Performance in BQN versus C
BQN, despite being interpreted through a C-based system (CBQN), can sometimes outperform hand-written C code due to efficient use of array-oriented programming and optimized primitives. Performance advantages arise particularly in tasks leveraging BQN's built-in operations, such as processing line endings in text files. However, achieving such performance requires programmer expertise and favorable problem structures, and typical BQN programs are unlikely to surpass well-optimized C in general.
- ▪BQN programs can outperform equivalent C programs in specific cases, especially when using built-in primitives like Scan, Transpose, or Sort.
- ▪CBQN leverages low-level optimizations such as SIMD and lookup tables, enabling it to achieve up to 10x speed improvements over basic C implementations.
- ▪In a real-world benchmark replacing CRLF with LF in text, BQN matched C at around 200 bytes and reached over 5x faster performance on larger inputs, exceeding 10x with AVX-512 enabled.
- ▪Writing high-performance code in both C and BQN demands specialized knowledge—branchless programming and bit manipulation in C, and array-oriented techniques in BQN.
- ▪The performance advantage of BQN is not universal and depends on problem structure, input density, and hardware factors like cache and vector instruction support.
Opening excerpt (first ~120 words) tap to expand
(github) / BQN / implementation Performance in BQN versus C The title alone! You know BQN has some fast performance to wave around! If you think it's paradoxical that programs written for CBQN, a C-based interpreter, might outperform programs written directly in C, it's because you think performance comes from the language implementation. Not so: performance comes from the programmer, taking advantage of the features offered by their language implementation. The difficulty of doing so has two consequences: BQN programs can and do outperform equivalent C programs. Your BQN programs are unlikely to outperform equivalent C programs.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
