The document discusses optimization techniques for deep learning frameworks on Intel CPUs and Fugaku aimed architectures. It introduces oneDNN, a performance library for deep learning operations on Intel CPUs. It discusses issues with C++ implementation, and how just-in-time assembly generation using Xbyak can address these issues by generating optimal code depending on parameters. It also introduces Xbyak_aarch64 for generating optimized code for Fugaku's Scalable Vector Extension instructions.
The document discusses optimization techniques for deep learning frameworks on Intel CPUs and Fugaku aimed architectures. It introduces oneDNN, a performance library for deep learning operations on Intel CPUs. It discusses issues with C++ implementation, and how just-in-time assembly generation using Xbyak can address these issues by generating optimal code depending on parameters. It also introduces Xbyak_aarch64 for generating optimized code for Fugaku's Scalable Vector Extension instructions.
The document contains numerical and color information. It starts with k=1, then k=3, and the majority of the document contains repetitive instances of k=2 followed by two dots in different color patterns.
This document contains mathematical equations and calculations involving variables and coefficients. Steps are shown to solve for unknown variables through algebraic manipulation and substitution of values into equations. The final lines indicate the solution was checked against the original problem.
- The document contains code and explanations for solving optimization problems using dynamic programming, including calculating minimum costs using a 2D array to store results.
- It describes applying dynamic programming to problems involving finding minimum costs for tasks that can be split into subtasks, with the overall cost determined by combining subtask costs.
- The code provided shows initializing a 2D array and using nested for loops to iterate through values, calculate minimum costs based on previous results, and store them in the 2D array to build up an optimal solution.