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We settled on using AVX2, with 256 bit vectors. This is a perfect strategy when dealing with vector problems - as is the case for our task. SIMD is a way to instruct the CPU to process multiple data points using one instruction. SIMD for helpДЄn obvious improvement is to use more complex SIMD instructions. It took 55ms of CPU time on average to find the closest neighbour. We matched 1,536 test hashes against a database of 1 million random vectors in 85 seconds. HereвЂ™s our first run of the first, naive, algorithm: We generated the test smartly by byte-swaps of the actual data from the database - this allows us to precisely control the distance between test hashes and database hashes. The query hashes canвЂ™t be random and must be slightly more sophisticated, otherwise the exercise wouldnвЂ™t be that interesting.

The large database of known hashes can be well emulated by random data.

#Rimworld already has short hash full#

This alone adds up to 432 million operations! How does it look in practice? To illustrate this blog post we prepared a full test suite. Finding the smallest hash distance in a database of, say, 1M entries, would require going over all records, and would need at least:ДЄnd more.

#Rimworld already has short hash code#

While this code might be simple, we expect it to be rather slow. Surely this has been solved in the past - but letвЂ™s not jump ahead. In our case it doesnвЂ™t matter if weвЂ™ll compare distances or squared distances!ДЄs you can see, fuzzy matching is basically a standard problem of finding the closest point in a multi-dimensional space. We donвЂ™t need the actual distance value, we just need to find the vector with the smallest one. Inside the code, for performance and simplicity, weвЂ™ll mostly operate on the squared value. We avoid computing the actual distance to save us from running the square root function вЂ“ itвЂ™s slow. This function returns the squared distance.

#Rimworld already has short hash how to#

HereвЂ™s how to count the squared distance in C: To calculate the distance between two 144-byte hashes, we take each byte, calculate the delta, square it, sum it to an accumulator, do a square root, and ta-dah! We have the distance! The Euclidean distance equation used by the algorithm is standard: This is the expected result - most of our queries will not have a related image in the database. Otherwise, when the distance is large, we can assume the images arenвЂ™t similar.

Given two such points, we can calculate the distance using the standard Euclidean formula.ДЇor our particular problem, though, we are interested in the вЂњclosestвЂќ match in a database only if the distance is lower than some predefined threshold. We treat each hash as a vector containing 144 numbers, identifying a point in a 144-dimensional space. Given a query hash, the fuzzy match is the вЂњclosestвЂќ hash in a database. But how much work exactly?ДЇirst, we need to explain how fuzzy matching works.

In naive implementation, this creates a lot of work. The first algorithm that comes to mind has O(K*N) complexity: for each query, go through every hash in the database. To make this work, we need to seriously optimize the matching algorithm. We want to match thousands of hashes per second, of images passing through our network, against a database of millions of known images. The specific algorithm is well defined, but making it fast is left to the programmer - and at Cloudflare we need the matching to be done super fast. The hash is designed to be used in a fuzzy matching algorithm that can find вЂњnearbyвЂќ, related images. As John explained in his blog post, the image matching algorithm creates a fuzzy hash from a processed image. The problem was in the speed of the matching algorithm itself. Some time later, I was chatting with Kornel: вЂњWe have all the pieces in the image processing pipeline, but we are struggling with the performance of one component.вЂќ Scaling to Cloudflare needs ainвЂ™t easy! I remember thinking: this is so cool! Image processing is always hard, and deploying a real image identification system at Cloudflare is no small achievement! Late last year I read a blog post about our CSAM image scanning tool. Post Syndicated from Marek Majkowski original