Benchmark: compute vs mutation

Script Preparation code:

let obj = [],
    result = {},
    count = 0

Tests:

compute
let int = setInterval(qwe, 1) function qwe () { if (count > 2000) { clearInterval(int) } count++ obj.push(count) return obj.forEach((item) => { result[item] = `rand${item}` }) }
mutate
let int = setInterval(qwe, 1) function qwe () { if (count > 2000) { clearInterval(int) } count++ obj.push(count) if (count % 2 === 0) { result[item] = `rand${item}` } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
compute
mutate

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's dive into the provided benchmark definition and test cases.

**Benchmark Definition**

The benchmark definition is essentially a script that defines two different approaches to achieve the same goal: computing a result and mutating an object. The scripts are:

1. **Compute**: This approach uses a function `qwe` that runs every second using `setInterval`. Inside the loop, it increments a counter `count`, pushes the current value of the counter to an array `obj`, and then updates an object `result` with a new key-value pair.
2. **Mutate**: This approach is similar to the compute approach but introduces a slight difference: instead of pushing the entire object to the array, it only updates the `result` object when the `count` is even.

**Options Compared**

The two options being compared are:

1. **Compute**: This approach uses a traditional loop with incrementing values and push-to-array operations.
2. **Mutate**: This approach modifies an existing object instead of creating new ones, which might be more efficient in terms of memory allocation.

**Pros and Cons**

* **Compute**:
	+ Pros: Easy to understand and implement, no extra memory allocation required for the `result` object.
	+ Cons: Creates a new array on each iteration, which can lead to increased memory usage over time.
* **Mutate**:
	+ Pros: Reuses an existing object, potentially reducing memory allocation.
	+ Cons: More complex implementation due to the need to update the existing object.

**Library Usage**

There is no explicit library used in this benchmark definition. However, it's worth noting that `forEach` and `push` methods are part of the JavaScript standard library.

**Special JS Feature or Syntax**

None mentioned explicitly, but the use of `setInterval` is a notable feature, as it allows running code at regular intervals without blocking the main thread.

Now, let's analyze the benchmark results:

The latest benchmark result shows that the **mutate** approach outperforms the **compute** approach in terms of executions per second. This might be due to the reuse of an existing object, which reduces memory allocation and potentially leads to better performance.

However, it's essential to note that this benchmark is highly specific to this particular use case and might not generalize well to other scenarios.

If you're interested in exploring alternative approaches, here are a few options:

1. **Caching**: Consider using caching mechanisms to reduce the number of operations required for both approaches.
2. **Parallelization**: If possible, parallelize the computation or mutation process to take advantage of multiple CPU cores.
3. **Just-In-Time (JIT) compilation**: Use JIT compilers like V8 (used by Chrome) to optimize the execution of the benchmarked code.

Keep in mind that each approach will have its own set of trade-offs and optimization opportunities, and it's essential to consider the specific requirements and constraints of your use case.

Related benchmarks:

compute vs mutation (version: 0)

Comparing performance of: compute vs mutate

Created: 7 years ago by: Guest

Jump to the latest result

compute

mutate

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):