Semi-static Conditions in Low-latency C++ for High Frequency Trading: Better than Branch Prediction Hints

TL;DR

This paper introduces semi-static conditions in C++ that allow dynamic modification of branch directions at runtime, outperforming traditional branch prediction hints in low-latency, high-frequency trading scenarios.

Contribution

It presents a novel language construct enabling runtime branch direction changes by modifying assembly code, surpassing existing compiler hints like [[likely]] and [[unlikely]].

Findings

Semi-static conditions outperform traditional branch prediction hints in specific scenarios.

The approach enhances performance in high-frequency trading applications.

Considerations of portability, syntax, and security were addressed during development.

Abstract

Conditional branches pose a challenge for code optimisation, particularly in low latency settings. For better performance, processors leverage dedicated hardware to predict the outcome of a branch and execute the following instructions speculatively, a powerful optimisation. Modern branch predictors employ sophisticated algorithms and heuristics that utilise historical data and patterns to make predictions, and often, are extremely effective at doing so. Consequently, programmers may inadvertently underestimate the cost of misprediction when benchmarking code with synthetic data that is either too short or too predictable. While eliminating branches may not always be feasible, C++20 introduced the [[likely]] and [[unlikely]] attributes that enable the compiler to perform spot optimisations on assembly code associated with likely execution paths. Can we do better than this? This work presents the development of a novel language construct, referred to as a semi-static condition, which enables programmers to dynamically modify the direction of a branch at run-time by modifying the assembly code within the underlying executable. Subsequently, we explore scenarios where the use of semi-static conditions outperforms traditional conditional branching, highlighting their potential applications in real-time machine learning and high-frequency trading. Throughout the development process, key considerations of performance, portability, syntax, and security were taken into account.