High-Performance Parallel Computation in Web Workers with SharedArrayBuffer and Atomics: A Guide to Avoiding Deadlocks

JavaScript’s single-threaded nature has long been a challenge for CPU-intensive tasks, as long-running operations can block the main thread, leading to unresponsive user interfaces. Web Workers offer a solution by enabling background script execution, but traditional communication via postMessage() involves data copying, which can be a significant bottleneck for large datasets.

Enter SharedArrayBuffer (SAB) and Atomics. SharedArrayBuffer provides a mechanism for true shared memory between the main thread and Web Workers (or among multiple Workers). This allows multiple threads to access and manipulate the same block of memory directly, eliminating costly data copying. The Atomics object, in turn, provides essential tools for orchestrating this shared access safely, preventing race conditions and ensuring data integrity through atomic operations.

However, the power of shared memory concurrency comes with its own set of complexities, most notably the risk of race conditions and deadlocks. This article provides a comprehensive guide for experienced developers on leveraging SharedArrayBuffer and Atomics for high-performance parallel computation in Web Workers, with a strong focus on understanding and preventing deadlocks.

Prerequisites: Enabling Cross-Origin Isolation

For security reasons, primarily to mitigate risks from speculative execution vulnerabilities like Spectre, SharedArrayBuffer is only available in web pages that are in a “cross-origin isolated” state. Achieving this state requires specific HTTP headers to be set by your server.

The two key headers are:

Cross-Origin-Opener-Policy (COOP): Set to same-origin. This policy ensures that top-level documents do not share a browsing context group with cross-origin documents.
Cross-Origin-Embedder-Policy (COEP): Set to require-corp (or credentialless for more flexibility with iframes). This policy prevents a document from loading any cross-origin resources that don’t explicitly grant the document permission.

Example: Setting COOP and COEP headers in Node.js/Express:

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// Server-side code (e.g., Express middleware)
app.use((req, res, next) => {
  res.setHeader('Cross-Origin-Opener-Policy', 'same-origin');
  res.setHeader('Cross-Origin-Embedder-Policy', 'require-corp');
  next();
});

Example: Setting COOP and COEP headers in Nginx:

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# Nginx server configuration
add_header 'Cross-Origin-Opener-Policy' 'same-origin' always;
add_header 'Cross-Origin-Embedder-Policy' 'require-corp' always;

Once these headers are correctly configured, you can verify if your page is cross-origin isolated in your client-side JavaScript using window.crossOriginIsolated:

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if (window.crossOriginIsolated) {
  console.log("Cross-origin isolation is active. SharedArrayBuffer can be used.");
} else {
  console.error(
    "Cross-origin isolation is NOT active. SharedArrayBuffer is unavailable."
  );
  // Provide guidance or link to documentation on setting COOP/COEP headers.
  // See https://web.dev/coop-coep/ and 
  // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference\
  // /Global_Objects/SharedArrayBuffer#security_requirements
}

Without window.crossOriginIsolated being true, any attempt to use SharedArrayBuffer will result in an error.

Understanding `SharedArrayBuffer` (SAB)

A SharedArrayBuffer (SAB) object represents a fixed-length raw binary data buffer, similar to an ArrayBuffer. The crucial difference is that an SAB can be shared across multiple JavaScript threads (main thread and Web Workers, or multiple workers). When an SAB is sent from one thread to another via postMessage(), a reference to the same memory block is passed, not a copy of the data.

Key characteristics of SharedArrayBuffer:

Shared Memory: Allows direct memory access from multiple threads.
No Copying Overhead: Significantly faster for large data compared to postMessage() with ArrayBuffer.
TypedArray Views: Like ArrayBuffer, SABs are not directly readable or writable. You must use TypedArray views (e.g., Int32Array, Float64Array, Uint8Array) to interpret and manipulate the underlying binary data.

Creating and Sharing a SharedArrayBuffer:

The main thread typically creates the SharedArrayBuffer and then shares it with workers.

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// main.js - Main thread script

if (!window.crossOriginIsolated) {
    console.error("This environment is not cross-origin isolated.");
} else {
    // Create a SharedArrayBuffer of 1024 bytes
    const buffer = new SharedArrayBuffer(1024);

    // Create a 32-bit integer view on the buffer
    const int32View = new Int32Array(buffer);
    console.log("Initial value at index 0:", int32View[0]); // Typically 0

    // Initialize some data
    int32View[0] = 5;
    int32View[1] = 10;

    // Create a worker and post the SharedArrayBuffer
    // Note: The buffer itself is passed, not a structured clone.
    const worker = new Worker('worker.js');
    worker.postMessage({ type: 'sab', data: buffer });

    worker.onmessage = (event) => {
        if (event.data.type === 'sab_updated') {
            // The worker has modified the SAB, read the updated value
            // No need to receive the buffer back, we already have access.
            console.log("Value at index 0 after worker update:", int32View[0]);
        }
    };
}

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// worker.js - Web Worker script

self.onmessage = (event) => {
    if (event.data.type === 'sab') {
        const sharedBuffer = event.data.data;
        const int32View = new Int32Array(sharedBuffer);

        console.log("Worker received value at index 0:", int32View[0]); // 5

        // Modify the shared data
        // This modification will be visible to the main thread
        // (and any other workers sharing this buffer)
        // without explicit postMessage of the data itself.
        // However, synchronization is needed for safe concurrent writes.
        int32View[0] = int32View[0] * 2; // Example modification

        // Potentially use Atomics for safe modification:
        // Atomics.store(int32View, 0, currentVal * 2);
        // Atomics.add(int32View, 1, 5); // Example atomic addition

        // Notify main thread (optional, for control flow)
        self.postMessage({ type: 'sab_updated' });
    }
};

In this example, worker.js directly modifies int32View[0]. If multiple threads were to modify this location concurrently without proper synchronization, race conditions would occur. This is where Atomics comes in.

Understanding `Atomics`

The Atomics object provides static methods for performing atomic operations on SharedArrayBuffer locations when viewed with an integer TypedArray (like Int8Array, Uint8Array, Int16Array, Uint16Array, Int32Array, Uint32Array, BigInt64Array, or BigUint64Array). “Atomic” means these operations are performed as a single, indivisible step, preventing interruptions from other threads and ensuring that no other thread observes the operation half-complete.

Why Atomics are essential:

Prevent Race Conditions: Ensure that read-modify-write sequences are completed without interference.
Ordered Memory Access: Provide stronger memory ordering guarantees than non-atomic operations, crucial for predictable behavior in concurrent programs.

Key Atomics operations:

Atomics.load(typedArray, index): Atomically reads the value at index.
Atomics.store(typedArray, index, value): Atomically stores value at index.
Atomics.add(typedArray, index, value): Atomically adds value to the element at index and returns the old value at index.
Atomics.sub(typedArray, index, value): Atomically subtracts value.
Atomics.and(typedArray, index, value), Atomics.or(...), Atomics.xor(...): Atomic bitwise operations.
Atomics.exchange(typedArray, index, value): Atomically replaces the value at index with value and returns the old value.
Atomics.compareExchange(typedArray, index, expectedValue, replacementValue): If the value at index currently matches expectedValue, it’s replaced with replacementValue. Returns the old value at index. This is the cornerstone for implementing locks.

Example: Shared Counter with Atomics.add()

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// main.js - sets up a shared counter
// Assume 'buffer' is a SharedArrayBuffer from the previous example
const sharedCounterView = new Int32Array(buffer, 0, 1); // Use first 4 bytes

// In worker.js or multiple workers:
// const counterView = new Int32Array(sharedBuffer, 0, 1);
// To increment the counter safely:
Atomics.add(sharedCounterView, 0, 1);

// To read the current value safely:
// let currentValue = Atomics.load(sharedCounterView, 0);

Each call to Atomics.add(sharedCounterView, 0, 1) will reliably increment the counter, even if multiple workers call it simultaneously.

Implementing Synchronization: Locks (Mutexes)

Often, a sequence of operations on shared data needs to be performed exclusively by one thread at a time. This is known as a “critical section.” A common way to protect critical sections is by using a mutex (Mutual Exclusion lock).

A basic mutex can be implemented using Atomics.compareExchange(). A common approach is a spinlock, where a thread repeatedly tries to acquire the lock until it succeeds.

Basic Mutex Implementation (Spinlock):

A designated memory location in the SharedArrayBuffer acts as the lock. For instance, 0 means unlocked, and 1 means locked.

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// mutex.js - A simple Mutex class using Atomics
// This lock should be stored in a SharedArrayBuffer, e.g., an Int32Array.
// const lockView = new Int32Array(sharedStatusBuffer, lockIndex, 1);
// const UNLOCKED = 0;
// const LOCKED = 1;

class SpinLockMutex {
    // lockArray is an Int32Array of length 1, on a SharedArrayBuffer
    // lockIndex is usually 0 if lockArray is dedicated to this lock
    constructor(lockArray, lockIndex = 0) {
        this.lockArray = lockArray;
        this.lockIndex = lockIndex;
        this.UNLOCKED = 0;
        this.LOCKED = 1;
    }

    lock() {
        // Try to change from UNLOCKED to LOCKED.
        // If it was UNLOCKED, compareExchange returns UNLOCKED and we got the lock.
        // If it was already LOCKED, it returns LOCKED, and we spin.
        while (
            Atomics.compareExchange(
                this.lockArray,
                this.lockIndex,
                this.UNLOCKED, // Expected value
                this.LOCKED      // Replacement value
            ) !== this.UNLOCKED
        ) {
            // Spin/wait. In a real scenario, you might add a short pause
            // or use Atomics.wait for more complex locks, but this is a
            // basic spinlock. Be cautious with long spins.
        }
        // Lock acquired
    }

    unlock() {
        // To unlock, we simply store UNLOCKED.
        // This must only be called by the thread that holds the lock.
        // A more robust mutex might store a thread ID to verify.
        const prevValue = Atomics.exchange(
            this.lockArray,
            this.lockIndex,
            this.UNLOCKED
        );

        if (prevValue !== this.LOCKED) {
            // This indicates a potential issue, like unlocking a lock
            // that wasn't properly locked or double unlocking.
            console.warn("Mutex: Unlocking an unexpected state.");
        }
    }
}

// Usage in a worker (conceptual):
// const sabForLock = new SharedArrayBuffer(4); // 4 bytes for one Int32
// const lockView = new Int32Array(sabForLock);
// const mutex = new SpinLockMutex(lockView);
//
// mutex.lock();
// try {
//   // --- Critical Section Start ---
//   // Access shared resources protected by this mutex
//   // --- Critical Section End ---
// } finally {
//   mutex.unlock();
// }

Spinlocks are simple but can be inefficient if contention is high or critical sections are long, as they consume CPU cycles while spinning.

Efficient Waiting: `Atomics.wait()` and `Atomics.notify()`

For more efficient locks, especially when waits might be longer, Atomics.wait() and Atomics.notify() are preferred. Atomics.wait() allows a thread to sleep (block) until it’s notified or a timeout occurs, consuming fewer CPU resources than spinning.

Atomics.wait(typedArray, index, valueToWaitFor, timeout): If typedArray[index] is valueToWaitFor, the agent sleeps. Returns ‘ok’, ’not-equal’, or ’timed-out’. Crucially, Atomics.wait() can only be used in Web Workers, not on the main thread, as it is blocking.
Atomics.notify(typedArray, index, count): Wakes up count (or all if count is Infinity) agents waiting on typedArray[index]. Returns the number of agents woken.

Mutex using Atomics.wait() and Atomics.notify():

This creates a more CPU-friendly lock.

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// mutexWaitNotify.js - A Mutex using Atomics.wait and Atomics.notify
// lockView is an Int32Array(sharedBuffer, lockIndex, 1)
// UNLOCKED = 0, LOCKED_NO_WAITERS = 1, LOCKED_WITH_WAITERS = 2

class WaitNotifyMutex {
    constructor(lockView, lockIndex = 0) {
        this.lockView = lockView; // An Int32Array of length 1
        this.idx = lockIndex;
        this.UNLOCKED = 0;
        this.LOCKED_NO_WAITERS = 1; // Locked, no one currently waiting
        this.LOCKED_WITH_WAITERS = 2; // Locked, threads are waiting
    }

    lock() {
        // Attempt to acquire the lock directly
        let currentLockValue = Atomics.compareExchange(
            this.lockView, this.idx, this.UNLOCKED, this.LOCKED_NO_WAITERS
        );

        if (currentLockValue === this.UNLOCKED) {
            return; // Lock acquired immediately
        }

        // Lock was not free, prepare to wait
        do {
            // If lock is LOCKED_NO_WAITERS, try to change to LOCKED_WITH_WAITERS
            // If it's already LOCKED_WITH_WAITERS, or still UNLOCKED (race),
            // currentLockValue will be updated.
            if (currentLockValue === this.LOCKED_NO_WAITERS) {
                Atomics.compareExchange(
                    this.lockView, this.idx,
                    this.LOCKED_NO_WAITERS, this.LOCKED_WITH_WAITERS
                );
            }
            // Wait if the lock is held (either _NO_WAITERS or _WITH_WAITERS)
            // The value we wait on is one of the locked states.
            // If it changes to UNLOCKED before we wait, wait() returns 'not-equal'.
            Atomics.wait(
                this.lockView, this.idx,
                this.LOCKED_WITH_WAITERS, // Value we expect if we need to wait
                Infinity // No timeout
            );
            // After waking up, try to acquire the lock again
            // from UNLOCKED to LOCKED_NO_WAITERS (or _WITH_WAITERS if high)
            currentLockValue = Atomics.compareExchange(
                this.lockView, this.idx, this.UNLOCKED, this.LOCKED_WITH_WAITERS
            );
        } while (currentLockValue !== this.UNLOCKED);
        // Lock acquired
    }

    unlock() {
        const previousState = Atomics.exchange(
            this.lockView, this.idx, this.UNLOCKED
        );

        // If the previous state was LOCKED_WITH_WAITERS, it means
        // other threads might be sleeping, so we need to wake one up.
        if (previousState === this.LOCKED_WITH_WAITERS) {
            Atomics.notify(this.lockView, this.idx, 1); // Wake one waiting thread
        }
    }
}

// Conceptual usage (in a worker):
// const sabForLock = new SharedArrayBuffer(4);
// const lockView = new Int32Array(sabForLock);
// const mutex = new WaitNotifyMutex(lockView);
//
// mutex.lock();
// try { /* Critical section */ }
// finally { mutex.unlock(); }

This implementation is more complex but significantly reduces CPU usage when threads are waiting for a lock.

The Specter of Deadlocks

A deadlock is a state in concurrent programming where two or more threads are blocked forever, each waiting for the other to release a resource that it needs. This typically occurs when threads attempt to acquire multiple locks.

Classic Deadlock Scenario:

Imagine two workers, Worker A and Worker B, and two locks, Lock 1 and Lock 2.

Worker A acquires Lock 1.
Worker B acquires Lock 2.
Worker A now tries to acquire Lock 2 (but it’s held by Worker B). Worker A blocks.
Worker B now tries to acquire Lock 1 (but it’s held by Worker A). Worker B blocks.

Both workers are now waiting indefinitely for a lock held by the other. This is a deadlock.

Conceptual Code Illustrating a Deadlock Risk:

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// --- Potentially Deadlocking Code (Conceptual) ---
// Assume lock1 and lock2 are instances of a Mutex (e.g., SpinLockMutex)

// Worker 1 Logic:
function worker1Task(lock1, lock2, sharedResource1, sharedResource2) {
    lock1.lock();
    console.log("Worker 1 acquired lock1");
    // Simulate some work with sharedResource1
    // Now, Worker 1 needs lock2
    console.log("Worker 1 attempting to acquire lock2...");
    lock2.lock(); // Potential block if lock2 is held by Worker 2
    console.log("Worker 1 acquired lock2");
    // Work with sharedResource1 and sharedResource2
    lock2.unlock();
    lock1.unlock();
}

// Worker 2 Logic:
function worker2Task(lock1, lock2, sharedResource1, sharedResource2) {
    lock2.lock();
    console.log("Worker 2 acquired lock2");
    // Simulate some work with sharedResource2
    // Now, Worker 2 needs lock1
    console.log("Worker 2 attempting to acquire lock1...");
    lock1.lock(); // Potential block if lock1 is held by Worker 1
    console.log("Worker 2 acquired lock1");
    // Work with sharedResource1 and sharedResource2
    lock1.unlock();
    lock2.unlock();
}

// If worker1Task and worker2Task run concurrently and interleave
// in a specific way, they can deadlock.

Preventing Deadlocks

The most effective strategy to prevent deadlocks is to break one of the four Coffman conditions necessary for deadlock (Mutual Exclusion, Hold and Wait, No Preemption, Circular Wait). The easiest and most common condition to break is Circular Wait.

Lock Ordering (Primary Strategy): Establish a global, fixed order in which all threads acquire multiple locks. If a thread needs locks A and B, and the global order is (A, then B), it must always acquire A before attempting to acquire B.

Example (Fixing the previous scenario): Assign numerical IDs to locks (e.g., lock1 is ID 1, lock2 is ID 2). Always acquire locks in ascending order of their IDs.

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// --- Deadlock Prevention with Lock Ordering ---

// Worker 1 Logic (Order: lock1 -> lock2)
function worker1TaskOrdered(lock1, lock2, res1, res2) {
    lock1.lock();
    console.log("Worker 1 acquired lock1");
    // Work
    lock2.lock();
    console.log("Worker 1 acquired lock2");
    // Work
    lock2.unlock();
    lock1.unlock();
}

// Worker 2 Logic (Order: lock1 -> lock2, same as Worker 1)
function worker2TaskOrdered(lock1, lock2, res1, res2) {
    // Must acquire in the same global order
    lock1.lock();
    console.log("Worker 2 acquired lock1");
    // Work
    lock2.lock();
    console.log("Worker 2 acquired lock2");
    // Work
    lock2.unlock();
    lock1.unlock();
}

Even if Worker 2 needs lock2 first for its “natural” operation, if it also needs lock1, it must still adhere to the global order. This might mean acquiring lock1, then lock2, even if lock1 isn’t immediately needed for the first part of its task within the critical section.

Minimize Lock Scope: Acquire locks only when absolutely necessary and release them as soon as possible. This reduces the duration locks are held and, consequently, the window of opportunity for deadlocks.
Avoid Nested Locks When Possible: If you can restructure your code to avoid needing multiple locks simultaneously, do so. If not, meticulous lock ordering is paramount.
Lock Timeout (Advanced): When acquiring a lock, specify a timeout (e.g., using the timeout parameter in Atomics.wait()). If the lock isn’t acquired within the timeout, the operation fails, and the thread can then release any locks it currently holds and retry, or report an error. This can prevent indefinite blocking but adds complexity to error handling and recovery logic.

Practical Parallel Computation Pattern Example

Let’s illustrate with a common task: parallel processing of an array. Each worker will process a distinct segment of the array. We’ll sum values in segments and then combine them. The number of workers is often based on navigator.hardwareConcurrency.

Main Thread (main.js):

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// main.js
if (!window.crossOriginIsolated) {
    alert("Cross-origin isolation is not enabled. App cannot run.");
} else {
    const NUM_WORKERS = navigator.hardwareConcurrency || 2;
    const ARRAY_SIZE = 1000000;
    const segmentSize = Math.ceil(ARRAY_SIZE / NUM_WORKERS);

    // +-----------------------------------------------------------------+
    // | Shared Memory Layout:                                           |
    // |-----------------------------------------------------------------|
    // | dataBuffer: Float64Array for the large input data array         |
    // | (ARRAY_SIZE * Float64Array.BYTES_PER_ELEMENT bytes)             |
    // |-----------------------------------------------------------------|
    // | resultsBuffer: Float64Array for partial sums from each worker   |
    // | (NUM_WORKERS * Float64Array.BYTES_PER_ELEMENT bytes)            |
    // |-----------------------------------------------------------------|
    // | statusBuffer: Int32Array for completion status                  |
    // | - Index 0: workersFinishedCount (atomic counter)                |
    // | - Index 1: main thread notification flag (for Atomics.waitAsync)|
    // +-----------------------------------------------------------------+

    const dataByteLength = ARRAY_SIZE * Float64Array.BYTES_PER_ELEMENT;
    const resultsByteLength = NUM_WORKERS * Float64Array.BYTES_PER_ELEMENT;
    const statusByteLength = 2 * Int32Array.BYTES_PER_ELEMENT; 

    const totalByteLength = dataByteLength + resultsByteLength + statusByteLength;
    const sharedBuffer = new SharedArrayBuffer(totalByteLength);

    const dataArray = new Float64Array(sharedBuffer, 0, ARRAY_SIZE);
    const resultsArray = new Float64Array(
        sharedBuffer, dataByteLength, NUM_WORKERS
    );
    const statusView = new Int32Array(
        sharedBuffer, dataByteLength + resultsByteLength
    ); 

    // Initialize input data
    for (let i = 0; i < ARRAY_SIZE; i++) {
        dataArray[i] = Math.random(); // Or some meaningful data
    }
    Atomics.store(statusView, 0, 0); // Init workersFinishedCount to 0
    Atomics.store(statusView, 1, 0); // Init notification flag to 0

    console.log(`Starting ${NUM_WORKERS} workers to process ${ARRAY_SIZE} items.`);

    for (let i = 0; i < NUM_WORKERS; i++) {
        const worker = new Worker('sum_worker.js');
        const startIndex = i * segmentSize;
        const endIndex = Math.min(startIndex + segmentSize, ARRAY_SIZE);

        worker.postMessage({
            workerId: i,
            sharedBuffer: sharedBuffer, 
            dataOffset: 0, 
            dataLength: ARRAY_SIZE,
            resultsOffset: dataByteLength, 
            resultsLength: NUM_WORKERS, // For worker to know its array length
            statusOffset: dataByteLength + resultsByteLength, 
            startIndex: startIndex,
            endIndex: endIndex
        });
    }

    // Wait for all workers to finish using Atomics.waitAsync on the main thread
    async function waitForWorkers() {
        console.log("Main thread waiting for workers to finish...");
        while (Atomics.load(statusView, 0) < NUM_WORKERS) {
            // Wait if statusView[1] (notification flag) is 0
            const asyncWaitResult = Atomics.waitAsync(statusView, 1, 0, 1000);
            const result = await asyncWaitResult.value; 

            if (result === 'timed-out') {
                console.log("Main thread wait timed out, checking again...");
            } else { // 'ok' or 'not-equal' (if value changed before wait)
                Atomics.store(statusView, 1, 0); // Reset notification flag
                console.log("Main thread woken up/notified. Workers completed:",
                            Atomics.load(statusView, 0));
            }
        }

        let totalSum = 0;
        for (let i = 0; i < NUM_WORKERS; i++) {
            totalSum += Atomics.load(resultsArray, i); // Load results safely
        }
        console.log("All workers finished. Total sum:", totalSum);
    }
    waitForWorkers();
}

Worker Thread (sum_worker.js):

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// sum_worker.js
self.onmessage = (event) => {
    const {
        workerId, sharedBuffer,
        dataOffset, dataLength,
        resultsOffset, resultsLength, // resultsLength is NUM_WORKERS
        statusOffset,
        startIndex, endIndex
    } = event.data;

    const dataArray = new Float64Array(sharedBuffer, dataOffset, dataLength);
    const resultsArray = new Float64Array(sharedBuffer, resultsOffset, resultsLength);
    const statusView = new Int32Array(sharedBuffer, statusOffset);
    // statusView[0] is workersFinishedCount
    // statusView[1] is main thread notification flag

    let partialSum = 0;
    for (let i = startIndex; i < endIndex; i++) {
        partialSum += dataArray[i]; // Reading only, Atomics.load not critical
    }

    Atomics.store(resultsArray, workerId, partialSum);
    console.log(`Worker ${workerId} finished. Partial sum: ${partialSum}`);

    const finishedCount = Atomics.add(statusView, 0, 1); // Increment finished count

    // If this worker is the last one to finish (finishedCount was NUM_WORKERS-1)
    if (finishedCount + 1 === resultsArray.length) { // resultsArray.length is NUM_WORKERS
        console.log("Last worker finished, notifying main thread.");
        Atomics.store(statusView, 1, 1); // Set notification flag for main thread
        Atomics.notify(statusView, 1, 1); // Notify main thread (1 waiter)
    }
};

This example demonstrates partitioning data, performing calculations in parallel, and safely aggregating results and status using atomic operations. The main thread uses Atomics.waitAsync() to efficiently wait for worker completion without freezing the UI.

Advanced Topics & Best Practices

Error Handling: Implement onerror handlers in workers and try...catch blocks for robust error management. Consider how a failing worker impacts the overall computation.
Atomics.isLockFree(byteSize): Can be used to check if atomic operations on a given byte size (e.g., 4 for Int32) are implemented lock-free by the hardware (typically true for common integer sizes). See MDN Atomics.isLockFree.
WebAssembly (Wasm): For maximum performance, C/C++/Rust code compiled to WebAssembly can use SABs (Wasm shared memory) and Wasm’s own atomic instructions. This is ideal for porting existing multi-threaded native libraries or for highly complex algorithms.
Performance Considerations:
- Atomic operations are slower than non-atomic ones due to synchronization overhead. Use them only where necessary (i.e., for shared, mutable state).
- False Sharing: A subtle performance issue where unrelated data items, happening to reside on the same CPU cache line, cause performance degradation if different threads frequently update them atomically. This is because modifications to one item invalidate the cache line for other cores, even if they are interested in different items on that same line. Padding data structures to align critical items on different cache lines can mitigate this, but it’s an advanced optimization.

Debugging Multi-Threaded JavaScript

Debugging concurrent applications is notoriously challenging due to non-deterministic behavior and timing-sensitive bugs.

Browser Developer Tools: Modern browsers offer reasonable support for debugging Web Workers. You can set breakpoints, step through code, and inspect variables within workers. Shared memory can be inspected via TypedArray views.
Logging: Use extensive console.log statements, prefixed with worker identifiers and timestamps, to trace execution flow and state changes.
Simplify and Isolate: If you encounter a bug, try to reproduce it in a minimal test case with fewer workers or simpler data.
Focus on Synchronization Points: Pay close attention to lock acquisitions/releases and atomic operations on shared data.
Avoid alert() or confirm() in workers for debugging as they may not behave as expected or could interfere with execution.

Conclusion

SharedArrayBuffer and Atomics bring true shared-memory parallelism to the web platform, unlocking significant performance gains for computationally intensive tasks that can be effectively parallelized. They enable a new class of web applications, from advanced image and video processing to sophisticated scientific computing and immersive gaming experiences.

However, this power demands careful and disciplined programming. Understanding and correctly implementing synchronization primitives like mutexes, and diligently applying strategies to prevent deadlocks (especially lock ordering), are paramount. While complex, mastering these tools allows developers to push the boundaries of what’s possible in the browser, delivering richer and more responsive user experiences.