This Algorithm is 1,606,240% FASTER

TL;DR

Improve algorithm running time by implementing optimization techniques, such as using vectors instead of hash sets and bit manipulation, resulting in significant speed improvements.

Transcript

1.6 million percent faster it's hard to even display visually because it shows up as nothing today I'm going to show you the seven steps taken to improve the running time of an algorithm but here's the kicker the slowest one was O of N and the fastest one is still o of N and along the way I'm going to use common optimization techniques that you can... Read More

Key Insights

• ⌛ Optimization techniques, such as inserting characters one at a time and using vectors instead of hash sets, can significantly improve algorithm running time.
• 🫦 Bit manipulation, specifically XOR operations, can be utilized to efficiently store and check the state of a search.
• 🐎 Unrolling loops and utilizing SIMD optimization can further enhance the speed of an algorithm.
• 🐎 The final optimized solution, with multi-threading and advanced optimization techniques, can achieve a speed improvement of over 16,000 times compared to the initial solution.

Questions & Answers

Q: What is the initial approach to solving the problem of finding 14 distinct characters in a string?

The initial approach involves using a hash set to store the first 14 characters from the string and checking the length of the hash set to determine if 14 distinct characters have been found.

Q: What is the first optimization technique that improves the running time of the algorithm?

The first optimization is to insert characters one at a time and stop the process if a duplicate is detected. This simple if statement results in a 92% faster runtime.

Q: How does using a vector instead of a hash set improve the running time further?

While hash set lookup is constant time, it involves computing a hash, index calculation, and potentially checking for collisions. Using a vector, which requires a simple reference follow and offset calculation, results in a significantly faster runtime.

Q: How does the final optimization technique, involving bit manipulation, achieve a speed improvement?

By using a singular 32-bit number to represent the state of the search, the algorithm can use bit manipulation, specifically the XOR operation, to toggle bits and check for duplicate characters. This technique, combined with other optimizations, leads to a runtime improvement of over 233 times compared to the initial hash set solution.

Key Insights:

• Optimization techniques, such as inserting characters one at a time and using vectors instead of hash sets, can significantly improve algorithm running time.
• Bit manipulation, specifically XOR operations, can be utilized to efficiently store and check the state of a search.
• Unrolling loops and utilizing SIMD optimization can further enhance the speed of an algorithm.
• The final optimized solution, with multi-threading and advanced optimization techniques, can achieve a speed improvement of over 16,000 times compared to the initial solution.
• These optimization techniques can be applied to various algorithms and improve the efficiency of computational processes.

Summary & Key Takeaways

• The content discusses the process of improving the running time of an algorithm by implementing various optimization techniques.

• The problem used in the example is finding 14 distinct characters in a long string, and the initial solution involves using a hash set.

• Through a series of optimizations, including using vectors instead of hash sets and bit manipulation, the algorithm's running time is significantly improved.