Martin Thompson explores performance testing, how to avoid the common pitfalls, how to profile when the results cause your team to pull a funny face, and what you can do about that funny face. Specific issues to Java and managed runtimes in general will be explored, but if other languages are your poison, don't be put off as much of the content can be applied to any development.
Emad Benjamin explains how to deploy and tune a JVM on a virtual infrastructure (vSphere), and how to tune the garbage collector in this environment.
Gareth Rushgrove overviews Ruby on Rails and Django: object caches, fragment and HTTP caching, asset compilation, profiling, log file measurement and framework hooks for instrumentation.
Johan Oskarsson explains how Twitter is using Zipkin to trace a pages in order to see their execution path and to determine the time spent for loading for performance monitoring and analysis.
David Nolen unveils optimization techniques behind ClojureScript: code size, expression-oriented semantics, numerics, operations on JS primitive types, persistent data structures, etc.
Lars Bak presents several language virtual machines (Self, Strongtalk, Hotspot), why they matter and how they influenced V8 and Dart. His talk is centered on performance.
Jay Harris offers tips on using unit testing to improve the performance of applications.
Steve Souders discusses the importance of mobile performance, providing advice on creating more responsive mobile apps, and outlining the latest developments in analyzing mobile performance.
Charlie Hunt explains what can be done to lower the latency introduced by the Java GC and JIT, including coding tips, and introducing tools for tuning the performance of Java applications.
Daniel Solano Gómez shares 11 tips for drastically enhancing the performance of Clojure applications crunching numbers.
Martin Thompson and Michael Barker explain how Intel x86_64 processors and their memory model work along with low-level techniques that help creating lock-free software.
Markus Püschel proposes to solve scientific calculation performance problems with code generation tools, introducing Spiral, an automatic performance programming framework for linear transforms.