cpp

Learning

cmu-db/15445-bootcamp: A basic introduction to coding in modern C++.

Blog posts

• C++ Papercuts :: The Coded Message; Aug 2023.

Opinion

C++ plus Effective Modern CMake plus Ninja plus boost is a good combo.

Performance

(Bilokon & Gunduz, 2023)

Design patterns and programming strategies will be used interchangeably throughout this work. As men- tioned earlier there are many ways to optimise C++ code to achieve lower latencies. The strategies that will be examined in this work are as follows.

• Cache Warming: To minimize memory access time and boost program responsiveness, data is preloaded into the CPU cache before it’s needed [50].
• Compile-time Dispatch: Through techniques like template specialization or function overloading, optimised code paths are chosen at compile time based on type or value, avoiding runtime dispatch and early optimisation decisions.
• Constexpr: Computations marked as constexpr are evaluated at compile time, enabling constant folding and efficient code execution by eliminating runtime calculations [46].
• Loop Unrolling: Loop statements are expanded during compilation to reduce loop control overhead and improve performance, especially for small loops with a known iteration count.
• Short-circuiting: Logical expressions cease evaluation when the final result is determined, reducing unnecessary computations and potentially improving performance.
• Signed vs Unsigned Comparisons: Ensuring consistent signedness in comparisons avoids conversion- related performance issues and maintains efficient code execution.
• Avoid Mixing Float and Doubles: Consistent use of float or double types in calculations prevents implicit type conversions, potential loss of precision, and slower execution.
• Branch Prediction/Reduction: Accurate prediction of conditional branch outcomes allows speculative code execution, reducing branch misprediction penalties and improving performance.
• Slowpath Removal: Optimisation technique aiming to minimize execution of rarely executed code paths, enhancing overall performance.
• SIMD: Single Instruction, Multiple Data (SIMD) allows a single instruction to operate on multiple data points simultaneously, significantly accelerating vector and matrix computations.
• Prefetching: Explicitly loading data into cache before it is needed can help in reducing data fetch delays, particularly in memory-bound applications.
• Lock-free Programming: Utilises atomic operations to achieve concurrency without the use of locks, thereby eliminating the overhead and potential deadlocks associated with lock-based synchronization.
• Inlining: Incorporates the body of a function at each point the function is called, reducing function call overhead and enabling further optimisation by the compiler.

Build

Ccache — Compiler cache speeds up recompilation by caching previous compilations and detecting when the same compilation is being done again.

Bilokon, P., & Gunduz, B. (2023). C++ Design Patterns for Low-Latency Applications Including High-Frequency Trading. https://arxiv.org/abs/2309.04259