Benchmark: Queue comparison4

Script Preparation code:

function QueueItem(value) {
  this.value = value;
  this.next  = void 0;
}

function Queue() {
  this.first = void 0;
  this.last  = void 0;
}
Queue.prototype.enqueue = function (value) {
  var wasEmpty = !this.first;
  var item = new QueueItem(value);
  if (wasEmpty) {
    this.first = item;
  } else {
    this.last.next = item;
  }
  this.last = item;
  return wasEmpty;
};
Queue.prototype.dequeue = function () {
  var first = this.first;
  if (first) {
    this.first = first.next;
  }
  if (first === this.last) {
    this.last = void 0;
  }
  return first.value;  
};
Queue.prototype.isEmpty = function () {
  return !this.first;
};

var a = [];
var q = new Queue();


class Queue2 {
  constructor() {
    this.list = new Set();
  }

  enqueue(value) {
    this.list.add({value});
  }

  dequeue() {
    for (let item of this.list) {
      this.list.delete(item);
      return item.value;
    }
  }

  isEmpty() {
    return this.list.size === 0;
  }
}

var q2 = new Queue2();

Tests:

array
for (var i = 0; i < 10000; i++) { a.push(i); } while (a.length) { var x = a.shift(); }
queue
for (var i = 0; i < 10000; i++) { q.enqueue(i); } while (!q.isEmpty()) { var x = q.dequeue(); }
array non-destructive
for (var i = 0; i < 10000; i++) { a.push(i); } for (var i = 0, len = a.length; i < len; i++) { var x = a[i]; } a = [];
queue2
for (var i = 0; i < 10000; i++) { q2.enqueue(i); } while (!q2.isEmpty()) { var x = q2.dequeue(); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array
queue
array non-destructive
queue2

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and options compared.

**Benchmark Definition:**

The benchmarks are designed to compare the performance of three different data structures: arrays, queues, and non-destructive arrays (Queue2). Each test case measures the execution time for a specific operation:

1. `array`: Pushing elements onto an array and then shifting them off using `shift()`.
2. `queue`: Enqueuing elements onto a queue and then dequeuing them.
3. `array non-destructive` (Queue2): Creating a new set, adding elements to it, and then iterating over its contents without modifying the original data.

**Options Compared:**

The benchmarks compare the performance of three different approaches:

1. **Arrays**: Using traditional array methods for pushing and shifting elements.
2. **Queues**: Implementing a basic queue using a linked list (Queue).
3. **Non-destructive arrays (Queue2)**: Using a set to store unique values and iterating over its contents without modifying the original data.

**Pros and Cons of Each Approach:**

1. **Arrays**:
	* Pros: Simple, intuitive, and widely supported.
	* Cons: May involve multiple array accesses, leading to caching issues and slower performance for large datasets.
2. **Queues** (Linked List):
	* Pros: Efficient use of memory, allowing for O(1) enqueue and dequeue operations.
	* Cons: More complex implementation, which may introduce additional overhead.
3. **Non-destructive arrays (Queue2)**:
	* Pros: Avoids modifying the original data, reducing cache thrashing and potential performance issues.
	* Cons: Requires creating a separate data structure (set), which may incur additional memory allocation and deallocation costs.

**Other Considerations:**

1. **Caching:** The benchmarks may be influenced by caching behavior, as arrays often rely on caching to improve performance.
2. **Memory Allocation and Deallocation:** Frequent creation and deletion of objects or sets can impact performance due to the overhead of memory management.
3. **Hardware-Specific Optimizations:** Some hardware architectures (e.g., SIMD) might provide optimizations for specific operations, which could affect benchmark results.

**Alternative Approaches:**

1. **Using `map()` or `forEach()` instead of loops:** These methods can simplify code and reduce cache thrashing by avoiding explicit iteration.
2. **Implementing a more efficient queue data structure:** Using techniques like lazy evaluation, skip lists, or balanced trees could improve performance for large datasets.
3. **Utilizing parallel processing:** Dividing the workload among multiple threads or processes can take advantage of multi-core processors and accelerate execution times.

When interpreting benchmark results, consider the specific use case, dataset size, and hardware architecture to determine which approach is most suitable for your particular requirements.

Related benchmarks:

Queue comparison4 (version: 0)

Comparing performance of: array vs queue vs array non-destructive vs queue2

Created: 6 years ago by: Guest

Jump to the latest result

array

queue

array non-destructive

queue2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):