Benchmark: branchless 4x compare vs 4x cached

Script Preparation code:

var tmp, tmpDist = Math.random(), camX = 1, camY = 1;

Tests:

cached
tmp = tmpDist > 0.05; //true == 1 camX = (tmp) * (2) + (!tmp) * (3); camY = (tmp) * (2) + (!tmp) * (3);
inline
camX = (tmpDist > 0.05) * (2) + (tmpDist <= 0.05) * (3); camY = (tmpDist > 0.05) * (2) + (tmpDist <= 0.05) * (3);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
cached
inline

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.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark measures the performance difference between two approaches: branchless comparison and cached comparison in JavaScript. The benchmark is designed to test which approach is faster.

**Test Cases**

There are two individual test cases:

1. **Cached**: This test case uses a cached version of the comparison, where the result of the comparison is stored in a variable (`tmp`) before the comparison is performed. In this case, the condition `tmpDist > 0.05` is evaluated first and its result is then used to calculate `camX` and `camY`. This approach avoids repeated comparisons.
2. **Inline**: This test case uses an inline version of the comparison, where the comparison is performed directly in the calculation without storing the result in a variable.

**Library and Features**

In this benchmark, no specific JavaScript library or feature is used beyond basic arithmetic operations. However, it's worth noting that some modern JavaScript engines, like V8 (used by Chrome), have features like `let` and `const` declarations, which can affect performance.

**Branchless Comparison**

The branchless comparison approach avoids the overhead of conditional statements (if-else). Instead, it uses arithmetic operations to mimic the behavior of a condition. This approach is often faster because it reduces the number of instructions executed.

However, there are some potential downsides:

* The code can be harder to read and understand due to the use of arithmetic operations that don't immediately convey the intent.
* Some older JavaScript engines might not optimize branchless comparisons as effectively.

**Cached Comparison**

The cached comparison approach stores the result of the comparison in a variable (`tmp`) before using it. This allows the calculation to proceed with known values, reducing the number of redundant calculations.

However, there are some potential downsides:

* The initial evaluation of the condition (`tmpDist > 0.05`) can still introduce overhead due to the need to calculate `tmp`.
* If the condition is frequently false (e.g., often less than 0.05), storing the result in a variable may not provide any significant benefits.

**Other Alternatives**

Some alternative approaches could be considered:

* **Memoization**: Instead of caching the result, you can store the results of expensive function calls in a cache and re-use them when needed. This approach would avoid the initial evaluation of the condition but might still incur some overhead due to storage and lookup.
* **SIMD Instructions**: If you're targeting an architecture with SIMD (Single Instruction, Multiple Data) instructions, you could use them to perform multiple comparisons simultaneously. This approach can be particularly effective for large datasets or numerical computations.

Keep in mind that these alternatives are not necessarily better or worse than the branchless and cached approaches; they depend on specific use cases, hardware capabilities, and personal preferences.

**Benchmarking Considerations**

When interpreting benchmark results like this one, consider the following:

* **Hardware**: The performance difference might be affected by the specific hardware being used. This benchmark runs on a Linux desktop with Chrome 102.
* **JavaScript Engine**: Different JavaScript engines have varying degrees of optimization for branchless comparisons and caching.
* **Code Optimization**: Are there other optimizations that could improve the performance of either approach?

Related benchmarks:

branchless 4x compare vs 4x cached (version: 0)

Comparing performance of: cached vs inline

Created: 4 years ago by: Guest

Jump to the latest result

cached

inline

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):