Measuring cache misses on macOS with Instruments
📰 Reddit r/programming
Learn to measure cache misses on macOS using Instruments for optimized programming
Action Steps
- Install Instruments on macOS to profile applications
- Run a toy problem, such as iterating sequentially or randomly, to see cache misses in action
- Use Instruments to track L1 data cache misses and analyze the results
- Apply optimization techniques, like tiling or structuring data, to reduce cache misses
- Compare the performance of different coding approaches, such as AoS vs SoA, using Instruments
Who Needs to Know This
Developers and programmers can benefit from understanding cache misses to optimize their code, while team leads can use this knowledge to improve overall project performance
Key Insight
💡 Measuring cache misses is crucial for optimized programming, and Instruments provides a powerful tool for doing so on macOS
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🚀 Optimize your code with Instruments: measure cache misses on macOS!
Key Takeaways
Learn to measure cache misses on macOS using Instruments for optimized programming
Full Article
I couldn't find many resources online that showed how to see cache misses on macOS. I thought I'd learn a bit about Instruments, throw some toy problems at it, and write about the experience in a blog post. These toy examples - iterating sequentially then randomly, summing elements in a matrix, naive vs tiled matmul and iterating AoS vs SoA - are great to see some real numbers from L1 data cache misses, but they're far away from real programming problems.
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