In this post, we compare three ways to find the first matching byte in an ASCII sequence—naive loops, SWAR (SIMD Within A Register) techniques, and the C memchr via Haskell’s FFI. You’ll learn how each performs in real benchmarks, where FFI can deliver 50× speedups, and why overhead can erase those gains in certain workloads.

In this post, we explore how to write high-performance Haskell code using multi-dimensional arrays—comparing vector and massiv across clarity, parallelism, and raw speed. You'll learn how array layout, indexing strategies, and stencil-based computation can unlock massive performance gains by leveraging modern hardware more effectively.

In this post, we explore the performance characteristics of string matching in ASCII text, comparing brute force, Boyer-Moore-Horspool, and DAWG-based approaches. You'll learn how benchmarking, algorithmic analysis, and practical implementation tradeoffs intersect—revealing when optimizations help, when they hurt, and how understanding your tools leads to better real-world performance.

In this post, discover how to push Haskell’s performance boundaries by using SWAR (“SIMD Within A Register”) techniques to validate ASCII data in bulk. You’ll see how packing bytes into registers—using bit masking to flag non-ASCII values in parallel—and unrolling loops to leverage the CPU’s superscalar engine both drive down overhead. By the end, we'll transfer a simple foldl'-based ASCII detecting function into a version running 10x faster.

In this post, discover how quasiquoters overcome three key smart-constructor limitations—compile-time errors, lack of pattern matching, and fixed syntax—by leveraging Template Haskell to produce both expressions and patterns with familiar, literal syntax.