Benchmark: Same operation different sized numbers

HTML Preparation code:

<!--your preparation HTML code goes here-->

Tests:

billion
1000000000 * 99 === 99000000000
million
1000000 * 99 === 99000000
1000
1000 * 99 === 99000
1
1 * 99 === 99

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
billion
million
1000
1

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36

Browser/OS: Chrome 132 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
billion	124914120.0 Ops/sec
million	73106560.0 Ops/sec
1000	173660928.0 Ops/sec
1	173458768.0 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 months ago):

LLMs can make mistakes. Check important info.

The benchmark titled "Same operation different sized numbers" tests the performance of multiplication operations using numbers of varying magnitudes. This benchmark helps evaluate how efficiently a JavaScript engine, like the one in Chrome, handles basic arithmetic operations with different operand sizes.

### Test Cases Overview

The benchmark comprises four individual test cases, each designed to multiply a constant (99) by numbers of different sizes:

1. **Billion**: 
   - Expression: `1000000000 * 99 === 99000000000`
   - Test Name: "billion"
   - This test case uses a large number (1 billion).

2. **Million**:
   - Expression: `1000000 * 99 === 99000000`
   - Test Name: "million"
   - This tests a middle-range number (1 million).

3. **Thousand**:
   - Expression: `1000 * 99 === 99000`
   - Test Name: "1000"
   - The operand here is much smaller (1 thousand).

4. **One**:
   - Expression: `1 * 99 === 99`
   - Test Name: "1"
   - This represents the smallest number in the tests (1).

### Comparison of Options

The benchmarks are contrived to focus on the same mathematical operation but vary only in operand size. Here’s a summary of how these different sizes could affect performance:

- **Smaller Numbers (1, 1000)**:
  - **Pros**: Generally, smaller integers are easier for the CPU to handle, leading to faster execution times.
  - **Cons**: Not as intensive on the processor, potentially making the results less revealing regarding the throughput of the system's arithmetic processing capabilities.

- **Larger Numbers (1 million, 1 billion)**:
  - **Pros**: This can showcase the calculator's ability to handle larger data types. In high-performance scenarios, it might reveal differences in how overflow and memory management are handled.
  - **Cons**: Larger calculations might invoke more complex logic in the engine (like handling larger data types) and impact performance. Some JavaScript implementations (especially in older environments) might struggle due to limitations on number precision as JavaScript uses double-precision 64-bit binary format.

### Latest Benchmark Results Interpretation

From the latest results, we see:

- The performance metrics are presented as "Executions Per Second" (EPS), indicating how many iterations of the test a machine could execute in one second.
  
**Results:**
- **1000**: 173,660,928 EPS
- **1**: 173,458,768 EPS
- **Billion**: 124,914,120 EPS
- **Million**: 73,106,560 EPS

### Observations:
- The tests with smaller numbers (1, 1000) perform significantly better than those with larger numbers (million, billion). This trend aligns with common expectations that smaller arithmetic operations require less computational resource.
- The drop in EPS for larger numbers indicates potential overhead in handling those values correctly due to JavaScript's number handling and memory allocation requirements.

### Alternatives

While this benchmark uses pure arithmetic operations, other approaches could be explored as alternatives:

- **Using Typed Arrays**: In scenarios requiring performance for numerical computations, using Typed Arrays rather than traditional JavaScript numbers can yield better performance due to more efficient memory representation.
  
- **WebAssembly**: For computational-heavy tasks, compiling performance-critical code (like numerical operations) to WebAssembly can provide significant performance improvements compared to pure JavaScript.

- **Different Algorithms/Libraries**: Libraries like Math.js or D3.js could be used for more complex mathematical operations and might offer optimizations over basic operations.

### Conclusion

This benchmark is pivotal in understanding how JavaScript engines efficiently handle multiplication with various number sizes. Software engineers utilizing JavaScript for numerical calculations should be mindful of operand size and consider alternative approaches for performance-critical applications. The results outlined in this benchmark emphasize the capability gaps in handling larger numbers and inform design considerations for similar scenarios.

Related benchmarks:

Same operation different sized numbers (version: 1)

Comparing performance of: billion vs million vs 1000 vs 1

Created: 2 months ago by: Registered User

Jump to the latest result

billion

million

1000

1

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 months ago):