Benchmark: cached vs uncached math.pi (const)

Script Preparation code:

const pi = Math.PI, res = 0;

Tests:

cached
res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05; res = pi * 0.05;
uncached
res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05; res = Math.PI * 0.05;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
cached
uncached

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.

The provided benchmark compares the performance of two approaches to calculate the value of `Math.PI` multiplied by a constant value (0.05) in JavaScript.

**Approach 1: Caching**

In this approach, the value of `Math.PI` is assigned to a variable `pi` before any calculations are performed. The calculation then uses this cached value.

**Approach 2: Non-Caching**

In this approach, the value of `Math.PI` is not cached and is used directly in each iteration of the loop.

**Pros of Caching:**

1. Reduced overhead: By caching the value of `Math.PI`, we avoid recalculating it on every iteration.
2. Improved performance: This approach can lead to significant performance improvements, especially for large datasets or high-traffic applications.

**Cons of Caching:**

1. Memory usage: Storing the value of `Math.PI` in memory can increase the overall memory usage of the application.
2. Initial overhead: Assigning the initial value of `Math.PI` to a variable may incur additional overhead due to string interpolation or other operations.

**Pros of Non-Caching:**

1. Low memory usage: This approach does not require storing the value of `Math.PI` in memory, making it suitable for applications with limited resources.
2. No initial overhead: Since `Math.PI` is used directly on every iteration, there is no additional overhead due to string interpolation or other operations.

**Cons of Non-Caching:**

1. Reduced performance: Recalculating the value of `Math.PI` on every iteration can lead to significant performance overhead, especially for large datasets or high-traffic applications.
2. Increased complexity: This approach may be more complex and harder to optimize compared to caching.

The benchmark result shows that the "cached" approach outperforms the "uncached" approach by a significant margin. This suggests that caching is an effective way to improve performance in this specific scenario.

**Other alternatives:**

1. **Using a specialized library:** The `Math.PI` value could be replaced with a more accurate or efficient implementation from a specialized library, such as the [js-helpers](https://github.com/addaleax/js-helpers) or [decimal.js](https://github.com/MikeMcl/decimal.js) libraries.
2. **Using a Just-In-Time (JIT) compiler:** The `Math.PI` value could be used with a JIT compiler, such as V8 or SpiderMonkey, to improve performance by compiling the code into native machine code.
3. **Using parallel processing:** The calculation could be split across multiple CPU cores using parallel processing techniques, such as Web Workers or async operations.

However, these alternatives may introduce additional complexity and overhead, and their effectiveness depends on specific use cases and requirements.

Related benchmarks:

cached vs uncached math.pi (const) (version: 0)

Comparing performance of: cached vs uncached

Created: 4 years ago by: Guest

Jump to the latest result

cached

uncached

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):