--- title: Performance analysis in Linux | daily.dev description: Dynamic profiling tools help identify non-obvious performance bottlenecks in Linux applications. Using a concrete C example on an Intel Core i7-5500U, the post... canonical: https://daily.dev/posts/performance-analysis-in-linux-mwalg0mn2 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Performance analysis in Linux | daily.dev og:description: Dynamic profiling tools help identify non-obvious performance bottlenecks in Linux applications. Using a concrete C example on an Intel Core i7-5500U, the post... og:url: https://daily.dev/posts/performance-analysis-in-linux-mwalg0mn2 og:image: https://api.daily.dev/og/posts/MwALG0mn2.png og:image:alt: Performance analysis in Linux og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Performance analysis in Linux **[Collabora Blog](https://daily.dev/sources/collabora)** · 13 min read · 0 upvotes · 0 comments ## Summary Dynamic profiling tools help identify non-obvious performance bottlenecks in Linux applications. Using a concrete C example on an Intel Core i7-5500U, the post demonstrates how branch mispredictions in an unsorted vector loop cause 16% branch miss rate and 29.97s execution time. Pre-sorting the vector drops misses to 0.02% and cuts runtime to 13.20s — a 56% improvement. The Linux perf tool (perf record, perf annotate, perf stat) is used step-by-step to identify the hot code region and confirm branch mispredictions as the root cause, showing how hardware-level CPU pipeline behavior can dramatically affect application performance in ways that are invisible from algorithm analysis alone. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I use Linux perf to find branch mispredictions in my program? Run `perf stat ./your-program` to get a summary including branch miss percentage — values highlighted in red indicate unexpectedly high counts. Then use `perf record ./your-program` followed by `perf annotate` to pinpoint hot instruction regions in the source. Compile with `-g3` to interleave C source with assembly output. A branch miss rate above ~16% is a strong signal of pipeline stall overhead. _Developers tracking down CPU pipeline issues like this share findings and tooling tips on daily.dev._ ### Why does sorting an array before iterating over it make my code faster? Sorting enables the CPU branch predictor to learn the pattern of conditional branches. With random data, the predictor guesses wrong roughly half the time, causing pipeline flushes on each miss. With sorted data, the branch outcome is predictable — one contiguous region skips the if-leg, another always enters it — dropping the miss rate from 16% to 0.02% and more than doubling instructions-per-cycle throughput. _Engineers optimizing tight loops find discussions on branch prediction trade-offs on daily.dev._ ## Similar posts on daily.dev - [How Do I Profile eBPF Code?](https://daily.dev/posts/how-do-i-profile-ebpf-code--bcxzrgh49) · Hacker News · 0 upvotes · 0 comments - [One Line x86 Change To GCC Compiler Nets \+12% Benchmark Win For Modern Intel/AMD CPUs](https://daily.dev/posts/one-line-x86-change-to-gcc-compiler-nets-12-benchmark-win-for-modern-intel-amd-cpus-hm9xklk3r) · Phoronix · 1 upvotes · 0 comments - [Pitfalls of Benchmarking on Modern Systems](https://daily.dev/posts/pitfalls-of-benchmarking-on-modern-systems-tvk8mrdyb) · Awesome Java Newsletter · 0 upvotes · 0 comments - [One Giant Leap: 95% Less Sampling Cost](https://daily.dev/posts/one-giant-leap-95-less-sampling-cost-knpvkaex9) · Inside Java · 0 upvotes · 0 comments - [Someone changed one line in the GCC compiler and scored a 12% improvement on modern Intel and AMD chips](https://daily.dev/posts/someone-changed-one-line-in-the-gcc-compiler-and-scored-a-12-improvement-on-modern-intel-and-amd-ch-d1otkz2tx) · XDA Developers · 0 upvotes · 0 comments --- Tags: [#linux](https://daily.dev/tags/linux) [View this post on daily.dev](https://daily.dev/posts/performance-analysis-in-linux-mwalg0mn2) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Performance analysis in Linux","url":"https://daily.dev/posts/performance-analysis-in-linux-mwalg0mn2","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/performance-analysis-in-linux-mwalg0mn2"},"datePublished":"2026-08-11T08:54:05.818Z","dateModified":"2026-08-11T09:24:21.385Z","description":"Dynamic profiling tools help identify non-obvious performance bottlenecks in Linux applications. 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Then use `perf record ./your-program` followed by `perf annotate` to pinpoint hot instruction regions in the source. Compile with `-g3` to interleave C source with assembly output. A branch miss rate above ~16% is a strong signal of pipeline stall overhead. Developers tracking down CPU pipeline issues like this share findings and tooling tips on daily.dev."}},{"@type":"Question","name":"Why does sorting an array before iterating over it make my code faster?","acceptedAnswer":{"@type":"Answer","text":"Sorting enables the CPU branch predictor to learn the pattern of conditional branches. With random data, the predictor guesses wrong roughly half the time, causing pipeline flushes on each miss. With sorted data, the branch outcome is predictable — one contiguous region skips the if-leg, another always enters it — dropping the miss rate from 16% to 0.02% and more than doubling instructions-per-cycle throughput. 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