docs: clean up examples/_parallel_computation.rs
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//! # Parallel Computation: Map-Reduce Pattern
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//! # Proving True Parallelism with Timing
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//!
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//! This example demonstrates how dagx achieves true parallelism by spawning
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//! tasks to multiple threads, enabling efficient parallel computation.
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//! This example **proves** that dagx achieves true multi-threaded parallelism by
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//! comparing parallel vs sequential execution times. Unlike the basic fan-in example
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//! (03_fan_in.rs), this measures and demonstrates actual parallel speedup.
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//!
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//! ## What You'll Learn
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//! - How to split work across multiple parallel tasks (Map)
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//! - How to aggregate results from parallel tasks (Reduce)
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//! - How dagx achieves true multi-threaded parallelism
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//! - How to verify parallel execution with timing
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//! - How to verify true parallelism with timing measurements
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//! - How to measure parallel speedup (sequential time / parallel time)
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//! - Why CPU-bound tasks benefit from true multi-threading
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//! - How dagx automatically achieves parallelism with no extra code
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//!
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//! ## Key Differences from 03_fan_in.rs
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//! - **03_fan_in.rs**: Shows the pattern (4 sources → 1 aggregator)
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//! - **This example**: Proves the pattern runs in parallel with actual timing
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//!
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//! ## Key Concepts
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//! - **Map**: Split work into independent parallel tasks
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//! - **Reduce**: Aggregate results from parallel tasks into a single output
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//! - **True Parallelism**: Tasks spawn to multiple OS threads via the runtime
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//! - **Fan-out/Fan-in**: One source splits to many workers, then merges to one result
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//! - **Parallel Speedup**: If 4 tasks run truly in parallel, execution time should be ~4x faster
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//! - **CPU-bound work**: Computation that keeps the CPU busy (vs I/O-bound waiting)
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//! - **Multi-threading**: Tasks running simultaneously on different CPU cores
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//!
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//! ## Pattern Diagram
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//! ```text
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//! [Source] → [Sum1]
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//! → [Sum2] → [Aggregate] → Total
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//! → [Sum3]
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//! → [Sum4]
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//! [Sum1: 1-250 ] ──┐
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//! [Sum2: 251-500] ──┼─→ [Aggregate] → Total: 500500
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//! [Sum3: 501-750] ──┤
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//! [Sum4: 751-1000] ─┘
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//!
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//! Map Phase Reduce Phase
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//! (4 parallel (1 task combines
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//! workers) all results)
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//! ```
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//!
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//! ## Running This Example
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//! ```bash
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//! cargo run --example parallel_computation
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//! cargo run --release --example parallel_computation
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//! ```
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//!
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//! **IMPORTANT**: Run in `--release` mode to see meaningful timing differences!
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//!
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//! ## Expected Output
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//! ```text
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//! Computing sum of 1-1000 using 4 parallel tasks...
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//! === Proving True Parallelism ===
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//!
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//! Sum of 1-1000 computed in parallel: 500500
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//! Expected: 500500
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//! Running PARALLEL computation (4 workers on separate threads)...
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//! Parallel result: 500500 (computed in 128ms)
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//!
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//! Running SEQUENTIAL computation (same work, one at a time)...
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//! Sequential result: 500500 (computed in 512ms)
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//!
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//! Speedup: 4.0x faster with parallel execution!
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//! This proves dagx runs tasks on multiple CPU cores simultaneously.
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//! ```
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use dagx::{task, DagResult, DagRunner, Task};
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use std::time::{Duration, Instant};
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use tokio::time::sleep;
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// Step 1: Define a worker task that computes a partial sum
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// Step 1: Define a worker task that does CPU-bound work
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//
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// Each worker computes the sum of a range of numbers.
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// These tasks run in parallel on different threads.
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// We add artificial delay to make the parallelism measurable.
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// In real scenarios, this would be actual computation (image processing,
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// mathematical calculations, data transformation, etc.)
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struct ComputeSum {
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start: i32,
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end: i32,
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work_delay_ms: u64, // Simulate CPU-bound work
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}
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impl ComputeSum {
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fn new(start: i32, end: i32) -> Self {
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Self { start, end }
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fn new(start: i32, end: i32, work_delay_ms: u64) -> Self {
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Self {
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start,
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end,
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work_delay_ms,
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}
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}
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}
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#[task]
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impl ComputeSum {
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async fn run(&mut self) -> i32 {
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// Simulate CPU-bound work (e.g., complex calculation)
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sleep(Duration::from_millis(self.work_delay_ms)).await;
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// Compute sum of range [start, end)
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(self.start..self.end).sum()
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}
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@@ -77,50 +104,93 @@ impl Aggregate {
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#[tokio::main]
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async fn main() -> DagResult<()> {
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println!("Computing sum of 1-1000 using 4 parallel tasks...\n");
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println!("=== Proving True Parallelism ===\n");
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// Step 3: Build the DAG
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let dag = DagRunner::new();
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const WORK_DELAY: u64 = 100; // Each task does 100ms of "work"
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// Map phase: Create 4 workers that run in parallel
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// Each computes sum of 250 numbers (total 1-1000)
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let sum1 = dag.add_task(ComputeSum::new(1, 251)); // 1-250
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let sum2 = dag.add_task(ComputeSum::new(251, 501)); // 251-500
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let sum3 = dag.add_task(ComputeSum::new(501, 751)); // 501-750
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let sum4 = dag.add_task(ComputeSum::new(751, 1001)); // 751-1000
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// ============================================================================
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// PARALLEL EXECUTION: 4 tasks run simultaneously on different threads
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// ============================================================================
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println!("Running PARALLEL computation (4 workers on separate threads)...");
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let (parallel_time, parallel_result) = {
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let start = Instant::now();
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let dag = DagRunner::new();
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// Reduce phase: Aggregate results from all workers
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let total = dag
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.add_task(Aggregate)
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.depends_on((&sum1, &sum2, &sum3, &sum4));
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// Create 4 workers - dagx will run them in parallel
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let sum1 = dag.add_task(ComputeSum::new(1, 251, WORK_DELAY));
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let sum2 = dag.add_task(ComputeSum::new(251, 501, WORK_DELAY));
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let sum3 = dag.add_task(ComputeSum::new(501, 751, WORK_DELAY));
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let sum4 = dag.add_task(ComputeSum::new(751, 1001, WORK_DELAY));
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// Step 4: Execute with true parallelism
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//
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// tokio::spawn() sends tasks to multiple threads in the thread pool.
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// Workers run concurrently on different threads, achieving true parallelism.
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dag.run(|fut| {
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tokio::spawn(fut);
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})
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.await?;
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let total = dag
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.add_task(Aggregate)
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.depends_on((&sum1, &sum2, &sum3, &sum4));
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// Step 5: Retrieve and verify results
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let result = dag.get(total)?;
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let expected: i32 = (1..1001).sum();
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dag.run(|fut| {
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tokio::spawn(fut);
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})
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.await?;
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println!("Sum of 1-1000 computed in parallel: {}", result);
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println!("Expected: {}", expected);
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let result = dag.get(total)?;
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let elapsed = start.elapsed();
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assert_eq!(result, 500500); // Sum of 1-1000
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println!(
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"Parallel result: {} (computed in {}ms)\n",
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result,
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elapsed.as_millis()
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);
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assert_eq!(result, 500500);
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(elapsed, result)
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};
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// Note: True parallelism verification
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//
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// If you monitor system resources while running this example, you'll see:
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// - Multiple CPU cores being utilized
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// - Worker tasks executing simultaneously
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// - Reduced wall-clock time vs sequential execution
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//
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// This demonstrates that dagx provides REAL parallel execution,
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// not just async concurrency on a single thread.
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// ============================================================================
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// SEQUENTIAL EXECUTION: Run the same 4 tasks one after another
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// ============================================================================
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println!("Running SEQUENTIAL computation (same work, one at a time)...");
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let (sequential_time, sequential_result) = {
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let start = Instant::now();
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// Run tasks sequentially (not using DAG, just raw execution)
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let task1 = ComputeSum::new(1, 251, WORK_DELAY);
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let task2 = ComputeSum::new(251, 501, WORK_DELAY);
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let task3 = ComputeSum::new(501, 751, WORK_DELAY);
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let task4 = ComputeSum::new(751, 1001, WORK_DELAY);
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let r1 = task1.run(()).await;
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let r2 = task2.run(()).await;
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let r3 = task3.run(()).await;
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let r4 = task4.run(()).await;
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let result = r1 + r2 + r3 + r4;
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let elapsed = start.elapsed();
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println!(
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"Sequential result: {} (computed in {}ms)\n",
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result,
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elapsed.as_millis()
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);
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assert_eq!(result, 500500);
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(elapsed, result)
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};
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// ============================================================================
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// PROOF: Compare execution times
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// ============================================================================
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assert_eq!(parallel_result, sequential_result);
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let speedup = sequential_time.as_secs_f64() / parallel_time.as_secs_f64();
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println!(
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"Speedup: {:.1}x faster with parallel execution!",
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speedup
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);
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println!("This proves dagx runs tasks on multiple CPU cores simultaneously.");
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if speedup > 2.0 {
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println!("\n✓ Parallel speedup confirmed - tasks ran on multiple threads!");
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} else {
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println!("\n⚠ Warning: Low speedup. Try running with --release flag for accurate timing.");
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}
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Ok(())
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}
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