var x = Math.random() * 1000
var y
y = x / 100
y = x * 0.01
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
division | |
multiplication |
Test name | Executions per second |
---|---|
division | 2985216.5 Ops/sec |
multiplication | 3019666.0 Ops/sec |
The benchmark described in the provided JSON compares the performance of two mathematical operations in JavaScript: division (y = x / 100
) and multiplication (y = x * 0.01
). Both operations aim to compute the same result, but they utilize different mathematical approaches to do so.
y = x / 100
)y = x * 0.01
)The benchmark results indicate performance through "Executions Per Second," which reflects how many times each operation can be executed in a second on a given device configuration. In this case, both operations were run on a desktop browser (Chrome 132) with the following results:
Pros:
Cons:
Pros:
Cons:
Accuracy: Both operations should produce accurate results when performed with floating-point numbers in JavaScript. However, floating-point arithmetic can lead to precision issues based on how numbers are represented in binary format, particularly when comparing very small numbers or numbers with many decimal places.
Hardware and Environment Influence: The speeds may vary depending on the system's CPU, architecture, and how well the JavaScript engine optimizes for each type of operation. Results can vary across different browsers and devices, emphasizing the importance of testing in the specific environment where the application will run.
In addition to division and multiplication, developers may consider the use of other operations or techniques depending on the context. Alternatives could include:
x >> 1
) to divide by powers of two.math.js
or decimal.js
could be used for more complex mathematical operations, though this can introduce additional overhead compared to pure JavaScript operations.In sum, while both division and multiplication achieve the same result in this benchmark, multiplication emerges as the more efficient approach. Developers should weigh performance against code clarity and consider the specific constraints of their use case when choosing mathematical operations in their code.