The Performance Battle: A Window into EXCIS system in Softanza

Softanza EXCIS, built on the stzExtCode class, makes it effortless to embed and execute code from multiple languages within a Haro program.

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Important Note on Performance Stats: The results, measured in milliseconds, are relative to a specific computer environment and influenced by runtime warmups (e.g., Julia’s JIT compilation or Python’s interpreter startup). These times should be interpreted on a relative scale, comparing languages within this context rather than as absolute measures, as performance can vary across systems and runs.

Round 1: Fibonacci Frenzy

Task: Compute the 94-digit 450th Fibonacci number.

Results: - C, Python, SWI-Prolog: 0 ms - NodeJS: 0.07 ms - Haro: 1 ms - Julia: 11 ms - R: 20 ms

Insights: Python and SWI-Prolog excelled with big integer support, while C matched their speed but faltered on overflow. NodeJS nearly tied C, showcasing its computational versatility. Haro performed solidly, but Julia and R lagged slightly due to warmup and non-numeric focus.

Round 2: Sorting Showdown

Task: Sort a 1,000,000-element array.

Results: - SWI-Prolog: 110 ms - NodeJS: 134.67 ms - C: 197 ms - R: 690 ms - Julia: 944 ms - Python: 3,896.04 ms - Haro: 29,923 ms

Insights: SWI-Prolog’s optimized sort/2 stunned, outpacing manual quicksorts. NodeJS and C delivered raw power, while R and Julia leveraged built-in sorts. Python and Haro struggled without library optimizations.

Round 3: Matrix Mania

Task: Multiply two 250x250 matrices.

Results: - R: 10 ms - C: 61 ms - NodeJS: 80.76 ms - Julia: 255 ms - Python: 2,512.93 ms - SWI-Prolog: 4,250 ms - Haro: 7,805 ms

Insights: R dominated with BLAS-powered matrix operations, C and NodeJS showed versatility, and Julia held strong. Python, SWI-Prolog, and Haro lagged in pure implementations.

Total Times (Relative)

• NodeJS: 215.5 ms
• C: 258 ms
• R: 720 ms
• Julia: 1,210 ms
• SWI-Prolog: 4,360 ms
• Python: 6,408.97 ms
• Haro: 37,729 ms

Winner: NodeJS, edging out C, but the real story is how each language excels in its niche.

Beyond Speed: A Computational Thinking Revolution

The performance battle highlights where each language shines—C for raw speed, R for matrices, SWI-Prolog for sorting efficiency—but EXCIS’s true power lies in enabling a domain of domains-of-expertise. Instead of forcing one language to handle every task, EXCIS lets you design solutions by combining the best tools:

• System Programming: Use C for low-level efficiency.
• Data Analysis: Let R crunch numbers and visualize results.
• Machine Learning: Tap Python’s rich ecosystem (e.g., Pandas, scikit-learn).
• Scientific Computing: Leverage Julia’s numerical precision.
• Asynchronous APIs: Build with NodeJS’s event-driven model.
• Logic Reasoning: Solve complex problems with Prolog’s rule-based approach.
• Prototyping: Write clear, readable code with Haro.

This polyglot approach fosters computational thinking, allowing developers to focus on solution design rather than language limitations. By orchestrating these languages, Haro becomes a polyglot powerhouse, outperforming monolingual environments in tackling multifaceted challenges like AI-driven analytics, real-time systems, or logical constraint solving.

Cite

Mansour Ayouni. "The Performance Battle: A Window into EXCIS system in Softanza". Softanza narrations, 2025. https://mayouni.github.io/stzsite/en/narrations/stzexcis-performance-battle.html

This article holds no code. The code of the articles is Haro code. Where an article was written before the language took its present name, the name was changed and nothing else. Genre, area and series read from the file's name, the year from its first commit: derived, until the article carries its own header. The file on GitHub(opens in a new tab)