Explore & Benchmark Booth Multiplication
Generate millions of signed integer pairs, run optimized Booth multiplication via native Python (Numba) or in-browser BigInt implementations, compare sequential vs parallel execution, and export results — all in a streamlined educational & performance lab environment.
Deterministic Data
Seeded RNG lets you regenerate identical datasets for consistent benchmarking and reproducibility in reports.
Hybrid Stack
Python backend leverages Numba for multi-threaded JIT acceleration; browser frontend uses Web Workers for parallelism.
Algorithm Clarity
Clean BigInt Booth implementation mirrors the two's complement procedure for instructional transparency.
Performance Metrics
Throughput & elapsed time displayed instantly. Compare sequential vs parallel strategies interactively.
Scalable Input
Handle up to millions of pairs in memory; switch to chunking strategies in Python for extreme scales.
Downloadable Outputs
Export computed products for downstream analysis or audit trails with a single click.
Built-In Validation
Random sampling ensures correctness without expensive full cross-checks during large runs.
Theme Adaptive
Dark & light modes remembered per device for long analysis sessions.
Extendable Design
Modular structure makes it easy to add visual bit-step animations or new algorithms.
Educational Value
Great for demonstrating how Booth reduces additions via bit-pair recoding on signed values.