docs: create new docs directory

Abduqodiri Qurbonzoda · Abduqodiri Qurbonzoda · commit ca27a413874d · 2024-01-25T20:01:10.000+02:00
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
diff --git a/docs/benchmark-runtime.md b/docs/benchmark-runtime.md
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+# kotlinx.benchmark: A Comprehensive Guide to Benchmark Runtime for Each Target
+
+This document provides an in-depth overview of the kotlinx.benchmark library, focusing on how the benchmark runtime works for each supported target: JVM, JavaScript, and Native. This guide is designed for beginners and intermediates, providing a clear understanding of the underlying libraries used and the benchmark execution process.
+
+## Table of Contents
+
+- [JVM Target](#jvm-target)
+- [JavaScript Target](#javascript-target)
+- [Native Target](#native-target)
+
+## JVM Target
+
+The JVM target in kotlinx.benchmark leverages the Java Microbenchmark Harness (JMH) to run benchmarks. JMH is a widely-used tool for building, running, and analyzing benchmarks written in Java and other JVM languages.
+
+### Benchmark Execution
+
+JMH handles the execution of benchmarks, managing the setup, running, and teardown of tests. It also handles the calculation of results, providing a robust and reliable framework for benchmarking on the JVM.
+
+### Benchmark Configuration
+
+The benchmark configuration is handled through annotations that map directly to JMH annotations. These include `@State`, `@Benchmark`, `@BenchmarkMode`, `@OutputTimeUnit`, `@Warmup`, `@Measurement`, and `@Param`.
+
+### File Operations
+
+File reading and writing operations are performed using standard Java I/O classes, providing a consistent and reliable method for file operations across all JVM platforms.
diff --git a/docs/benchmarking-overview.md b/docs/benchmarking-overview.md
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+# Harnessing Code Performance: The Art and Science of Benchmarking with kotlinx-benchmark
+
+This guide serves as your compass for mastering the art of benchmarking with kotlinx-benchmark. By harnessing the power of benchmarking, you can unlock performance insights in your code, uncover bottlenecks, compare different implementations, detect regressions, and make informed decisions for optimization. 
+
+## Table of Contents
+
+1. [Understanding Benchmarking](#understanding-benchmarking)
+   - [Benchmarking Unveiled: A Beginner's Introduction](#benchmarking-unveiled-a-beginners-introduction)
+   - [Why Benchmarking Deserves Your Attention](#why-benchmarking-deserves-your-attention)
+   - [Benchmarking: A Developer's Torchlight](#benchmarking-a-developers-torchlight)  
+2. [Benchmarking Use Cases](#benchmarking-use-cases)
+3. [Target Code for Benchmarking](#target-code-for-benchmarking)
+   - [What to Benchmark](#what-to-benchmark)
+   - [What Not to Benchmark](#what-not-to-benchmark)
+4. [Maximizing Benchmarking](#maximizing-benchmarking)
+   - [Top Tips for Maximizing Benchmarking](#top-tips-for-maximizing-benchmarking)
+5. [Community and Support](#community-and-support)
+6. [Inquiring Minds: Your Benchmarking Questions Answered](#inquiring-minds-your-benchmarking-questions-answered)
+7. [Further Reading and Resources](#further-reading-and-resources)
+
+## Understanding Benchmarking
+
+### Benchmarking Unveiled: A Beginner's Introduction
+
+Benchmarking is the magnifying glass for your code's performance. It helps you uncover performance bottlenecks, carry out comparative analyses, detect performance regressions, and evaluate different environments. By providing a standard and reliable method of performance measurement, benchmarking ensures code optimization and quality, and improves decision-making within the team and the wider development community.
+
+_kotlinx-benchmark_ is designed for microbenchmarking, providing a lightweight and accurate solution for measuring the performance of Kotlin code.
+
+### Why Benchmarking Deserves Your Attention
+
+The significance of benchmarking in software development is undeniable:
+
+- **Performance Analysis**: Benchmarks provide insights into performance characteristics, allowing you to identify bottlenecks and areas for improvement.
+- **Algorithm Optimization**: By comparing different implementations, you can choose the most efficient solution.
+- **Code Quality**: Benchmarking ensures that your code meets performance requirements and maintains high quality.
+- **Scalability**: Understanding how your code performs at different scales helps you make optimization decisions and trade-offs.
+
+### Benchmarking: A Developer's Torchlight
+
+Benchmarking provides several benefits for software development projects:
+
+1. **Performance Optimization:** By benchmarking different parts of a system, developers can identify performance bottlenecks, areas for improvement, and potential optimizations. This helps in enhancing the overall efficiency and speed of the software.
+
+2. **Comparative Analysis:** Benchmarking allows developers to compare various implementations, libraries, or configurations to make informed decisions. It helps choose the best-performing option or measure the impact of changes made during development.
+
+3. **Regression Detection:** Regular benchmarking enables the detection of performance regressions, i.e., when a change causes a degradation in performance. This helps catch potential issues early in the development process and prevents performance degradation in production.
+
+4. **Hardware and Environment Variations:** Benchmarking helps evaluate the impact of different hardware configurations, system setups, or environments on performance. It enables developers to optimize their software for specific target platforms.
+
+ comparison across systems. This eases sharing and discussing performance results within a team or the larger community.
+
+## Benchmarking Use Cases
+
+Benchmarking serves as a critical tool across various scenarios in software development. Here are a few notable use cases:
+
+- **Performance Tuning:** Developers often employ benchmarking while optimizing algorithms, especially when subtle tweaks could lead to drastic performance changes.
+
+- **Library Selection:** When deciding between third-party libraries offering similar functionalities, benchmarking can help identify the most efficient option.
+
+- **Hardware Evaluation:** Benchmarking can help understand how a piece of software performs across different hardware configurations, aiding in better infrastructure decisions.
+
+- **Continuous Integration (CI) Systems:** Automated benchmarks as part of a CI pipeline help spot performance regressions in the early stages of development.
+
+## Target Code for Benchmarking
+
+### What to Benchmark
+
+Consider benchmarking these:
+
+- **Measurable Microcosms: Isolated Code Segments:** Benchmarking thrives on precision, making small, isolated code segments an excellent area of focus. These miniature microcosms of your codebase are more manageable and provide clearer, more focused insights into your application's performance characteristics.
+
+- **The Powerhouses: Performance-Critical Functions, Methods or Algorithms:** Your application's overall performance often hinges on a select few performance-critical sections of code. These powerhouses - whether they're specific functions, methods, or complex algorithms - have a significant influence on your application's overall performance and thus make for ideal benchmarking candidates.
+
+- **The Chameleons: Code Ripe for Optimization or Refactoring:** Change is the only constant in the world of software development. Parts of your code that are regularly refactored, updated, or optimized hold immense value from a benchmarking perspective. By tracking performance changes as this code evolves, you gain insights into the impact of your optimizations, ensuring that every tweak is a step forward in performance.
+
+### What Not to Benchmark
+
+It's best to avoid benchmarking:
+
+- **The Giants: Complex, Monolithic Code Segments:** Although it might be tempting to analyze large, intricate segments of your codebase, these can often lead to a benchmarking quagmire. Interdependencies within these sections can complicate your results, making it challenging to derive precise, actionable insights. Instead, concentrate your efforts on smaller, isolated parts of your code that can be analyzed in detail.
+
+- **The Bedrocks: Stagnant, Inflexible Code:** Code segments that are infrequently altered or have reached their final form may not provide much value from benchmarking. While it's important to understand their performance characteristics, it's the code that you actively optimize or refactor that can truly benefit from the continuous feedback loop that benchmarking provides.
+
+- **The Simples: Trivial or Overly Simplistic Code Segments:** While every line of code contributes to the overall performance, directing your benchmarking efforts towards overly simple or negligible impact parts of your code may not yield much fruit. Concentrate on areas that have a more pronounced impact on your application's performance to ensure your efforts are well spent.
+
+- **The Wild Cards: Non-Reproducible or Unpredictable Behavior Code:** Consistency is key in benchmarking, so code that's influenced by external, unpredictable factors, such as I/O operations, network conditions, or random data generation, should generally be avoided. The resulting inconsistent benchmark results may obstruct your path to precise insights, hindering your optimization efforts.
+
+## Maximizing Benchmarking
+
+### Top Tips for Maximizing Benchmarking
+
+To obtain accurate and insightful benchmark results, keep in mind these essential tips:
+
+1. **Focus on Vital Code Segments**: Benchmark small, isolated code segments that are critical to performance or likely to be optimized.
+
+2. **Employ Robust Tools**: Employ powerful benchmarking tools like kotlinx-benchmark that handle potential pitfalls and provide reliable measurement solutions.
+
+3. **Context is Crucial**: Supplement your benchmarking with performance evaluations on real applications to gain a holistic understanding of performance traits.
+
+4. **Control Your Environment**: Minimize external factors by running benchmarks in a controlled environment, reducing variations in results.
+
+5. **Warm-Up the Code**: Before benchmarking, execute your code multiple times. This allows the JVM to perform optimizations, leading to more accurate results.
+
+6. **Interpreting Results**: Understand that lower values are better in a benchmarking context. Also, consider the statistical variance and look for meaningful differences, not just any difference.
+
+## Community and Support
+
+For further assistance and learning, consider engaging with these communities:
+
+- **Stack Overflow:** Use the `kotlinx-benchmark` tag to find or ask questions related to this tool.
+
+- **Kotlinlang Slack:** The `#benchmarks` channels is the perfect place to discuss topics related to benchmarking.
+
+- **Github Discussions:** The kotlinx-benchmark Github repository is another place to discuss and ask questions about this library.
+
+## Inquiring Minds: Your Benchmarking Questions Answered
+
+Benchmarking may raise a myriad of questions, especially when you're first getting started. To help you navigate through these complexities, we've compiled answers to some commonly asked questions.
+
+**1. The Warm-Up Riddle: Why is it Needed Before Benchmarking?**
+
+The Java Virtual Machine (JVM) features sophisticated optimization techniques, such as Just-In-Time (JIT) compilation, which becomes more effective as your code runs. Warming up allows these optimizations to take place, providing a more accurate representation of how your code performs under standard operating conditions
+
+**2. Decoding Benchmark Results: How Should I Interpret Them?**
+
+In benchmarking, lower values represent better performance. But don't get too fixated on minuscule differences. Remember to take into account statistical variances and concentrate on significant performance disparities. It's the impactful insights, not every minor fluctuation, that matter most.
+
+**3. Multi-threaded Conundrum: Can I Benchmark Multi-threaded Code with kotlinx-benchmark?**
+
+While kotlinx-benchmark is geared towards microbenchmarking — typically examining single-threaded performance — it's possible to benchmark multi-threaded code. However, keep in mind that such benchmarking can introduce additional complexities due to thread synchronization, contention, and other concurrency challenges. Always ensure you understand these intricacies before proceeding.
+
+## Further Reading and Resources
+
+If you'd like to dig deeper into the world of benchmarking, here are some resources to help you on your journey:
+
+- [Mastering High Performance with Kotlin](https://www.amazon.com/Mastering-High-Performance-Kotlin-difficulties/dp/178899664X)
diff --git a/docs/compatibility.md b/docs/compatibility.md
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+# Compatibility Guide
+
+This guide provides you with information on the compatibility of different versions of `kotlinx-benchmark` with both Kotlin and Gradle. To use `kotlinx-benchmark` effectively, ensure that you have the minimum required versions of Kotlin and Gradle installed.
+
+| `kotlinx-benchmark` Version | Minimum Required Kotlin Version | Minimum Required Gradle Version |
+| :-------------------------: | :-----------------------------: | :-----------------------------: |
+|            0.4.8           |             1.8.2              |           8.0 or newer         |
+|            0.4.7           |             1.8.0              |           8.0 or newer         |
+|            0.4.6           |             1.7.20             |           8.0 or newer         |
+|            0.4.5           |             1.7.0              |           7.0 or newer         |
+|            0.4.4           |             1.7.0              |           7.0 or newer         |
+|            0.4.3           |             1.6.20             |           7.0 or newer         |
+|            0.4.2           |             1.6.0              |           7.0 or newer         |
+|            0.4.1           |             1.6.0              |           6.8 or newer         |
+|            0.4.0           |             1.5.30             |           6.8 or newer         |
+|            0.3.1           |             1.4.30             |           6.8 or newer         |
+|            0.3.0           |             1.4.30             |           6.8 or newer         |
+
+*Note: "Minimum Required" implies that any higher version than the one mentioned will also be compatible.*
+
+For more details about the changes, improvements, and updates in each `kotlinx-benchmark` version, please refer to the [RELEASE NOTES](https://github.com/Kotlin/kotlinx-benchmark/releases) and [CHANGELOG](#).
diff --git a/docs/interpreting-results.md b/docs/interpreting-results.md
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+# Interpreting and Analyzing Kotlinx-Benchmark Results
+
+When you use the kotlinx-benchmark library to profile your Kotlin code, it provides a detailed output that can help you identify bottlenecks, inefficiencies, and performance variations in your application. Here is a comprehensive guide on how to interpret and analyze these results.
+
+## Understanding the Output
+
+A typical kotlinx-benchmark result may look something like this:
+
+```
+Benchmark             Mode   Cnt      Score       Error  Units
+ListBenchmark.first   thrpt   20  74512.866 ±  3415.994  ops/s
+ListBenchmark.first   thrpt   20   7685.378 ±   359.982  ops/s
+ListBenchmark.first   thrpt   20    619.714 ±    31.470  ops/s
+```
+
+Let's break down what each column represents:
+
+1. **Benchmark:** This is the name of the benchmark test.
+2. **Mode:** This is the benchmark mode. It may be "avgt" (average time), "ss" (single shot time), "thrpt" (throughput), or "sample" (sampling time).
+3. **Cnt:** This is the number of measurements taken for the benchmark. More measurements lead to more reliable results.
+4. **Score:** This is the primary result of the benchmark. For "avgt", "ss" and "sample" modes, lower scores are better, as they represent time taken per operation. For "thrpt", higher scores are better, as they represent operations per unit of time.
+5. **Error:** This is the error rate for the Score. It helps you understand the statistical dispersion in the data. A small error rate means the Score is more reliable.
+6. **Units:** These indicate the units for Score and Error, like operations per second (ops/s) or time per operation (us/op, ms/op, etc.)
+
+## Analyzing the Results
+
+Here are some general steps to analyze your benchmark results:
+
+1. **Compare Scores:** The primary factor to consider is the Score. Remember to interpret it in the context of the benchmark mode - for throughput, higher is better, and for time-based modes, lower is better.
+
+2. **Consider Error:** The Error rate gives you an idea of the reliability of your Score. If the Error is high, the benchmark might need to be run more times to get a reliable Score.
+
+3. **Review Parameters:** Consider the impact of different parameters (like 'size' in the example) on your benchmark. They can give you insights into how your code performs under different conditions.
+
+4. **Factor in Units:** Be aware of the units in which your results are measured. Time can be measured in nanoseconds, microseconds, milliseconds, or seconds, and throughput in operations per second.
+
+5. **Compare Benchmarks:** If you have run multiple benchmarks, compare the results. This can help identify which parts of your code are slower or less efficient than others.
+
+## Common Pitfalls
+
+While analyzing benchmark results, watch out for these common pitfalls:
+
+1. **Variance:** If you're seeing a high amount of variance (a high Error rate), consider running the benchmark more times.
+
+2. **JVM Warmup:** Java's HotSpot VM optimizes the code as it runs, which can cause the first few runs to be significantly slower. Make sure you allow for adequate JVM warmup time to get accurate benchmark results.
+
+3. **Micro-benchmarks:** Be cautious when drawing conclusions from micro-benchmarks (benchmarks of very small pieces of code). They can be useful for testing small, isolated pieces of code, but real-world performance often depends on a wide array of factors that aren't captured in micro-benchmarks.
+
+4. **Dead Code Elimination:** The JVM is very good at optimizing your code, and sometimes it can optimize your benchmark right out of existence! Make sure your benchmarks do real work and that their results are used somehow (often by returning them from the benchmark method), or else the JVM might optimize them away.
+
+5. **Measurement error:** Ensure that you are not running any heavy processes in the background that could distort your benchmark results.
