The represents the bext version yet of my Game of Life optimization, delivering ~10,000x performance improvement over my first naive implementation through bit manipulation and hardware-accelerated computation. While benchmarking this engine achieved 158 billion cell updates per second on my laptop, although there is still plenty of room for improvement.

The engine automatically detects your hardware capabilities and selects optimal SIMD configurations, supporting widths of 4, 8, or 16 parallel operations. It uses configurable generic parameters UltimateEngine<const N: usize> where N represents the SIMD lane count, allowing compile-time optimization for specific hardware targets.

The engine stores 64 cells per u64 integer, achieving 8x memory efficiency compared to byte-per-cell representations. This bit-packing strategy dramatically improves cache performance and enables vectorized operations on large cell groups simultaneously.

Grid layout uses MSB-first bit ordering with strategic padding which eliminates boundary checks during computation, while SIMD alignment ensures optimal memory access patterns. Pre-computed boundary masks handle edge cases efficiently without conditional branching in the hot path.

The engine leverages Rust's portable SIMD to process multiple 64-bit words simultaneously. Custom shift operations shl() and shr() handle cross-lane bit propagation using rotate_elements_left/right with pre-selected masks to maintain bit continuity across SIMD lanes. Using 8 SIMD lanes represents a 512x speedup compared to byte-per-cell representations.

Memory prefetching on x86_64 architectures uses _mm_prefetch with _MM_HINT_T0 to load next rows into L1 cache before processing, reducing memory latency. The engine processes columns in SIMD-aligned chunks, maximizing instruction-level parallelism and minimizing cache misses.

The core sub_step() function implements an optimized full/half adder algorithm for counting neighbors. Rather than iterating through 8 neighbors individually, it uses a two-stage binary tree reduction to compute neighbor counts for all 64 cells in parallel.

Neighbors are grouped into pairs using XOR for sum bits and AND for carry bitsand then combined. The Game of Life rules are applied using bitwise operations: center |= ab; center &= b3 ^ b4; center &= !c0; eliminating conditional branching in the hotpath, allowing the vectorization to remain efficient.

The engine uses Rayon thread pools to distribute row processing across CPU cores. Work is divided into chunks based on available_parallelism() with automatic load balancing. Each thread processes a contiguous block of rows, minimizing false sharing and maximizing cache locality.

Thread synchronization uses scoped threads with zero-overhead spawning. The double-buffering strategy with swap(&mut self.field, &mut self.new_field) eliminates memory allocation overhead between generations, while maintaining memory access patterns for optimal cache performance.

WebAssembly compilation maintains near-native performance while providing seamless browser integration. The engine detects the WebAssembly target using cfg!(target_arch = "wasm32") and automatically disables thread pools, falling back to sequential processing with identical SIMD optimizations.

SIMD operations compile to WebAssembly SIMD instructions when supported, providing significant performance benefits in modern browsers. The engine gracefully handles hardware limitations with automatic fallback to scalar implementations, ensuring compatibility across all platforms while maximizing performance on capable hardware.

Several optimization vectors remain unexplored for further performance enhancement. Current benchmarks indicate memory bandwidth saturation as the primary bottleneck, with CPU utilization plateauing at approximately 50% due to DRAM throughput limitations. Future work could investigate other memory access pattern optimizations, including software prefetching heuristics, and cache-friendly algorithms to mitigate memory bottlenecks.

I hope the attention to detail and the performance optimizations are appreciated even though they weren't needed to begin with, the initial implementation could do 12 million cell updates per second, which is plenty for this webpage and most tasks in general. If I really wanted to go faster there are always GPU implementations to explore as well.

A highly optimized Conway's Game of Life simulator featuring advanced SIMD parallelism and automatic hardware detection. The engine delivers 10,000x performance improvements through bit-packed representation (64 cells per u64), vectorized operations, and multi-threading with Rayon.

Key Features: Automatic SIMD width selection, memory prefetching, boundary optimization with ghost cells, and support for multiple input formats.

Repository Link: Link

A Rust implementation that solves the NP-Complete problem of reversing Conway's Game of Life by converting it into boolean satisfiability (SAT) problems. Supports both CaDiCaL (single-threaded) and ParKissat-RS (multithreaded) solver backends for optimal performance across different problem sizes.

Key Features: Multiple SAT backends, hybrid encoding strategies, configurable parameters via YAML, comprehensive benchmarking tools, pattern analysis, and multiple output formats (text, JSON, visual).

Repository Link: Link

Safe Rust bindings for the ParKissat-RS parallel SAT solver, winner of SAT Competition 2022. Provides a memory-safe, idiomatic Rust API while maintaining high performance through minimal overhead FFI calls. Combines Kissat solver efficiency with parallel processing capabilities. Multithreaded may be a bit of a stretch since parallelizing SAT solving is very difficult, but some problems will be able to utilize multiple cores.

Key Features: Multi-threaded SAT solving, DIMACS format support, configurable solver parameters, comprehensive error handling, interruption support.

Repository Link: Link

A Rust crate that converts text input into pixel art using 1s and 0s with a sophisticated variable-width font system. Supports A-Z uppercase, a-z lowercase, and space characters with optimal spacing through 1-5 pixel wide characters for compact, readable output.

Key Features: Variable-width font optimization, automatic buffer generation, case-sensitive rendering, graceful error handling without unwrap() calls, and performance optimization with pre-allocated collections.

Repository Link: Link

Create your text pattern using either the website functionality above or the text_to_input crate. You can also manually draw on the grid with your mouse if you prefer custom patterns.

Website Method: Enter your name in the text input above, adjust the buffer size, and click "Generate from Text". Then use the download button to save your pattern.

CLI Method: Use the text_to_input crate for more control:

Important: No matter which method you use, you must download/save the pattern file for the next steps.

Clone and set up the reverse Game of Life crate. This is the only crate not implemented on the website because running a SAT solver in the browser is not advisable.

Prerequisites: You must have Rust installed on your system. Check the crate documentation for additional requirements.

Repository Link: Link

Move your generated pattern file into the input area of the reverse Game of Life crate, then run the solver to find a predecessor state.

Performance Guide: For a first name, 3-4 generations will take on the order of minutes, whereas 10 generations took about 12 hours on my laptop.

Take the new file from the reverse solver and upload it to the website above, or load it into the game_of_life crate. The initial state will look like random static - this is expected!

File Location: The solver will output a predecessor state file in the output directory. This file will contain multiple generations - this is completely normal! The one labelled generation 0 is the one you want, copy this into a new text file for upload.

Click the Multi-Step button and watch as your name emerges from the apparent chaos! The seemingly random initial state will evolve through the deterministic rules of Conway's Game of Life to reveal your text pattern.

TODO: I am planning on adding a function to turn this evolution into a downloadable GIF. The implementation approach is still being determined, but this feature will allow you to easily share your name emergence animation.

This section will be updated once the GIF generation functionality is implemented.

No Solutions Found? If your name has no solutions, try adding a bigger buffer to the pattern. This increases the search space and makes it more likely to find a solution, though it will also increase computation time.

Performance Optimization: Start with fewer generations (3-4) for initial testing. You can always increase the generation count for more dramatic emergence effects once you've confirmed the pattern works.

Pattern Design: Simpler patterns with more spacing tend to have more solutions. Complex, tightly-packed text may require longer solve times or larger buffers.

The project successfully demonstrates the feasibility of using SAT solving to reverse-engineer Conway's Game of Life states. By encoding cellular automaton rules and constraints into boolean logic clauses, the system can efficiently discover predecessor states that would evolve into a given target configuration.

Performance Benchmarks: The system reveals impressive scalability for practical use cases: a 300-cell grid can be traced back 4 generations in under a second, while more ambitious reverse-engineering tasks like 6 generations require 8 minutes and 10 generations complete in approximately 2 hours. Even computationally intensive 20-generation lookbacks finish within 48 hours, anthough your results may vary, bigger grids will result in much longer search times.

The most significant challenge encountered was SAT encoding optimization, where initial implementations generated an excessive number of clauses that severely degraded solver performance. This experience highlighted how the formulation of constraints can dramatically impact computational efficiency, even when the underlying logical relationships remain equivalent.

Optimization Success: Through careful pruning I was able to reduce the number of clauses by a factor of 10 without compromising the encoding accuracy. When it comes to problems with exponential runtime complexity, pre-compute optimization becomes increasingly important for practical applications.

The next phase will focus on leveraging parallelization to further enhance performance, specifically investigating multithreaded SAT solvers with robust Rust bindings for seamless integration into the existing codebase. The ParKissat-RS integration represents a significant step in this direction.

Additional Opportunities: Optimizing auxiliary algorithms and data structures presents opportunities for incremental improvements, though these optimizations will likely yield diminishing returns given that core SAT solving dominates the computational workload for large-scale problems. While I do not anticipate much more active development, I will post any breakthroughs here.