Explore >> Select a destination
|
You are here 
|
beeb.li | ||
| | | | |
bayesianneuron.com
|
|
| | | | | [AI summary] The user has shared a detailed exploration of optimizing the 0/1 Knapsack problem using dynamic programming with Python and NumPy. They discuss various optimization techniques, including reducing memory usage with a 2-row approach, vectorization using NumPy's `np.where` for faster computation, and the performance improvements achieved. The final implementation shows significant speedups, especially for large-scale problems, and the user highlights the importance of vectorization and efficient memory management in computational tasks. | |
| | | | |
andreabergia.com
|
|
| | | | | This post is part of the Writing a JVM in Rust series. In this post, I will discuss how rjvm parses .class files. The code I will discuss today is contained in the reader crate. A warning before you read: this is the earliest part of the project and, since I have written this project to learn Rust, it is also the one that contains the least idiomatic code. Don't take this as an example of the best Rust ever written! | |
| | | | |
cprimozic.net
|
|
| | | | | A detailed summary of the techniques I used to optimize my Advent of Code 2024 solution for Day 9 Part 2. Employs a variety of techniques including algorithmic shortcuts, bespoke data structures, and low-level optimizations + SIMD. | |
| | | | |
www.nayuki.io
|
|
| | | [AI summary] The user has provided a comprehensive overview of the x86 architecture, covering topics such as basic arithmetic operations, control flow with jumps and conditionals, memory addressing modes, the stack and calling conventions, advanced instructions like SSE, virtual memory, and differences between x86-32 and x86-64. The user is likely looking for a summary or clarification of the x86 architecture, possibly for learning purposes or to reinforce their understanding. | ||
