InfoQ Homepage Performance Content on InfoQ
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Performance Mythbusting Panel
The panelists answer audience questions on real-world applied performance proofs across stacks including Java, .NET and Python.
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Control Flow Integrity Using Hardware Counters
J. Butler and C. Pierce present a system for early detection and prevention of unknown exploits. Their system uses Performance Monitoring Unit hardware to enforce coarse-grained Control Flow Integrity
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Scale @Reddit Triple Team Size w/o Losing Control
Nick Caldwell discusses his engineering team's approach to Agile development as they scaled from 40 to 120 engineers.
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The Anatomy of a Distributed System
Tyler McMullen talks through the components and design of a real system, built to perform very high volumes of health checks, done across a cluster of machines for reliability and scalability.
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Three Baseline Metrics
Mike Burns outlines three metrics -cycle time, throughput, and work item size- a team can use to help improve team performance, and allow for the right decisions to be made at the right time.
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Code Archaeology
David Mitchell shows how to create visualizations out of code: building a map for a large, legacy code base, creating visuals without drawing, and explaining a roadmap to bring code under control.
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Java at Speed: Getting the Most out of Modern Hardware
Gil Tene discusses some of the optimizations and capabilities that the latest crop of JVMs are able to apply when running on the latest servers, and performance issues with financial applications.
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Better: Fearless Feedback for Software Teams
Erika Carlson introduces effective feedback, with strategies for giving, receiving, and processing feedback, and the challenges and rewards of using feedback as a tool to improve team performance.
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Wobserver: Easy to Integrate Monitoring and Debugging
Ian Luites introduces wobserver, discussing the background of the project and showing how to mount it into a Phoenix application, hook it up to Prometheus, and deploy it behind a load balancer.
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Refactoring Elixir - Lessons Learned from a Year on Exercism.Io
Devon Estes discusses some common, but less than optimal, solutions to some of the problems on exercism.io followed by refactoring, showing the performance improvements and tradeoffs made.
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Deep Learning @Google Scale: Smart Reply in Inbox
Anjuli Kannan describes the algorithmic, scaling, deployment considerations involved in a an application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox
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Enabling High Performance Real-time Analytics for IoT Environments
Mahish Singh discusses how to use methodologies during design, development, deployment and operation for delivery of analytics platforms which offer real-time SLAs.
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