# SUMMARY Casey discusses the inadequacies of a performance diagram ranking programming languages, emphasizing the misleading nature of micro-benchmarks and their implications on language choice. # IDEAS: - Benchmarks can be misleading when they focus on artificial or unrepresentative tasks like loop iterations. - The comparison of programming languages should involve real workloads rather than abstract micro-benchmarks. - Loop performance is often determined more by workload than the underlying iteration mechanics. - Compilers can optimize code differently, affecting benchmark results significantly depending on implementation. - There’s a distinction between benchmarks designed for comparison and those for demonstration or exploration. - Proper benchmarking requires knowing the context and semantics of the language in use. - Integer division is significantly slower than addition, which can skew benchmarking results unfairly. - Successful benchmarking needs to account for compiler optimizations rather than thwart them. - Effective benchmarks need representative workloads, ideally reflecting actual programming tasks and scenarios. - A focus on unordered or transient examples can obscure broader programming experiences and results. - Real-world conditions—like startup time and garbage collection—can heavily impact benchmark outcomes in higher-level languages. - Micro-benchmarks can lead developers to draw erroneous conclusions about language performance. - The semantics of numerical operations can differ across languages, affecting their performance in benchmarks. - Visualization choices in benchmark presentations can mislead developers about performance relative to convenience. - Potentially misleading benchmarks can affect language adoption decisions in software development communities. - An ideal benchmark should demonstrate actual usage scenarios rather than abstract or simplified examples. - Average users benefit from compiler optimizations, not from contrived tests thwarting these optimizations. - The choice of benchmarks affects perceptions of language efficiency, potentially propagating misinformed decisions. - Diversity in workloads is essential to obtain a well-rounded understanding of language performance. - Intentionally designing benchmarks to avoid optimizations shows a misunderstanding of compiler capabilities. - An inappropriate benchmark can undermine an entire programming language’s reputation. # INSIGHTS: - Misleading benchmarks reduce the reliability of language performance comparisons across diverse programming tasks. - Language benchmarks should be reflective of real-world scenarios instead of contrived micro-benchmark tests. - Integer operations' computational costs greatly affect language performance, impacting benchmarking validity. - Proper understanding of compiler behavior enhances effective use of language benchmarks for developers. - Good benchmarks prioritize simplicity and contextual relevance rather than intricate and misleading complexity. - Effective benchmarking improves comprehension of coding language efficiencies and how they operate practically. - Underestimating the effects of optimization on coding tasks leads to erroneous conclusions about language speed. - Benchmarks revealing CPU execution details provide clearer insights into language performance rather than superficial measures. - Visualization can dramatically influence the perceived performances of programming languages; clarity is key. - The value of programming languages should not rely solely on specific micro-benchmark outcomes. # QUOTES: - "These bouncy back and forth diagrams are not good to begin with." - "This is just not real; there’s no memory being created." - "You risk testing how efficiently it generated those things." - "You can’t compare these synthesized examples with real workloads." - "If you’re building benchmarks, you have to know what you are building them for." - "Gains in performance must be measured in representative workloads." - "Compiler optimizations should not be thwarted in the practice of benchmarking." - "You have to actually develop a whole HTTPS server… for benchmarking." - "An inappropriate benchmark can undermine an entire programming language’s reputation." - "What you want is very much… representative of real work." - "Average programmers want compilers to automatically optimize their code." - "The distinctions between benchmarks can mislead developers into making bad decisions." - "You should not create contrived examples as comparisons like this." - "There’s a level of unpredictability when dealing with unoptimized workloads." - "If it was slightly less efficient, you’d never know in practice." - "Real workloads vary widely; benchmarks should reflect that diversity." # HABITS: - Ensure regular code reviews to identify potential inefficiencies or optimizations. - Engage in continuous learning about compiler behavior and optimizations for effective programming. - Always validate benchmark results against practical application scenarios before drawing conclusions. - Consciously design benchmarks with representative workloads for accurate performance metrics. - Regularly revisit and revise outdated benchmarks to maintain their relevance and accuracy. - Prioritize code simplicity and clarity to ensure optimal compiler optimizations. - Analyze performance as part of ongoing development rather than isolated benchmarks. - Collaborate with peers to challenge and enhance benchmarking techniques and methodologies. - Document and share experiences of benchmarks to help establish better practices in communities. - Experiment with various languages in real scenarios to observe performance differences firsthand. # FACTS: - Integer division is significantly slower than addition, impacting performance in benchmarks. - Many benchmarks rely on artificial constructs, failing to represent real coding scenarios. - Compiler optimizations can vary greatly between languages and significantly alter benchmarking outcomes. - The misinterpretation of performance benchmarks is common among developers, leading to poor language choices. - Performance measurements can often overlook significant overheads introduced by program initialization processes. - Benchmark results can be skewed based on the semantics and numerical operations performed. - Proper language benchmarks should include a diverse set of tasks representative of real-world applications. - Compiled languages often outperform interpreted languages under specific workload conditions in benchmarks. - JavaScript has seen performance improvements over time regarding internal numerical representations. - People can form strong opinions about a language's efficiency based on potentially inaccurate benchmarks. - Vectorization plays a critical role in optimizing loop performance and should be benchmarked for effectiveness. - Visual presentation choices can greatly impact how benchmark results are perceived and interpreted. - Misleading benchmarks can inadvertently shape programming trends and impact community perceptions. - Real-world programming scenarios often differ vastly from micro-benchmark test environments. - Proper understanding of compiler and language semantics is essential for creating effective benchmarks. - A thorough examination of how benchmarks are devised can reveal their actual utility and accuracy. # REFERENCES: - Performance diagram comparing languages (not specified). - Examples of benchmarks from Gamers Nexus and Hardware Unboxed. - Discussion of integer modulus and its effects on computational efficiency. - Reference to various programming languages, including Zig, Rust, C, Go, and Lua. - Mention of AVX and SIMD instruction sets as related to benchmarking performance. - General understanding of compiler optimizations and their impact on performance. - Concept of benchmarking for demonstration versus comparison as discussed. # ONE-SENTENCE TAKEAWAY Misleading benchmarks obscure the true performance of programming languages, highlighting the need for real-world, representative comparisons. # RECOMMENDATIONS: - Design benchmarks using real-world workloads for accurate and practical language comparisons. - Regularly review and update benchmarks to ensure they reflect current programming practices. - Prioritize simplicity in coding to allow compilers to optimize effectively during execution. - Educate developers on the importance of understanding compiler behavior when benchmarking. - Use diverse performance tests covering various scenarios to obtain a comprehensive analysis of languages. - Avoid creating benchmarks that hinder compiler optimizations to ensure valid results. - Collaborate with the programming community to share insights and best practices regarding benchmarking. - Analyze programming language performance in the context of specific applications or workloads. - Utilize both micro-benchmarks and full application benchmarks to gain a balanced perspective on performance. - Focus language selections on real use cases instead of isolated micro-benchmark results.

### Key Takeaways from the Discussion on Language Performance Benchmarks 1. **Benchmarking Methodology**: - Performance benchmarks can vary widely based on methodology. - Micro benchmarks (small tests of specific functions) are often misleading, especially when comparing programming languages. 2. **Performance Results**: - Languages like Zig, Rust, and C ranked highest in performance, while interpreted languages (e.g., Python, Ruby) ranked lower. - Performance measurements often include the overhead of setup, initialization, and cleanup tasks, which may distract from the actual performance of the loop. 3. **Limitations of Benchmarking Loops**: - A simple loop test often does not reflect real-world usage. - Performance may not indicate the overall efficiency of a language; how well code runs in practice with actual data and workloads is more meaningful. - Optimizations done by compilers (e.g., loop unrolling, vectorization) can significantly alter performance outcomes, depending on how loops are constructed. 4. **Complexities of Language Execution**: - Differences in compilation can result in drastically different performance outcomes even when the same algorithm is used. - Features like garbage collection, type handling (integers vs. doubles), and error checking can complicate performance tests. - The use of specific optimization flags (e.g., `-march=native`) in compilers can lead to performance gains by tailoring the output to the specific architecture. 5. **Real Workloads vs. Toy Examples**: - Real-world applications involve more than just loops. They often include data fetching, processing, and I/O operations, which can differ massively in performance. - Context matters—testing real workloads (server requests, database queries) provides more accurate insights into a language's practical performance. 6. **Compiler Behavior**: - Compilers may optimize code in unexpected ways, altering performance characteristics based on the structure of the code provided. - Functions that are predictable (i.e., no unknown variables) can often be optimized away by compilers, leading to misleading benchmarks. 7. **Recommendations for Future Benchmarking**: - Benchmarks should involve realistic workloads that accurately represent the kinds of tasks the languages will perform in production. - Avoiding contrived examples will yield more valuable insights into the languages' capabilities. - Focus should be placed on exploratory benchmarks for understanding specific performance characteristics rather than broad comparisons. 8. **Concerns about Influencing Decisions**: - Poor benchmarking practices can lead to incorrect decisions regarding language choice, potentially steering developers away from more suitable technologies based on misleading data. - It's essential to critically evaluate benchmark reports and approaches to ensure they provide valid and applicable information. 9. **Understanding Compiler Outputs**: - Disassembling the generated code can provide insights into how loops and other constructs are processed by the compiler. - The actual assembly instructions can reveal the efficiency of different programming constructs and operations within the code base. ### Conclusion In summary, while performance is a crucial aspect of programming languages, the manner in which it's benchmarked heavily influences the results. A thoughtful approach that considers real-world usage scenarios, along with understanding compiler behavior and execution contexts, is necessary for making informed decisions about language performance.