Better Math in JavaScript?

For web developers tackling data visualization and scientific computing, JavaScript's built-in math functions can sometimes fall short. My search for a more robust solution led me to stdlib-js.

stdlib-js: A Closer Look

stdlib-js impressed me with its features:

• Extensive mathematical functions
• Emphasis on numerical accuracy
• Cross-platform reliability
• Functionality exceeding native Math

Benchmarking the Results

I benchmarked stdlib-js against native JavaScript Math using 100,000 data points. The outcomes were unexpected:

Performance Comparison

Operation	Performance Difference	Precision (Max Diff)	Precision (Avg Diff)	Notes
exp	189.44% slower	0.000e 0	0.000e 0	Largest performance discrepancy
log10	58.94% slower	4.441e-16	1.142e-17	Substantial slowdown
ln/log	33.63% slower	0.000e 0	0.000e 0	Noticeable performance hit
tan	31.87% slower	0.000e 0	0.000e 0	Slower trigonometric operation
acos	29.17% slower	4.441e-16	4.569e-17	Slower inverse trigonometric function
sin	24.54% slower	1.110e-16	3.101e-18	Basic trigonometry affected
cos	23.00% slower	1.110e-16	3.039e-18	Basic trigonometry affected
asin	22.74% slower	2.220e-16	1.219e-17	Inverse trigonometric function impact
atan2	17.02% slower	2.220e-16	6.958e-18	Moderate performance decrease
atan	13.86% slower	1.110e-16	2.675e-18	Better performing inverse trigonometric function
ceil	2.84% slower	0.000e 0	0.000e 0	Minimal impact
abs	1.99% slower	0.000e 0	0.000e 0	Insignificant difference
floor	2.22% faster	0.000e 0	0.000e 0	Slight performance gain
round	1.44% faster	0.000e 0	0.000e 0	Minor performance improvement
sqrt	2.68% faster	0.000e 0	0.000e 0	Improved performance
hypot	1.18% faster	0.000e 0	0.000e 0	Slight performance gain

Positive Aspects

Simple operations showed slight performance enhancements:

• sqrt: 2.68% faster
• round: 1.44% faster
• floor: 2.22% faster

Negative Aspects

Complex operations suffered significant performance penalties:

• exp: 189.44% slower
• log10: 58.94% slower
• Trigonometric functions: 23-33% slower

Precision Considerations

Precision differences were negligible (maximum 4.441e-16). Most operations yielded identical results between stdlib-js and native Math.

When to Choose stdlib-js

• Basic mathematical operations
• Projects demanding guaranteed cross-platform behavior
• Situations where consistent results outweigh raw speed

Future Directions

Compiling stdlib-js to WebAssembly could drastically improve performance, especially for complex operations. Until then, the choice between stdlib-js and native Math requires careful evaluation of project needs.

Conclusion

stdlib-js delivers on its promise of consistency and added functionality. However, performance trade-offs must be considered. For my projects, I'll stick with the native approach, possibly exploring WebAssembly compilation of stdlib-js in the future.

Your Experiences?

Have you used stdlib-js? Share your experiences with JavaScript mathematical computations!

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Resources

• Live demo (link needed)
• Benchmark repository (link needed)
• Asadbek Karimov (link needed)

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