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Presentation: A Walk Along the Complexity-performance Curve
- The presentation discusses the concept of the performance complexity curve, originating from Aleksey Shipilev's performance work phase diagram, which illustrates how performance and complexity evolve during performance optimization phases.
- Different phases of performance tuning are explained, starting with addressing low-hanging fruit issues, such as non-functional bugs, poor programming practices, and algorithmic inefficiencies that can be detected and resolved through profiling tools.
- The talk delves into the tuning phase, where more sophisticated optimizations are explored, like optimizing the String#hashCode method in Java, leading to significant improvements in performance through manual unrolling and vectorization.
- The competitive advantage phase is highlighted, showcasing the implementation and integration of optimized solutions into JDK 21, demonstrating how efficient performance improvements can be achieved through collaborative efforts between organizations like Datadog, Oracle, and Intel.
- The importance of continuous profiling, objective setting, and utilizing tools like vectorization for performance enhancement is emphasized, along with insights on the evolution of hardware optimizations and the significance of adapting software to modern architectures.
- The presentation concludes by discussing key takeaways on performance optimization, the impact of outdated hardware optimizations, and the timelines involved in identifying, implementing, and deploying performance improvements in large-scale software projects.
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