Less Slow C++
📰 Hacker News · ashvardanian
Improve C++ performance by exploring coroutines, SIMD, and secure enclaves, and learn how to optimize memory access and error handling
Action Steps
- Explore coroutines for high-performance work using libraries like cppcoro
- Use SIMD intrinsics for clarity and performance, and consider dropping to assembly for easier library distribution
- Investigate hardware support for vectorized scatter/gather in AVX-512 and SVE
- Compare secure enclaves and pointer tagging on Intel, Arm, and AMD architectures
- Measure the throughput gap between CPU and GPU Tensor Cores (TCs) using benchmarks like MLPerf
- Optimize memory access by minimizing misaligned memory accesses and split-loads, and using non-temporal loads/stores
Who Needs to Know This
This article is relevant to software engineers, particularly those working on high-performance applications, as it discusses optimization techniques and design choices that can impact performance
Key Insight
💡 Coroutines, SIMD, and secure enclaves can significantly improve C++ performance, but require careful evaluation of trade-offs and optimization techniques
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🚀 Improve C++ performance with coroutines, SIMD, and secure enclaves! 🤔
Key Takeaways
Improve C++ performance by exploring coroutines, SIMD, and secure enclaves, and learn how to optimize memory access and error handling
Full Article
Earlier this year, I took a month to reexamine my coding habits and rethink some past design choices. I hope to rewrite and improve my FOSS libraries this year, and I needed answers to a few questions first. Perhaps some of these questions will resonate with others in the community, too. - Are coroutines viable for high-performance work? - Should I use SIMD intrinsics for clarity or drop to assembly for easier library distribution? - Has hardware caught up with vectorized scatter/gather in AVX-512 & SVE? - How do secure enclaves & pointer tagging differ on Intel, Arm, & AMD? - What's the throughput gap between CPU and GPU Tensor Cores (TCs)? - How costly are misaligned memory accesses & split-loads, and what gains do non-temporal loads/stores offer? - Which parts of the standard library hit performance hardest? - How do error-handling strategies compare overhead-wise? - What's the compile-time vs. run-time trade-off for lazily evaluated ranges? - Wha
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