Andrew Drakeford

  • Director/UBS

Physics PhD who started out at BT Labs working with AI and acting as a design consultant for large scale systems in network management and billing. He has spent the last few decades designing and writing high performance quantitative libraries and applications in C++. He is interested in quant finance, machine learning, vectorisation, SIMD and HPC. He is a member of the UK C++ panel.

Sessions

  • Data Oriented Design

    Intermediate
    Advanced
    Design

    Data-oriented design (DOD) has gained traction for its practical approach to solving real-world problems. Refactoring data into an Entity-Component format sets the stage for cleaner, more efficient implementations—where lambdas process ranges and SIMD operations exploit the power of contiguous data layouts. DOD is widely recognized for addressing hardware-related performance challenges, but where do these challenges originate? The engineers behind our hardware platforms didn’t design performance bottlenecks; they created performance opportunities. By engineering how our code interacts with hardware, we can unlock these opportunities and achieve substantial speed-ups. The key lies in understanding and exploiting the state of the system. From […]

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