InfoQ Homepage Performance Tuning Content on InfoQ
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Practicing at the Cutting Edge: Learning and Unlearning about Performance
Martin Thompson discusses the major steps in the evolution of Java and how it contrasts to alternative technologies, and the challenges of pushing the limits of performance.
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JS Optimization Techniques
Guillaume Lathoud suggests expanding JavaScript with mutual tail-call optimization, map/filter/reduce and math computations to obtain faster code.
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New Optimizations of Google Chrome's V8
Ben Titzer presents the latest optimizations of the Chrome V8 engine: reducing pause times through asynchrony and incrementalism, and JIT compiler optimizations targeting all JavaScript programs.
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One Small Step for Consumers, One Giant Lead for Enterprise
In this solutions track talk, sponsored by AppDynamics, Tom Levey discusses how to monitor UX, identify bottlenecks, and measure the revenue impact by turning on the lights inside a mobile app.
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Enabling Java in Latency Sensitive Environments
Gil Tene examines the core issues that have historically kept Java environments from performing well in low latency environments and how it can perform now without trade-offs and compromises.
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Java Marshalling: A Performance Approach
Todd Montgomery proposes a new approach to marshalling in Java using FIX/SBE, new marshalling API approaches, and the extensive application of mechanical sympathy to this problem domain.
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Functional Vectors, Maps, and Sets in Julia
Zach Allaun shows how to build a functional and persistent vector, hash map, and set on top of the same data structure, and how to optimize the code for performance.
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Top 10 - Performance Folklore
Martin Thompson discusses Java, concurrency, operating systems, and functional programming in the context of designing and testing high-performance systems.
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Practicing at the Cutting Edge: Learning and Unlearning about Java Performance
Martin Thompson overviews Java's evolution, comparing it with C++'s, discussing the challenges of pushing the performance limits.
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Fast and Dynamic
Maxime Chevalier-Boisvert discusses making dynamic languages faster providing various examples of optimizations: SmallTalk, LISP machine, Google V8 and others.
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Why Ruby Isn't Slow
Alex Gaynor explains how he solved the usual Ruby VM speed problems with Topaz, a high performance VM built on the same technologies that power PyPy.
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The Unreasonable Effectiveness of Tuning
Keith Adams shares HHVM insights showing how a system can become very performant if it is well tuned.