--- title: How We Increased Shannon Entropy Throughput in Go by 41%... description: A Go-based Git secret scanner called Crenox optimized its Shannon entropy calculation, which is used to flag high-randomness strings like tokens and... canonical: https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How We Increased Shannon Entropy Throughput in Go by 41% Using a Lookup Table. | daily.dev og:description: A Go-based Git secret scanner called Crenox optimized its Shannon entropy calculation, which is used to flag high-randomness strings like tokens and... og:url: https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk og:image: https://api.daily.dev/og/posts/Qu11TYRHk.png og:image:alt: How We Increased Shannon Entropy Throughput in Go by 41% Using a Lookup Table. 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. # How We Increased Shannon Entropy Throughput in Go by 41% Using a Lookup Table. **[Awesome Go](https://daily.dev/sources/awego)** · 10 min read · 1 upvotes · 0 comments ## Summary A Go-based Git secret scanner called Crenox optimized its Shannon entropy calculation, which is used to flag high-randomness strings like tokens and credentials. By algebraically rewriting the entropy formula to factor out the input length, the expensive count × log2(count) term became isolated and could be precomputed into a small lookup table (513 float64 values, about 4 KiB) for inputs up to 512 bytes, falling back to the standard calculation for larger inputs. Benchmarks on an Intel Xeon Platinum CPU showed the LUT version dropped from 228.153 ns/op to 161.441 ns/op, a 29.2% latency reduction translating to a 41.4% throughput increase, with zero heap allocations in both versions. The piece emphasizes optimizing the mathematical structure of a computation before reaching for assembly, CGo, or unsafe code, and stresses measuring latency and throughput as distinct metrics. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I speed up Shannon entropy calculation in Go without using unsafe code or CGo? You can rewrite the entropy formula algebraically to factor out the input length n, reducing the per-byte computation to count times log2(count) instead of (count/n) times log2(count/n). Since count is a bounded integer for inputs up to 512 bytes, you precompute a 513-entry float64 lookup table at initialization, replacing repeated logarithm calls with simple array lookups in the hot loop. _Developers chasing hot-loop speedups in Go can track algorithmic optimization techniques like this on daily.dev._ ### Why does a Shannon entropy lookup table in Go need to be sized 513 instead of 512? Go arrays are zero-indexed, so a 513-element array provides valid indices from 0 to 512. If an input of exactly 512 bytes consists entirely of one repeated byte value, its frequency count reaches 512, requiring index 512 in the table, which only exists if the array is declared with size 513. _Anyone building lookup-table optimizations in Go can compare implementation details like this via daily.dev._ ### What performance improvement can a lookup table give for Shannon entropy calculation in Go compared to the naive floating-point implementation? A lookup-table-based Shannon entropy implementation reduced per-operation latency from 228.153 ns/op to 161.441 ns/op, a 29.2% reduction, while increasing throughput by approximately 41.4%, measured with Go's benchmark tooling on an Intel Xeon Platinum CPU. Both the naive and optimized versions reported zero heap allocations per operation. _Engineers benchmarking Go performance tricks can follow measured results like these on daily.dev._ ## Similar posts on daily.dev - [Hash tables in Go and advantage of self-hosted compilers](https://daily.dev/posts/hash-tables-in-go-and-advantage-of-self-hosted-compilers-tkdlr8n5e) · Lobsters · 4 upvotes · 0 comments - [Simple batch decoding of unary codes](https://daily.dev/posts/simple-batch-decoding-of-unary-codes-kyuqotiln) · Ryg blog · 0 upvotes · 0 comments - [Binary Obfuscation that Doesn't Kill LTO: A Recap of My Thotcon Talk](https://daily.dev/posts/binary-obfuscation-that-doesn-t-kill-lto-a-recap-of-my-thotcon-talk-5st70tkez) · Hacker News · 0 upvotes · 0 comments --- Tags: [#performance](https://daily.dev/tags/performance), [#golang](https://daily.dev/tags/golang), [#algorithms](https://daily.dev/tags/algorithms), [#appsec](https://daily.dev/tags/appsec) [View this post on daily.dev](https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk) ```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":"How We Increased Shannon Entropy Throughput in Go by 41% Using a Lookup Table.","url":"https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk"},"datePublished":"2026-08-20T20:15:43.542Z","dateModified":"2026-09-14T08:07:16.067Z","description":"A Go-based Git secret scanner called Crenox optimized its Shannon entropy calculation, which is used to flag high-randomness strings like tokens and...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/b747433052719fd7fd15e74d8fa1c0df?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/b747433052719fd7fd15e74d8fa1c0df?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Awesome Go","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Awesome Go","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/awego","url":"https://daily.dev/sources/awego"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"performance,golang,algorithms,appsec","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Awesome Go","item":"https://daily.dev/sources/awego"},{"@type":"ListItem","position":3,"name":"How We Increased Shannon Entropy Throughput in Go by 41% Using a Lookup Table."}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/how-we-increased-shannon-entropy-throughput-in-go-by-41-using-a-lookup-table--qu11tyrhk#faq","mainEntity":[{"@type":"Question","name":"How can I speed up Shannon entropy calculation in Go without using unsafe code or CGo?","acceptedAnswer":{"@type":"Answer","text":"You can rewrite the entropy formula algebraically to factor out the input length n, reducing the per-byte computation to count times log2(count) instead of (count/n) times log2(count/n). Since count is a bounded integer for inputs up to 512 bytes, you precompute a 513-entry float64 lookup table at initialization, replacing repeated logarithm calls with simple array lookups in the hot loop. Developers chasing hot-loop speedups in Go can track algorithmic optimization techniques like this on daily.dev."}},{"@type":"Question","name":"Why does a Shannon entropy lookup table in Go need to be sized 513 instead of 512?","acceptedAnswer":{"@type":"Answer","text":"Go arrays are zero-indexed, so a 513-element array provides valid indices from 0 to 512. If an input of exactly 512 bytes consists entirely of one repeated byte value, its frequency count reaches 512, requiring index 512 in the table, which only exists if the array is declared with size 513. 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