#matrixmultiplication#cachelocality#looptiling
17K subscribers • 1 video on Hallway
Intermediate. Assumes working knowledge.
139K views3 years ago2 recommendations
Systems programmingComputer sciencePerformance and optimizationAlgorithmsCPUs and computer architecture
An explanation of how to optimize memory-bounded algorithms like matrix multiplication using cache blocking and loop tiling.
In the last video, I introduced the concepts of compute-bounded and memory-bounded tasks. This video takes a step further and uses the theory we discussed to optimize a famous memory-bounded algorithm. Many of these tricks are counterintuitive but highly effective. By the end of the video, you'll find we can make the algorithm around 120x faster than the naive implementation. Join the Discord server: discord.gg/ux9BKSu4eg My GitHub profile: github.com/fangjunzhou The source code used in this video: github.com/fangjunzhou/blas-playground Motion Canvas official repository: github.com/motion-canvas/motion-canvas My custom fork of Motion Canvas: github.com/fangjunzhou/motion-canvasWhat people say. Recommended for understanding how cache locality and memory hierarchy affect software performance.
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