Michael Kopp explains how to run performance code at scale with Hadoop and how to analyze and optimize Hadoop jobs.
Charlie Hunt and Monica Beckwith describe the operational basics of G1 and how to tune it, along with tips on what to expect when migrating from Parallel GC or CMS to G1 GC.
Charlie Hunt presents the fundamentals of JVM tuning and provides advice for developers on writing a Java application that performs well at runtime.
Gil Tene discusses JVM observation-based runtime optimizations, ordering and memory model rules, basics GC functions, memory management, and JVM mechanics.
Ben Evans discusses garbage collection in Java along with some tooling for understanding and visualizing how it works.
Keith Adams shares details on building HHVM, a PHP VM built by Facebook, along with lessons learned doing it and tuning it for high performance.
Gil Tene discusses some common pitfalls encountered in measuring and characterizing latency, demonstrating and discussing some false assumptions and measurement techniques that lead to dramatically incorrect reporting results, and covers simple ways to sanity check and correct these situations.
Martin Thompson discusses the need to measure what’s going on at the hardware level in order to be able to create high performing lock-free algorithms.
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.