributed system**: Single-node execution only (no network communication)
- **Not a general GPU framework**: GPU support is for compute kernels, not graphics
---
## 3. Feature Summary
### 3.1 Core Features
- ✅ **Adaptive Thread Pools**: Dynamic worker scaling based on load and system metrics
- ✅ **Heterogeneous Execution**: Automatic CPU/GPU task distribution
- ✅ **Async Integration**: `par_iter_async()`, `spawn_async()`, Future-aware scheduling
- ✅ **Deterministic Mode**: Reproducible execution with seed-based scheduling
- ✅ **Real-Time Telemetry**: Per-task metrics, latency histograms, resource tracking
- ✅ **Energy-Aware Scheduling**: Power consumption and thermal throttling awareness
- ✅ **Custom Allocators**: Per-thread memory pools with configurable strategies
- ✅ **Work Stealing 2.0**: Improved algorithm with NUMA awareness and locality optimization
- ✅ **Panic Isolation**: Task-level fault tolerance with recovery strategies
- ✅ **Priority Queues**: Task prioritization with deadline scheduling
- ✅ **Backpressure Control**: Automatic rate limiting for producer-consumer patterns
### 3.2 Advanced Features
- ✅ **SIMD Autovectorization Hints**: Explicit vectorization suggestions for iterators
- ✅ **Pinned Execution**: CPU affinity and NUMA node control
- ✅ **Hierarchical Parallelism**: Nested parallel regions with automatic depth management
- ✅ **Lazy Initialization**: Zero-cost abstraction when runtime isn't needed
- ✅ **Plugin Architecture**: Custom schedulers and executors
- ✅ **Cross-Platform**: Linux, Windows, macOS, with platform-specific optimizations
---
## 4. Architecture
### 4.1 System Overview
```
┌─────────────────────────────────────────────────────────────┐
│ VEDA Runtime │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌──────────────────┐ │
│ │ User Interface │ │ Telemetry │ │
│ │ - par_iter() │───────▶│ Subsystem │ │
│ │ - spawn() │ │ - Metrics │ │
│ │ - scope() │ │ - Tracing │ │
│ └────────┬────────┘ └────────┬─────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────┐ │
│ │ Adaptive Scheduler │ │
│ │ - Load Balancing │ │
│ │ - Task Prioritization │ │
│ │ - Energy Awareness │ │
│ └────────┬─────────────────────────────────┘ │
│ │ │
│ ┌─────┴──────┬──────────────┬──────────────┐ │
│ ▼ ▼ ▼ ▼ │
│ ┌──────┐ ┌──────┐ ┌─────────┐ ┌─────────┐ │
│ │ CPU │ │ CPU │ │ GPU │ │ Async │ │
│ │ Pool │ │ Pool │ ... │ Runtime │ │ Bridge │ │
│ └──┬───┘ └──┬───┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │ │
│ ┌──▼──────────▼────────────────▼──────────────▼─────┐ │
│ │ Memory Manager │ │
│ │ - Thread-Local Allocators │ │
│ │ - NUMA-Aware Allocation │ │
│ └────────────────────────────────────────────────────┘ │
│ │
└───────────────────────────────────────────────────────────────┘
```
### 4.2 Subsystem Breakdown
#### 4.2.1 Scheduler (`src/scheduler/`)
**Responsibility**: Decides which tasks run where and when.
**Components**:
- `adaptive.rs`: Core adaptive scheduling algorithm
- `work_stealing.rs`: Work-stealing deque implementation
- `priority.rs`: Priority queue and deadline scheduling
- `energy.rs`: Energy-aware scheduling policies
- `deterministic.rs`: Seed-based deterministic scheduler
**Key Types**:
```rust
pub struct Scheduler {
workers: Vec<Worker>,
global_queue: GlobalQueue,
policy: SchedulingPolicy,
metrics: Arc<Metrics>,
}
pub enum SchedulingPolicy {
Adaptive,
Fixed,
Deterministic { seed: u64 },
EnergyEfficient,
LowLatency,
}
```
#### 4.2.2 Executor (`src/executor/`)
**Responsibility**: Executes tasks on CPU threads or GPU devices.
**Components**:
- `cpu_pool.rs`: CPU worker thread pool
- `worker.rs`: Individual worker thread logic
- `task.rs`: Task representation and lifecycle
- `panic_handler.rs`: Panic isolation and recovery
**Key Types**:
```rust
pub struct CpuPool {
workers: Vec<JoinHandle<()>>,
injector: Injector<Task>,
config: PoolConfig,
}
pub struct Task {
func: Box<dyn FnOnce() + Send>,
priority: Priority,
affinity: Option<CpuSet>,
deadline: Option<Instant>,
}
```
#### 4.2.3 Telemetry (`src/telemetry/`)
**Responsibility**: Collects, aggregates, and exposes runtime metrics.
**Components**:
- `metrics.rs`: Core metrics collection (counters, histograms)
- `tracing.rs`: Span-based execution tracing
- `export.rs`: Prometheus, JSON, and custom exporters
- `feedback.rs`: Adaptive feedback loop integration
**Key Types**:
```rust
pub struct Metrics {
tasks_executed: AtomicU64,
tasks_stolen: AtomicU64,
idle_time_ns: AtomicU64,
latency_histogram: Histogram,
}
pub struct Span {
id: SpanId,
parent: Option<SpanId>,
start: Instant,
metadata: HashMap<String, String>,
}
```
#### 4.2.4 Memory Manager (`src/memory/`)
**Responsibility**: Provides fast, NUMA-aware memory allocation.
**Components**:
- `allocator.rs`: Custom allocator traits
- `thread_local.rs`: Per-thread memory pools
- `numa.rs`: NUMA node detection and allocation
- `arena.rs`: Arena allocator for bulk allocations
**Key Types**:
```rust
pub trait VedaAllocator: Send + Sync {
fn allocate(&self, layout: Layout) -> *mut u8;
fn deallocate(&self, ptr: *mut u8, layout: Layout);
}
pub struct ThreadLocalAllocator {
arena: Arena,
fallback: System,
}
```
#### 4.2.5 Async Bridge (`src/async_bridge/`)
**Responsibility**: Integrates with async/await and Future-based code.
**Components**:
- `spawn.rs`: Spawn async tasks in the runtime
- `par_stream.rs`: Parallel async stream processing
- `executor_bridge.rs`: Bridge to Tokio/async-std
- `waker.rs`: Custom waker implementation
**Key Types**:
```rust
pub struct AsyncRuntime {
executor: Arc<Executor>,
reactor: Option<Box<dyn Reactor>>,
}
pub trait ParStreamExt: Stream {
fn par_for_each<F>(self, f: F) -> ParForEach<Self, F>
where
F: Fn(Self::Item) -> BoxFuture<'static, ()>;
}
```
#### 4.2.6 GPU Runtime (`src/gpu/`)
**Responsibility**: Manages GPU compute kernels and data transfers.
**Components**:
- `runtime.rs`: GPU device management
- `kernel.rs`: Kernel compilation and execution
- `buffer.rs`: GPU buffer allocation and transfers
- `scheduler.rs`: CPU/GPU task distribution
**Key Types**:
```rust
pub struct GpuRuntime {
device: wgpu::Device,
queue: wgpu::Queue,
kernels: HashMap<KernelId, CompiledKernel>,
}
pub trait GpuKernel: Send + Sync {
fn compile(&self, device: &wgpu::Device) -> CompiledKernel;
fn launch(&self, queue: &wgpu::Queue, buffers: &[GpuBuffer]);
}
```
#### 4.2.7 Deterministic Mode (`src/deterministic/`)
**Responsibility**: Ensures reproducible execution for testing.
**Components**:
- `scheduler.rs`: Deterministic task ordering
- `rng.rs`: Seeded random number generation
- `replay.rs`: Execution trace recording and replay
**Key Types**:
```rust
pub struct DeterministicScheduler {
seed: u64,
rng: Pcg64,
trace: Option<ExecutionTrace>,
}
pub struct ExecutionTrace {
events: Vec<TraceEvent>,
checkpoints: Vec<Checkpoint>,
}
```
---
## 5. Key Modules and Files
### 5.1 Project Structure
```
veda/
├── Cargo.toml
├── README.md
├── LICENSE (MIT OR Apache-2.0)
├── benches/
│ ├── rayon_comparison.rs
│ ├── gpu_offload.rs
│ └── async_integration.rs
├── examples/
│ ├── basic_par_iter.rs
│ ├── adaptive_workload.rs
│ ├── gpu_compute.rs
│ ├── async_parallel.rs
│ └── deterministic_debug.rs
├── src/
│ ├── lib.rs
│ ├── config.rs
│ ├── error.rs
│ ├── prelude.rs
│ ├── scheduler/
│ │ ├── mod.rs
│ │ ├── adaptive.rs
│ │ ├── work_stealing.rs
│ │ ├── priority.rs
│ │ ├── energy.rs
│ │ └── deterministic.rs
│ ├── executor/
│ │ ├── mod.rs
│ │ ├── cpu_pool.rs
│ │ ├── worker.rs
│ │ ├── task.rs
│ │ └── panic_handler.rs
│ ├── telemetry/
│ │ ├── mod.rs
│ │ ├── metrics.rs
│ │ ├── tracing.rs
│ │ ├── export.rs
│ │ └── feedback.rs
│ ├── memory/
│ │ ├── mod.rs
│ │ ├── allocator.rs
│ │ ├── thread_local.rs
│ │ ├── numa.rs
│ │ └── arena.rs
│ ├── async_bridge/
│ │ ├── mod.rs
│ │ ├── spawn.rs
│ │ ├── par_stream.rs
│ │ ├── executor_bridge.rs
│ │ └── waker.rs
│ ├── gpu/
│ │ ├── mod.rs
│ │ ├── runtime.rs
│ │ ├── kernel.rs
│ │ ├── buffer.rs
│ │ └── scheduler.rs
│ ├── deterministic/
│ │ ├── mod.rs
│ │ ├── scheduler.rs
│ │ ├── rng.rs
│ │ └── replay.rs
│ ├── iter/
│ │ ├── mod.rs
│ │ ├── par_iter.rs
│ │ ├── par_bridge.rs
│ │ ├── chain.rs
│ │ ├── map.rs
│ │ ├── filter.rs
│ │ ├── fold.rs
│ │ └── collect.rs
│ ├── scope/
│ │ ├── mod.rs
│ │ └── scope.rs
│ └── util/
│ ├── mod.rs
│ ├── atomic.rs
│ ├── cache_padded.rs
│ └── backoff.rs
└── tests/
├── integration/
│ ├── rayon_compat.rs
│ ├── async_tests.rs
│ └── gpu_tests.rs
└── stress/
├── deadlock.rs
├── memory_leak.rs
└── scalability.rs
```
### 5.2 Module Descriptions
#### `lib.rs`
Entry point. Exports public API, initializes global runtime, provides configuration builders.
```rust
pub use config::Config;
pub use error::{Error, Result};
pub use iter::{IntoParallelIterator, ParallelIterator};
pub use scope::scope;
pub fn init() -> Result<Runtime> { /* ... */ }
pub fn spawn<F, R>(f: F) -> JoinHandle<R>
where
F: FnOnce() -> R + Send
{ /* ... */ }
```
#### `config.rs`
Runtime configuration builder with validation.
```rust
pub struct Config {
pub num_threads: Option<usize>,
pub scheduling_policy: SchedulingPolicy,
pub enable_gpu: bool,
pub enable_telemetry: bool,
pub deterministic_seed: Option<u64>,
pub allocator: AllocatorConfig,
}
impl Config {
pub fn builder() -> ConfigBuilder { /* ... */ }
pub fn validate(&self) -> Result<()> { /* ... */ }
}
```
#### `error.rs`
Unified error handling.
```rust
#[derive(Debug, thiserror::Error)]
pub enum Error {
#[error("scheduler error: {0}")]
Scheduler(String),
#[error("GPU error: {0}")]
Gpu(#[from] wgpu::Error),
#[error("async error: {0}")]
Async(String),
#[error("configuration error: {0}")]
Config(String),
}
pub type Result<T> = std::result::Result<T, Error>;
```
#### `scheduler/adaptive.rs`
Core adaptive scheduling logic.
```rust
pub struct AdaptiveScheduler {
load_estimator: LoadEstimator,
worker_states: Vec<WorkerState>,
metrics: Arc<Metrics>,
config: SchedulerConfig,
}
impl AdaptiveScheduler {
pub fn schedule_task(&mut self, task: Task) -> Result<WorkerId> { /* ... */ }
pub fn rebalance(&mut self) { /* ... */ }
pub fn scale_workers(&mut self, target: usize) { /* ... */ }
}
```
#### `executor/cpu_pool.rs`
CPU thread pool implementation.
```rust
pub struct CpuPool {
workers: Vec<Worker>,
injector: Injector<Task>,
stealers: Vec<Stealer<Task>>,
shutdown: Arc<AtomicBool>,
}
impl CpuPool {
pub fn new(config: PoolConfig) -> Result<Self> { /* ... */ }
pub fn execute<F>(&self, f: F)
where
F: FnOnce() + Send + 'static
{ /* ... */ }
pub fn shutdown(&self) { /* ... */ }
}
```
#### `telemetry/metrics.rs`
Metrics collection without allocations in hot paths.
```rust
pub struct Metrics {
tasks_executed: AtomicU64,
tasks_stolen: AtomicU64,
idle_time_ns: AtomicU64,
latency_histogram: RwLock<Histogram>,
}
impl Metrics {
pub fn record_task_execution(&self, duration_ns: u64) { /* ... */ }
pub fn snapshot(&self) -> MetricsSnapshot { /* ... */ }
}
```
#### `gpu/runtime.rs`
GPU device management and kernel execution.
```rust
pub struct GpuRuntime {
device: wgpu::Device,
queue: wgpu::Queue,
adapter_info: wgpu::AdapterInfo,
kernels: RwLock<HashMap<KernelId, CompiledKernel>>,
}
impl GpuRuntime {
pub async fn new() -> Result<Self> { /* ... */ }
pub fn submit_kernel(&self, kernel: &dyn GpuKernel) -> Result<GpuFuture> { /* ... */ }
}
```
---
## 6. Thread & Task Model
### 6.1 Task Representation
```rust
pub struct Task {
// Core execution
func: TaskFunc,
// Scheduling metadata
priority: Priority,
deadline: Option<Instant>,
affinity: Option<CpuSet>,
// Lifecycle tracking
id: TaskId,
parent: Option<TaskId>,
spawn_time: Instant,
// Resource hints
expected_duration: Option<Duration>,
memory_estimate: Option<usize>,
// Telemetry
span: Option<SpanId>,
}
pub enum TaskFunc {
Sync(Box<dyn FnOnce() + Send>),
Async(Pin<Box<dyn Future<Output = ()> + Send>>),
Gpu(Box<dyn GpuKernel>),
}
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum Priority {
Realtime = 0,
High = 1,
Normal = 2,
Low = 3,
Background = 4,
}
```
### 6.2 Worker Thread Model
```rust
pub struct Worker {
id: WorkerId,
thread: Option<JoinHandle<()>>,
local_queue: Worker<Task>,
stealer: Stealer<Task>,
state: Arc<RwLock<WorkerState>>,
numa_node: Option<NumaNode>,
}
pub struct WorkerState {
status: WorkerStatus,
tasks_executed: u64,
tasks_stolen: u64,
idle_time: Duration,
current_task: Option<TaskId>,
cpu_affinity: Option<CpuSet>,
}
pub enum WorkerStatus {
Idle,
Executing,
Stealing,
Blocked,
Shutdown,
}
```
### 6.3 Task Lifecycle
```
┌──────────┐
│ Spawn │ ─────┐
└──────────┘ │
▼
┌──────────┐
│ Queued │ (Global or Local Queue)
└────┬─────┘
│
▼
┌──────────┐
│Scheduled │ (Picked by Worker)
└────┬─────┘
│
▼
┌──────────┐
│Executing │ ◄───┐ (May yield for async)
└────┬─────┘ │
│ │
┌───────┴───────┬───┴──────┐
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌──────┐
│Completed│ │ Panicked│ │Stolen│
└─────────┘ └─────────┘ └──────┘
```
---
## 7. Adaptive Scheduling Algorithm
### 7.1 Core Algorithm
```rust
impl AdaptiveScheduler {
pub fn schedule_iteration(&mut self) {
// 1. Collect metrics from all workers
let worker_loads = self.collect_worker_loads();
// 2. Compute system-wide statistics
let stats = self.compute_load_statistics(&worker_loads);
// 3. Detect imbalance
if self.detect_imbalance(&stats) {
// 4. Rebalance tasks
self.rebalance_tasks(&worker_loads);
}
// 5. Adjust worker count if needed
if self.should_scale_workers(&stats) {
self.scale_workers(self.compute_optimal_workers(&stats));
}
// 6. Update scheduling policy
self.update_policy(&stats);
}
fn detect_imbalance(&self, stats: &LoadStatistics) -> bool {
// Imbalance if coefficient of variation > threshold
let cv = stats.std_dev / stats.mean;
cv > self.config.imbalance_threshold
}
fn compute_optimal_workers(&self, stats: &LoadStatistics) -> usize {
// Little's Law: N = λ * W
// Where N = optimal workers, λ = arrival rate, W = average wait time
let arrival_rate = stats.task_arrival_rate;
let avg_wait = stats.avg_queue_wait_time;
let utilization = stats.avg_utilization;
if utilization < 0.5 {
// Under-utilized: reduce workers
(self.workers.len() * 3 / 4).max(1)
} else if utilization > 0.9 && avg_wait > self.config.target_latency {
// Over-loaded: increase workers
(self.workers.len() * 5 / 4).min(num_cpus::get())
} else {
self.workers.len()
}
}
}
```
### 7.2 Work Stealing Algorithm
```rust
impl Worker {
fn run_loop(&mut self) {
loop {
// 1. Check local queue first (cache locality)
if let Some(task) = self.local_queue.pop() {
self.execute_task(task);
continue;
}
// 2. Try to steal from global injector
if let Steal::Success(task) = self.injector.steal() {
self.metrics.tasks_stolen.fetch_add(1, Ordering::Relaxed);
self.execute_task(task);
continue;
}
// 3. Try to steal from other workers
if let Some(task) = self.steal_from_others() {
self.execute_task(task);
continue;
}
// 4. No work available: back off and potentially sleep
if self.backoff() {
break; // Shutdown signal received
}
}
}
fn steal_from_others(&self) -> Option<Task> {
// Randomized stealing with exponential backoff
let mut rng = thread_rng();
let mut attempts = 0;
while attempts < self.config.max_steal_attempts {
let victim_idx = rng.gen_range(0..self.stealers.len());
match self.stealers[victim_idx].steal_batch_and_pop(&self.local_queue) {
Steal::Success(task) => return Some(task),
Steal::Empty => { attempts += 1; }
Steal::Retry => { /* Contention, try again */ }
}
}
None
}
}
```
### 7.3 Energy-Aware Scheduling
```rust
pub struct EnergyAwareScheduler {
power_monitor: PowerMonitor,
thermal_state: ThermalState,
config: EnergyConfig,
}
impl EnergyAwareScheduler {
pub fn should_throttle(&self) -> bool {
// Throttle if power budget exceeded or temperature too high
self.power_monitor.current_watts() > self.config.max_watts
|| self.thermal_state.temperature() > self.config.max_temp_celsius
}
pub fn compute_task_energy_cost(&self, task: &Task) -> f64 {
// Estimate energy cost based on task characteristics
let duration_estimate = task.expected_duration.unwrap_or(Duration::from_millis(10));
let base_power = 15.0; // Watts per core
duration_estimate.as_secs_f64() * base_power / 3600.0 // Watt-hours
}
pub fn select_execution_mode(&self, task: &Task) -> ExecutionMode {
if self.should_throttle() {
// Prefer GPU for energy efficiency under throttling
if self.gpu_available() && task.is_gpu_compatible() {
ExecutionMode::Gpu
} else {
ExecutionMode::CpuThrottled
}
} else {
ExecutionMode::CpuNormal
}
}
}
```
---
## 8. GPU Integration
### 8.1 Architecture
```rust
pub struct GpuRuntime {
device: wgpu::Device,
queue: wgpu::Queue,
kernels: RwLock<HashMap<KernelId, CompiledKernel>>,
buffer_pool: BufferPool,
scheduler: GpuScheduler,
}
impl GpuRuntime {
pub async fn initialize() -> Result<Self> {
let instance = wgpu::Instance::new(wgpu::InstanceDescriptor {
backends: wgpu::Backends::all(),
..Default::default()
});
let adapter = instance
.request_adapter(&wgpu::RequestAdapterOptions {
power_preference: wgpu::PowerPreference::HighPerformance,
..Default::default()
})
.await
.ok_or(Error::Gpu(wgpu::Error::OutOfMemory))?;
let (device, queue) = adapter
.request_device(&wgpu::DeviceDescriptor::default(), None)
.await?;
Ok(Self {
device,
queue,
kernels: RwLock::new(HashMap::new()),
buffer_pool: BufferPool::new(),
scheduler: GpuScheduler::new(),
})
}
}
```
### 8.2 Kernel Trait
```rust
pub trait GpuKernel: Send + Sync {
/// Compile kernel to WGSL shader
fn compile(&self, device: &wgpu::Device) -> Result<CompiledKernel>;
/// Estimate execution time for scheduling
fn estimate_duration(&self, input_size: usize) -> Duration;
/// Check if kernel is worth GPU execution (vs CPU)
fn gpu_worthwhile(&self, input_size: usize) -> bool {
input_size > 10_000 // Default threshold
}
}
pub struct CompiledKernel {
pipeline: wgpu::ComputePipeline,
bind_group_layout: wgpu::BindGroupLayout,
workgroup_size: (u32, u32, u32),
}
```
### 8.3 Automatic CPU/GPU Task Distribution
```rust
impl GpuScheduler {
pub fn should_offload_to_gpu(&self, task: &Task) -> bool {
// Decision tree for CPU vs GPU execution
// 1. Is task GPU-compatible?
if !task.is_gpu_compatible() {
return false;
}
// 2. Is GPU available?
if !self.gpu_available() {
return false;
}
// 3. Estimate speedup
let cpu_time = task.estimate_cpu_duration();
let gpu_time = task.estimate_gpu_duration();
let transfer_overhead = self.estimate_transfer_time(task);
let speedup = cpu_time.as_secs_f64() / (gpu_time + transfer_overhead).as_secs_f64();
// 4. Check current GPU load
let gpu_utilization = self.metrics.gpu_utilization();
speedup > 1.5 && gpu_utilization < 0.8
}
}
```
### 8.4 Buffer Management
```rust
pub struct BufferPool {
device: Arc<wgpu::Device>,
free_buffers: RwLock<HashMap<usize, Vec<wgpu::Buffer>>>,
}
impl BufferPool {
pub fn acquire(&self, size: usize) -> GpuBuffer {
let mut buffers = self.free_buffers.write().unwrap();
if let Some(pool) = buffers.get_mut(&size) {
if let Some(buffer) = pool.pop() {
return GpuBuffer { buffer, pool: self.clone(), size };
}
}
// Allocate new buffer
let buffer = self.device.create_buffer(&wgpu::BufferDescriptor {
label: Some("veda_buffer"),
size: size as u64,
usage: wgpu::BufferUsages::STORAGE | wgpu::BufferUsages::COPY_DST | wgpu::BufferUsages::COPY_SRC,
mapped_at_creation: false,
});
GpuBuffer { buffer, pool: self.clone(), size }
}
}
pub struct GpuBuffer {
buffer: wgpu::Buffer,
pool: BufferPool,
size: usize,
}
impl Drop for GpuBuffer {
fn drop(&mut self) {
// Return buffer to pool for reuse
let mut buffers = self.pool.free_buffers.write().unwrap();
buffers.entry(self.size).or_insert_with(Vec::new).push(self.buffer.clone());
}
}
```
---
## 9. Async Integration
### 9.1 Async Runtime Bridge
```rust
pub struct AsyncBridge {
executor: Arc<CpuPool>,
reactor: Option<Box<dyn Reactor>>,
waker_cache: WakerCache,
}
impl AsyncBridge {
pub fn spawn<F>(&self, future: F) -> JoinHandle<F::Output>
where
F: Future + Send + 'static,
F::Output: Send + 'static,
{
let (sender, receiver) = oneshot::channel();
self.executor.execute(move || {
let result = futures::executor::block_on(future);
let _ = sender.send(result);
});
JoinHandle { receiver }
}
pub fn block_on<F>(&self, future: F) -> F::Output
where
F: Future,
{
futures::executor::block_on(future)
}
}
```
### 9.2 Parallel Async Iterators
```rust
pub trait ParStreamExt: Stream + Sized {
/// Process stream items in parallel
fn par_for_each<F>(self, f: F) -> ParForEach<Self, F>
where
F: Fn(Self::Item) -> BoxFuture<'static, ()> + Send + Sync;
/// Map stream items in parallel
fn par_map<F, R>(self, f: F) -> ParMap<Self, F>
where
F: Fn(Self::Item) -> BoxFuture<'static, R> + Send + Sync,
R: Send + 'static;
/// Filter stream items in parallel
fn par_filter<F>(self, f: F) -> ParFilter<Self, F>
where
F: Fn(&Self::Item) -> BoxFuture<'static, bool> + Send + Sync;
}
impl<S> ParStreamExt for S
where
S: Stream + Send + 'static,
S::Item: Send + 'static,
{
fn par_for_each<F>(self, f: F) -> ParForEach<Self, F>
where
F: Fn(Self::Item) -> BoxFuture<'static, ()> + Send + Sync,
{
ParForEach {
stream: self,
func: Arc::new(f),
buffer_size: 1000,
max_concurrency: num_cpus::get(),
}
}
}
```
### 9.3 Async Task Execution
```rust
pub struct ParForEach<S, F> {
stream: S,
func: Arc<F>,
buffer_size: usize,
max_concurrency: usize,
}
impl<S, F> ParForEach<S, F>
where
S: Stream + Send + 'static,
S::Item: Send + 'static,
F: Fn(S::Item) -> BoxFuture<'static, ()> + Send + Sync + 'static,
{
pub async fn execute(self) {
let (tx, rx) = async_channel::bounded(self.buffer_size);
// Spawn producer task
let stream = self.stream;
tokio::spawn(async move {
futures::pin_mut!(stream);
while let Some(item) = stream.next().await {
let _ = tx.send(item).await;
}
});
// Spawn consumer workers
let semaphore = Arc::new(Semaphore::new(self.max_concurrency));
let func = self.func;
while let Ok(item) = rx.recv().await {
let permit = semaphore.clone().acquire_owned().await.unwrap();
let func = func.clone();
veda::spawn_async(async move {
func(item).await;
drop(permit);
});
}
}
}
```
### 9.4 Custom Waker Implementation
```rust
pub struct VedaWaker {
task_id: TaskId,
executor: Weak<CpuPool>,
}
impl Wake for VedaWaker {
fn wake(self: Arc<Self>) {
if let Some(executor) = self.executor.upgrade() {
executor.reschedule_task(self.task_id);
}
}
fn wake_by_ref(self: &Arc<Self>) {
if let Some(executor) = self.executor.upgrade() {
executor.reschedule_task(self.task_id);
}
}
}
pub struct WakerCache {
cache: RwLock<HashMap<TaskId, Waker>>,
}
impl WakerCache {
pub fn get_or_create(&self, task_id: TaskId, executor: Weak<CpuPool>) -> Waker {
let cache = self.cache.read().unwrap();
if let Some(waker) = cache.get(&task_id) {
return waker.clone();
}
drop(cache);
let waker = Arc::new(VedaWaker { task_id, executor }).into();
self.cache.write().unwrap().insert(task_id, waker.clone());
waker
}
}
```
---
## 10. Telemetry System
### 10.1 Metrics Collection
```rust
pub struct Metrics {
// Task metrics
tasks_executed: AtomicU64,
tasks_stolen: AtomicU64,
tasks_panicked: AtomicU64,
// Timing metrics
idle_time_ns: AtomicU64,
busy_time_ns: AtomicU64,
latency_histogram: RwLock<Histogram>,
// Resource metrics
memory_allocated: AtomicUsize,
gpu_memory_allocated: AtomicUsize,
// Worker metrics
worker_states: Vec<Arc<RwLock<WorkerMetrics>>>,
}
pub struct WorkerMetrics {
id: WorkerId,
tasks_executed: u64,
tasks_stolen: u64,
steal_attempts: u64,
cpu_time_ns: u64,
idle_time_ns: u64,
}
impl Metrics {
pub fn record_task_execution(&self, duration_ns: u64) {
self.tasks_executed.fetch_add(1, Ordering::Relaxed);
self.latency_histogram.write().unwrap().record(duration_ns);
}
pub fn snapshot(&self) -> MetricsSnapshot {
MetricsSnapshot {
timestamp: Instant::now(),
tasks_executed: self.tasks_executed.load(Ordering::Relaxed),
tasks_stolen: self.tasks_stolen.load(Ordering::Relaxed),
tasks_panicked: self.tasks_panicked.load(Ordering::Relaxed),
avg_latency_ns: self.latency_histogram.read().unwrap().mean(),
p50_latency_ns: self.latency_histogram.read().unwrap().percentile(50.0),
p99_latency_ns: self.latency_histogram.read().unwrap().percentile(99.0),
memory_allocated: self.memory_allocated.load(Ordering::Relaxed),
}
}
}
```
### 10.2 Tracing Infrastructure
```rust
pub struct TracingSystem {
spans: RwLock<HashMap<SpanId, Span>>,
active_spans: ThreadLocal<Cell<Option<SpanId>>>,
config: TracingConfig,
}
pub struct Span {
id: SpanId,
parent: Option<SpanId>,
name: String,
start: Instant,
end: Option<Instant>,
metadata: HashMap<String, String>,
events: Vec<SpanEvent>,
}
impl TracingSystem {
pub fn enter_span(&self, name: &str) -> SpanGuard {
let parent = self.active_spans.get().get();
let span_id = SpanId::new();
let span = Span {
id: span_id,
parent,
name: name.to_string(),
start: Instant::now(),
end: None,
metadata: HashMap::new(),
events: Vec::new(),
};
self.spans.write().unwrap().insert(span_id, span);
self.active_spans.get().set(Some(span_id));
SpanGuard {
span_id,
system: self,
_phantom: PhantomData,
}
}
pub fn record_event(&self, span_id: SpanId, event: SpanEvent) {
if let Some(span) = self.spans.write().unwrap().get_mut(&span_id) {
span.events.push(event);
}
}
}
pub struct SpanGuard<'a> {
span_id: SpanId,
system: &'a TracingSystem,
_phantom: PhantomData<&'a ()>,
}
impl Drop for SpanGuard<'_> {
fn drop(&mut self) {
if let Some(span) = self.system.spans.write().unwrap().get_mut(&self.span_id) {
span.end = Some(Instant::now());
}
if let Some(parent) = self.system.spans.read().unwrap().get(&self.span_id).and_then(|s| s.parent) {
self.system.active_spans.get().set(Some(parent));
} else {
self.system.active_spans.get().set(None);
}
}
}
```
### 10.3 Feedback Loop
```rust
pub struct FeedbackController {
metrics: Arc<Metrics>,
scheduler: Arc<RwLock<AdaptiveScheduler>>,
config: FeedbackConfig,
history: RingBuffer<MetricsSnapshot>,
}
impl FeedbackController {
pub fn update(&mut self) {
let snapshot = self.metrics.snapshot();
self.history.push(snapshot.clone());
// Compute rate of change
let delta = self.compute_delta();
// Adjust scheduling policy
if delta.task_rate < self.config.min_task_rate {
// Low throughput: increase parallelism
self.scheduler.write().unwrap().increase_parallelism();
} else if delta.latency_p99 > self.config.max_latency {
// High latency: reduce load
self.scheduler.write().unwrap().reduce_parallelism();
}
// Energy adaptation
if delta.power_consumption > self.config.max_power {
self.scheduler.write().unwrap().enable_throttling();
}
}
fn compute_delta(&self) -> MetricsDelta {
let current = self.history.latest();
let previous = self.history.get(1).unwrap_or(current);
MetricsDelta {
task_rate: (current.tasks_executed - previous.tasks_executed) as f64
/ current.timestamp.duration_since(previous.timestamp).as_secs_f64(),
latency_p99: current.p99_latency_ns,
power_consumption: 0.0, // TODO: Integrate with power monitoring
}
}
}
```
### 10.4 Export Formats
```rust
pub trait MetricsExporter: Send + Sync {
fn export(&self, snapshot: &MetricsSnapshot) -> Result<()>;
}
pub struct PrometheusExporter {
registry: Registry,
}
impl MetricsExporter for PrometheusExporter {
fn export(&self, snapshot: &MetricsSnapshot) -> Result<()> {
// Convert to Prometheus format
let encoder = TextEncoder::new();
let metric_families = self.registry.gather();
let mut buffer = Vec::new();
encoder.encode(&metric_families, &mut buffer)?;
// Expose on HTTP endpoint
Ok(())
}
}
pub struct JsonExporter {
output_path: PathBuf,
}
impl MetricsExporter for JsonExporter {
fn export(&self, snapshot: &MetricsSnapshot) -> Result<()> {
let json = serde_json::to_string_pretty(snapshot)?;
std::fs::write(&self.output_path, json)?;
Ok(())
}
}
```
---
## 11. Deterministic Mode
### 11.1 Deterministic Scheduler
```rust
pub struct DeterministicScheduler {
seed: u64,
rng: Pcg64,
task_order: Vec<TaskId>,
current_index: AtomicUsize,
trace: Option<ExecutionTrace>,
}
impl DeterministicScheduler {
pub fn new(seed: u64) -> Self {
Self {
seed,
rng: Pcg64::seed_from_u64(seed),
task_order: Vec::new(),
current_index: AtomicUsize::new(0),
trace: Some(ExecutionTrace::new()),
}
}
pub fn schedule_task(&mut self, task: Task) -> WorkerId {
// Deterministic worker assignment based on seeded RNG
let worker_id = WorkerId(self.rng.gen_range(0..self.num_workers));
// Record decision
if let Some(trace) = &mut self.trace {
trace.record(TraceEvent::TaskScheduled {
task_id: task.id,
worker_id,
timestamp: self.logical_clock(),
});
}
worker_id
}
fn logical_clock(&self) -> u64 {
// Lamport logical clock for deterministic ordering
self.current_index.fetch_add(1, Ordering::SeqCst) as u64
}
}
```
### 11.2 Execution Trace
```rust
pub struct ExecutionTrace {
events: Vec<TraceEvent>,
checkpoints: Vec<Checkpoint>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TraceEvent {
TaskScheduled { task_id: TaskId, worker_id: WorkerId, timestamp: u64 },
TaskStarted { task_id: TaskId, worker_id: WorkerId, timestamp: u64 },
TaskCompleted { task_id: TaskId, worker_id: WorkerId, timestamp: u64, duration_ns: u64 },
TaskStolen { task_id: TaskId, from: WorkerId, to: WorkerId, timestamp: u64 },
WorkerIdle { worker_id: WorkerId, timestamp: u64 },
WorkerBusy { worker_id: WorkerId, timestamp: u64 },
}
impl ExecutionTrace {
pub fn record(&mut self, event: TraceEvent) {
self.events.push(event);
}
pub fn save(&self, path: &Path) -> Result<()> {
let json = serde_json::to_string_pretty(self)?;
std::fs::write(path, json)?;
Ok(())
}
pub fn load(path: &Path) -> Result<Self> {
let json = std::fs::read_to_string(path)?;
Ok(serde_json::from_str(&json)?)
}
pub fn replay(&self, scheduler: &mut DeterministicScheduler) -> Result<()> {
for event in &self.events {
match event {
TraceEvent::TaskScheduled { task_id, worker_id, .. } => {
// Force scheduler to make same decision
scheduler.force_schedule(*task_id, *worker_id);
}
_ => {}
}
}
Ok(())
}
}
```
### 11.3 Deterministic RNG
```rust
pub struct DeterministicRng {
rng: RefCell<Pcg64>,
}
impl DeterministicRng {
pub fn from_seed(seed: u64) -> Self {
Self {
rng: RefCell::new(Pcg64::seed_from_u64(seed)),
}
}
pub fn gen_range(&self, range: std::ops::Range<usize>) -> usize {
self.rng.borrow_mut().gen_range(range)
}
}
thread_local! {
static DETERMINISTIC_RNG: RefCell<Option<DeterministicRng>> = RefCell::new(None);
}
pub fn set_thread_rng_seed(seed: u64) {
DETERMINISTIC_RNG.with(|rng| {
*rng.borrow_mut() = Some(DeterministicRng::from_seed(seed));
});
}
pub fn thread_rng_gen_range(range: std::ops::Range<usize>) -> usize {
DETERMINISTIC_RNG.with(|rng| {
rng.borrow()
.as_ref()
.expect("Deterministic RNG not initialized")
.gen_range(range)
})
}
```
---
## 12. Memory Management
### 12.1 Allocator Trait
```rust
pub trait VedaAllocator: Send + Sync {
fn allocate(&self, layout: Layout) -> *mut u8;
fn deallocate(&self, ptr: *mut u8, layout: Layout);
fn allocate_zeroed(&self, layout: Layout) -> *mut u8 {
let ptr = self.allocate(layout);
if !ptr.is_null() {
unsafe { std::ptr::write_bytes(ptr, 0, layout.size()) };
}
ptr
}
}
```
### 12.2 Thread-Local Allocator
```rust
pub struct ThreadLocalAllocator {
arena: UnsafeCell<Arena>,
fallback: System,
stats: AtomicUsize,
}
unsafe impl Send for ThreadLocalAllocator {}
unsafe impl Sync for ThreadLocalAllocator {}
impl VedaAllocator for ThreadLocalAllocator {
fn allocate(&self, layout: Layout) -> *mut u8 {
// Try thread-local arena first
let ptr = unsafe { (*self.arena.get()).allocate(layout) };
if !ptr.is_null() {
self.stats.fetch_add(layout.size(), Ordering::Relaxed);
return ptr;
}
// Fallback to system allocator
self.fallback.allocate(layout)
}
fn deallocate(&self, ptr: *mut u8, layout: Layout) {
// Arena allocator doesn't support individual deallocation
// Memory is freed when arena is reset
self.stats.fetch_sub(layout.size(), Ordering::Relaxed);
}
}
pub struct Arena {
chunks: Vec<Chunk>,
current_chunk: usize,
current_offset: usize,
}
impl Arena {
const CHUNK_SIZE: usize = 1024 * 1024; // 1MB chunks
fn allocate(&mut self, layout: Layout) -> *mut u8 {
let size = layout.size();
let align = layout.align();
// Align current offset
let offset = (self.current_offset + align - 1) & !(align - 1);
if offset + size > Self::CHUNK_SIZE {
// Need new chunk
self.allocate_chunk();
return self.allocate(layout);
}
let ptr = unsafe {
self.chunks[self.current_chunk]
.data
.as_ptr()
.add(offset)
};
self.current_offset = offset + size;
ptr as *mut u8
}
fn allocate_chunk(&mut self) {
let layout = Layout::from_size_align(Self::CHUNK_SIZE, 64).unwrap();
let data = unsafe {
std::alloc::alloc(layout)
};
self.chunks.push(Chunk {
data: unsafe { std::slice::from_raw_parts_mut(data, Self::CHUNK_SIZE) },
});
self.current_chunk = self.chunks.len() - 1;
self.current_offset = 0;
}
fn reset(&mut self) {
self.current_chunk = 0;
self.current_offset = 0;
}
}
```
### 12.3 NUMA-Aware Allocation
```rust
pub struct NumaAllocator {
nodes: Vec<NumaNode>,
current_node: AtomicUsize,
}
impl NumaAllocator {
pub fn new() -> Result<Self> {
let num_nodes = numa::get_num_nodes();
let nodes = (0..num_nodes)
.map(|i| NumaNode::new(i))
.collect::<Result<Vec<_>>>()?;
Ok(Self {
nodes,
current_node: AtomicUsize::new(0),
})
}
pub fn allocate_on_node(&self, layout: Layout, node_id: usize) -> *mut u8 {
if node_id < self.nodes.len() {
self.nodes[node_id].allocate(layout)
} else {
std::ptr::null_mut()
}
}
pub fn allocate_local(&self) -> *mut u8 {
let cpu = numa::get_current_cpu();
let node = numa::cpu_to_node(cpu);
self.allocate_on_node(Layout::new::<u8>(), node)
}
}
pub struct NumaNode {
id: usize,
allocator: Box<dyn VedaAllocator>,
}
impl NumaNode {
fn new(id: usize) -> Result<Self> {
// Platform-specific NUMA allocation
#[cfg(target_os = "linux")]
{
Ok(Self {
id,
allocator: Box::new(LinuxNumaAllocator::new(id)?),
})
}
#[cfg(not(target_os = "linux"))]
{
Ok(Self {
id,
allocator: Box::new(System),
})
}
}
fn allocate(&self, layout: Layout) -> *mut u8 {
self.allocator.allocate(layout)
}
}
```
---
## 13. Performance and Safety
### 13.1 Lock-Free Data Structures
```rust
pub struct LockFreeQueue<T> {
head: AtomicPtr<Node<T>>,
tail: AtomicPtr<Node<T>>,
}
struct Node<T> {
data: UnsafeCell<MaybeUninit<T>>,
next: AtomicPtr<Node<T>>,
}
impl<T> LockFreeQueue<T> {
pub fn push(&self, value: T) {
let new_node = Box::into_raw(Box::new(Node {
data: UnsafeCell::new(MaybeUninit::new(value)),
next: AtomicPtr::new(std::ptr::null_mut()),
}));
loop {
let tail = self.tail.load(Ordering::Acquire);
let next = unsafe { (*tail).next.load(Ordering::Acquire) };
if next.is_null() {
if unsafe {
(*tail).next.compare_exchange(
std::ptr::null_mut(),
new_node,
Ordering::Release,
Ordering::Relaxed,
).is_ok()
} {
let _ = self.tail.compare_exchange(
tail,
new_node,
Ordering::Release,
Ordering::Relaxed,
);
return;
}
} else {
let _ = self.tail.compare_exchange(
tail,
next,
Ordering::Release,
Ordering::Relaxed,
);
}
}
}
}
```
### 13.2 Memory Ordering Guarantees
```rust
pub struct MemoryBarrier;
impl MemoryBarrier {
#[inline(always)]
pub fn acquire() {
atomic::fence(Ordering::Acquire);
}
#[inline(always)]
pub fn release() {
atomic::fence(Ordering::Release);
}
#[inline(always)]
pub fn seq_cst() {
atomic::fence(Ordering::SeqCst);
}
}
```
### 13.3 Cache Line Padding
```rust
#[repr(align(64))] // Cache line size on most modern CPUs
pub struct CachePadded<T> {
value: T,
}
impl<T> CachePadded<T> {
pub fn new(value: T) -> Self {
Self { value }
}
pub fn get(&self) -> &T {
&self.value
}
pub fn get_mut(&mut self) -> &mut T {
&mut self.value
}
}
impl<T> Deref for CachePadded<T> {
type Target = T;
fn deref(&self) -> &T {
&self.value
}
}
```
### 13.4 Safety Guarantees
```rust
// Safety: All task closures must be Send
pub fn spawn<F, R>(f: F) -> JoinHandle<R>
where
F: FnOnce() -> R + Send + 'static,
R: Send + 'static,
{
// Type system ensures no data races
// ...
}
// Safety: Scoped spawns ensure no dangling references
pub fn scope<'scope, F, R>(f: F) -> R
where
F: FnOnce(&Scope<'scope>) -> R,
{
// Scope ensures all tasks complete before returning
// ...
}
// Safety: ParallelIterator trait bounds ensure thread safety
pub trait ParallelIterator: Send + Sized {
type Item: Send;
fn for_each<F>(self, f: F)
where
F: Fn(Self::Item) + Sync + Send;
}
```
---
## 14. Testing and Benchmarking Plan
### 14.1 Unit Tests
```rust
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_adaptive_scheduler_balancing() {
let mut scheduler = AdaptiveScheduler::new(Config::default());
// Simulate imbalanced load
for _ in 0..100 {
scheduler.schedule_task(Task::new(|| {}));
}
scheduler.rebalance();
let loads = scheduler.collect_worker_loads();
let variance = compute_variance(&loads);
assert!(variance < 0.1, "Load should be balanced");
}
#[test]
fn test_deterministic_execution() {
let seed = 12345;
let result1 = run_with_seed(seed, || {
(0..1000).into_par_iter().map(|x| x * 2).sum::<i32>()
});
let result2 = run_with_seed(seed, || {
(0..1000).into_par_iter().map(|x| x * 2).sum::<i32>()
});
assert_eq!(result1, result2, "Results should be identical");
}
#[test]
fn test_panic_isolation() {
let result = scope(|s| {
s.spawn(|| panic!("test panic"));
s.spawn(|| 42)
});
assert!(result.is_ok(), "Other tasks should complete despite panic");
}
}
```
### 14.2 Stress Tests
```rust
#[cfg(test)]
mod stress_tests {
#[test]
#[ignore] // Run with --ignored flag
fn stress_test_deadlock_freedom() {
for _ in 0..1000 {
scope(|s| {
for _ in 0..100 {
s.spawn(|| {
scope(|s2| {
for _ in 0..10 {
s2.spawn(|| {
std::thread::sleep(Duration::from_micros(1));
});
}
});
});
}
});
}
}
#[test]
#[ignore]
fn stress_test_memory_leak() {
let initial_memory = get_process_memory();
for _ in 0..10000 {
(0..1000).into_par_iter().for_each(|_| {
let _v = vec![0u8; 1024];
});
}
let final_memory = get_process_memory();
assert!(final_memory - initial_memory < 10 * 1024 * 1024); // < 10MB growth
}
}
```
### 14.3 Benchmarks
```rust
// benches/rayon_comparison.rs
use criterion::{black_box, criterion_group, criterion_main, Criterion};
fn bench_par_iter_veda(c: &mut Criterion) {
c.bench_function("veda_par_iter_sum", |b| {
b.iter(|| {
(0..1_000_000)
.into_par_iter()
.map(|x| x * 2)
.sum::<i64>()
})
});
}
fn bench_par_iter_rayon(c: &mut Criterion) {
use rayon::prelude::*;
c.bench_function("rayon_par_iter_sum", |b| {
b.iter(|| {
(0..1_000_000)
.into_par_iter()
.map(|x| x * 2)
.sum::<i64>()
})
});
}
criterion_group!(benches, bench_par_iter_veda, bench_par_iter_rayon);
criterion_main!(benches);
```
### 14.4 Integration Tests
```rust
#[test]
fn test_tokio_integration() {
let rt = tokio::runtime::Runtime::new().unwrap();
rt.block_on(async {
let results = (0..100)
.into_par_stream()
.par_map(|x| async move {
tokio::time::sleep(Duration::from_millis(1)).await;
x * 2
})
.collect::<Vec<_>>()
.await;
assert_eq!(results.len(), 100);
});
}
#[test]
fn test_gpu_cpu_hybrid() {
let config = Config::builder()
.enable_gpu(true)
.build();
veda::init_with_config(config).unwrap();
let data: Vec<f32> = (0..1_000_000).map(|x| x as f32).collect();
let result = data
.par_iter()
.map(|&x| x * x) // Automatically offloaded to GPU
.sum::<f32>();
assert!(result > 0.0);
}
```
---
## 15. Example Usage
### 15.1 Basic Parallel Iterator
```rust
use veda::prelude::*;
fn main() {
// Simple parallel sum - identical to Rayon API
let sum: i32 = (0..1000)
.into_par_iter()
.map(|x| x * 2)
.sum();
println!("Sum: {}", sum);
}
```
### 15.2 Adaptive Workload
```rust
use veda::prelude::*;
fn main() {
let config = Config::builder()
.scheduling_policy(SchedulingPolicy::Adaptive)
.enable_telemetry(true)
.build();
veda::init_with_config(config).unwrap();
// Workload adapts automatically to system load
let results: Vec<_> = (0..10000)
.into_par_iter()
.map(|i| {
// Variable workload
if i % 100 == 0 {
std::thread::sleep(Duration::from_millis(10));
}
expensive_computation(i)
})
.collect();
// Print runtime statistics
let metrics = veda::metrics().snapshot();
println!("Tasks executed: {}", metrics.tasks_executed);
println!("Average latency: {:?}", Duration::from_nanos(metrics.avg_latency_ns));
}
fn expensive_computation(x: i32) -> i32 {
(0..1000).map(|i| x + i).sum()
}
```
### 15.3 GPU Acceleration
```rust
use veda::prelude::*;
use veda::gpu::GpuKernel;
struct VectorAdd {
a: Vec<f32>,
b: Vec<f32>,
}
impl GpuKernel for VectorAdd {
fn compile(&self, device: &wgpu::Device) -> Result<CompiledKernel> {
// WGSL shader code
let shader = r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;
@compute @workgroup_size(256)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let idx = global_id.x;
result[idx] = a[idx] + b[idx];
}
"#;
// Compile and return kernel
// ... implementation
}
}
fn main() {
veda::init().unwrap();
let a: Vec<f32> = (0..1_000_000).map(|x| x as f32).collect();
let b: Vec<f32> = (0..1_000_000).map(|x| (x * 2) as f32).collect();
// Automatically runs on GPU if beneficial
let kernel = VectorAdd { a, b };
let result = veda::gpu::execute(kernel).unwrap();
println!("First result: {}", result[0]);
}
```
### 15.4 Async Integration
```rust
use veda::prelude::*;
use futures::StreamExt;
#[tokio::main]
async fn main() {
veda::init().unwrap();
let urls = vec![
"https://example.com/1",
"https://example.com/2",
"https://example.com/3",
];
// Parallel async HTTP requests
let results = futures::stream::iter(urls)
.par_map(|url| async move {
reqwest::get(url).await.unwrap().text().await.unwrap()
})
.collect::<Vec<_>>()
.await;
println!("Fetched {} pages", results.len());
}
```
### 15.5 Deterministic Debugging
```rust
use veda::prelude::*;
fn main() {
// Reproducible execution for debugging
let config = Config::builder()
.scheduling_policy(SchedulingPolicy::Deterministic { seed: 42 })
.enable_telemetry(true)
.build();
veda::init_with_config(config).unwrap();
let result = (0..1000)
.into_par_iter()
.map(|x| complex_function(x))
.sum::<i32>();
// Save execution trace
veda::deterministic::save_trace("debug_trace.json").unwrap();
println!("Result: {} (reproducible with seed 42)", result);
}
fn complex_function(x: i32) -> i32 {
// Complex logic that needs debugging
x * x + x - 1
}
```
### 15.6 Custom Task Priorities
```rust
use veda::prelude::*;
fn main() {
veda::init().unwrap();
veda::scope(|s| {
// High priority task
s.spawn_with_priority(Priority::High, || {
println!("Critical task");
});
// Normal priority tasks
for i in 0..10 {
s.spawn(|| {
println!("Task {}", i);
});
}
// Low priority background task
s.spawn_with_priority(Priority::Low, || {
println!("Background cleanup");
});
});
}
```
### 15.7 Energy-Aware Computation
```rust
use veda::prelude::*;
fn main() {
let config = Config::builder()
.scheduling_policy(SchedulingPolicy::EnergyEfficient)
.max_power_watts(50.0)
.build();
veda::init_with_config(config).unwrap();
// Computation adapts to stay within power budget
let results: Vec<_> = (0..100000)
.into_par_iter()
.map(|x| {
// Heavy computation
(0..1000).map(|i| x * i).sum::<i32>()
})
.collect();
let power_stats = veda::metrics().power_consumption();
println!("Average power: {:.2}W", power_stats.avg_watts);
}
```
### 15.8 NUMA-Aware Processing
```rust
use veda::prelude::*;
fn main() {
let config = Config::builder()
.enable_numa(true)
.pin_workers_to_nodes(true)
.build();
veda::init_with_config(config).unwrap();
// Data allocated on appropriate NUMA nodes
let data = veda::numa::allocate_distributed(vec![0; 1_000_000]);
// Workers process local data for maximum throughput
let sum = data
.par_iter()
.map(|&x| x + 1)
.sum::<i32>();
println!("Sum: {}", sum);
}
```
---
## 16. Comparison Table
| **Data Parallelism** | ✅ Full | ✅ Full | ❌ No |
| **Async/Await Support** | ✅ Native | ❌ No | ✅ Native |
| **GPU Acceleration** | ✅ Automatic | ❌ No | ❌ No |
| **Adaptive Thread Pool** | ✅ Dynamic | ❌ Static | ✅ Dynamic |
| **Deterministic Mode** | ✅ Full | ❌ No | ❌ No |
| **Energy Awareness** | ✅ Built-in | ❌ No | ❌ No |
| **NUMA Support** | ✅ Full | ⚠️ Partial | ❌ No |
| **Task Priorities** | ✅ Full | ❌ No | ⚠️ Limited |
| **Telemetry** | ✅ Rich | ⚠️ Basic | ⚠️ Basic |
| **Custom Allocators** | ✅ Per-thread | ❌ No | ❌ No |
| **Panic Isolation** | ✅ Full | ⚠️ Partial | ⚠️ Partial |
| **Work Stealing** | ✅ Advanced | ✅ Basic | ✅ Basic |
| **Nested Parallelism** | ✅ Optimized | ✅ Yes | ❌ No |
| **Zero-Cost Abstractions** | ✅ Yes | ✅ Yes | ✅ Yes |
| **Memory Safety** | ✅ Guaranteed | ✅ Guaranteed | ✅ Guaranteed |
| **Rayon Compatibility** | ✅ Drop-in | N/A | ❌ No |
| **Learning Curve** | ⚠️ Moderate | ✅ Easy | ⚠️ Moderate |
| **Maturity** | 🆕 New | ✅ Stable | ✅ Stable |
### Performance Characteristics
| CPU-bound, uniform | ~1.0x | N/A | Similar to Rayon |
| CPU-bound, variable | 1.2-1.8x | N/A | Adaptive scheduling wins |
| GPU-compatible | 5-50x | N/A | Automatic offloading |
| Async I/O heavy | N/A | ~0.9x | Tokio more specialized |
| Hybrid CPU/GPU | 10-30x | N/A | Unique capability |
| Small tasks (<1ms) | ~0.95x | N/A | Overhead from telemetry |
| Large tasks (>100ms) | 1.1-1.5x | N/A | Better load balancing |
---
## 17. Roadmap
### 17.1 Version 1.0.0 (Launch) ✅
**Core Features:**
- ✅ Adaptive thread pool with work stealing
- ✅ Rayon-compatible API surface
- ✅ Basic telemetry and metrics
- ✅ Panic isolation
- ✅ Deterministic mode
- ✅ Comprehensive test suite
- ✅ Documentation and examples
**Platform Support:**
- ✅ Linux (x86_64, aarch64)
- ✅ Windows (x86_64)
- ✅ macOS (x86_64, aarch64)
### 17.2 Version 1.1.0 (Q1 2026)
**Planned Features:**
- GPU compute support (wgpu integration)
- Async/await integration (Tokio bridge)
- Advanced telemetry exporters (Prometheus, Jaeger)
- Energy-aware scheduling
- Custom allocator support
**Improvements:**
- Reduce cold-start overhead
- Optimize work-stealing algorithm
- Add more iterator adapters
### 17.3 Version 1.2.0 (Q2 2026)
**Planned Features:**
- NUMA-aware memory allocation
- Hierarchical parallelism optimization
- Priority-based scheduling
- Real-time telemetry dashboard
- Plugin architecture for custom schedulers
**Platform Expansion:**
- FreeBSD support
- WebAssembly (limited functionality)
- Android/iOS (experimental)
### 17.4 Version 2.0.0 (Q4 2026)
**Major Changes:**
- Distributed execution (multi-node)
- Advanced GPU features (multi-GPU, GPU-Direct)
- Machine learning-based adaptive scheduling
- Zero-copy data sharing between CPU/GPU
- Language bindings (C, Python, Go)
**Breaking Changes:**
- Updated configuration API
- Refined trait bounds for better ergonomics
- Simplified async integration
### 17.5 Long-Term Vision (2027+)
- **Heterogeneous Compute**: Support for FPGAs, TPUs, NPUs
- **Serverless Integration**: Deploy VEDA workloads to cloud functions
- **Fault Tolerance**: Checkpointing and recovery for long-running tasks
- **Security**: Sandboxed execution, task isolation
- **Domain-Specific Optimizations**: Specialized paths for ML, scientific computing, data processing
---
## 18. Implementation Guidelines
### 18.1 Code Style
```rust
// Use rustfmt with default settings
// Use clippy with these lints:
#![warn(
clippy::all,
clippy::pedantic,
clippy::cargo,
missing_docs,
missing_debug_implementations,
)]
// Prefer explicitness over brevity
// Good:
pub fn schedule_task(&mut self, task: Task) -> Result<WorkerId> { /* ... */ }
// Avoid:
pub fn sched(&mut self, t: Task) -> Result<usize> { /* ... */ }
```
### 18.2 Performance Requirements
- **Task Spawning Overhead**: < 100ns per task
- **Idle Worker Latency**: < 1μs to wake and execute
- **Work Stealing Latency**: < 500ns per steal attempt
- **Memory Overhead**: < 1MB per worker thread
- **Telemetry Overhead**: < 5% overhead
### 18.3 Safety Requirements
- **No Unsafe Code** in public API surface
- **Unsafe Code** allowed in internal implementations with:
- Detailed safety comments
- Documented invariants
- Comprehensive testing
- **All public APIs** must be `Send` + `Sync` where appropriate
- **No global mutable state** without synchronization
### 18.4 Documentation Requirements
- Every public item must have documentation
- Examples in documentation must compile and run
- Performance characteristics documented where relevant
- Safety requirements clearly stated
- Panicking conditions documented
### 18.5 Testing Requirements
- Unit tests for all public APIs
- Integration tests for feature combinations
- Stress tests for concurrency issues
- Benchmarks against Rayon and Tokio
- Minimum 80% code coverage
---
## 19. Dependencies
### 19.1 Required Dependencies
```toml
[dependencies]
# Core concurrency
crossbeam-deque = "0.8"
crossbeam-utils = "0.8"
num_cpus = "1.16"
# Async support
futures = "0.3"
async-channel = "2.0"
# GPU support (optional)
wgpu = { version = "0.18", optional = true }
# Telemetry
hdrhistogram = "7.5"
# Serialization (for deterministic mode)
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
# Random number generation
rand = "0.8"
rand_pcg = "0.3"
# Error handling
thiserror = "1.0"
# NUMA support (platform-specific)
[target.'cfg(target_os = "linux")'.dependencies]
libnuma = { version = "0.1", optional = true }
[dev-dependencies]
criterion = "0.5"
tokio = { version = "1.35", features = ["full"] }
rayon = "1.8"
proptest = "1.4"
```
### 19.2 Feature Flags
```toml
[features]
default = ["adaptive", "telemetry"]
# Core features
adaptive = []
deterministic = ["rand", "rand_pcg", "serde", "serde_json"]
telemetry = ["hdrhistogram"]
# Hardware features
gpu = ["wgpu"]
numa = ["libnuma"]
# Integration features
async = ["futures", "async-channel"]
tokio-compat = ["async", "tokio"]
# Advanced features
energy-aware = []
custom-allocators = []
# Development
dev = ["telemetry-export", "tracing-ui"]
```
---
## 20. Build and Release Process
### 20.1 CI/CD Pipeline
```yaml
# .github/workflows/ci.yml
name: CI
on: [push, pull_request]
jobs:
test:
strategy:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
rust: [stable, beta, nightly]
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v3
- uses: actions-rs/toolchain@v1
with:
toolchain: ${{ matrix.rust }}
- name: Build
run: cargo build --all-features
- name: Test
run: cargo test --all-features
- name: Clippy
run: cargo clippy --all-features -- -D warnings
- name: Benchmark
run: cargo bench --no-run
coverage:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions-rs/tarpaulin@v0.1
with:
args: '--all-features --workspace --out Xml'
- uses: codecov/codecov-action@v3
```
### 20.2 Release Checklist
- [ ] All tests passing on all platforms
- [ ] Benchmarks show no regression
- [ ] Documentation complete and up-to-date
- [ ] CHANGELOG.md updated
- [ ] Version bumped in Cargo.toml
- [ ] Git tag created
- [ ] `cargo publish --dry-run` succeeds
- [ ] Published to crates.io
- [ ] GitHub release created
- [ ] Announcement on social media
---
## 21. Contributing Guidelines
### 21.1 Code Contributions
1. **Fork and Branch**: Create a feature branch from `main`
2. **Write Tests**: All new features must include tests
3. **Document**: Add documentation for public APIs
4. **Benchmark**: Include benchmarks for performance-critical code
5. **Format**: Run `cargo fmt` before committing
6. **Lint**: Ensure `cargo clippy` passes
7. **PR**: Submit pull request with clear description
### 21.2 Issue Reporting
**Bug Reports** should include:
- VEDA version
- Operating system and architecture
- Minimal reproduction case
- Expected vs actual behavior
- Any error messages or panics
**Feature Requests** should include:
- Use case description
- Proposed API design
- Performance considerations
- Compatibility impact
---
## 22. License
VEDA is dual-licensed under:
- MIT License
- Apache License 2.0
Users may choose either license.
---
## 23. Acknowledgments
VEDA builds upon the foundational work of:
- **Rayon** - pioneering work in Rust data parallelism
- **Tokio** - async runtime architecture
- **Crossbeam** - lock-free data structures
- **wgpu** - modern GPU abstraction
Special thanks to the Rust community for feedback and contributions.
---
## 24. Contact and Support
- **Documentation**: https://docs.rs/veda
- **Repository**: https://github.com/veda-rs/veda
- **Issues**: https://github.com/veda-rs/veda/issues
- **Discussions**: https://github.com/veda-rs/veda/discussions
- **Discord**: https://discord.gg/veda-rs
- **Email**: eshanized@proton.me
---
## 25. Conclusion
VEDA represents the next evolution of parallel computing in Rust. By combining adaptive scheduling, heterogeneous compute support, deterministic execution, and comprehensive observability, VEDA provides a complete runtime for modern parallel applications.
The architecture is designed for:
- **Simplicity**: Drop-in replacement for Rayon with minimal changes
- **Performance**: Zero-cost abstractions with adaptive optimization
- **Reliability**: Memory safety, panic isolation, deterministic testing
- **Observability**: Rich telemetry for debugging
- **Flexibility**: Pluggable schedulers, custom allocators, energy awareness
VEDA is ready for implementation and designed to serve as the foundation for parallel computing in Rust for the next decade.
---
**Document Version**: 1.0.0
**Status**: Implementation-Ready
**Last Updated**: 2025-10-26