simd-booster: Boost Your C++ Code with SIMD-Powered Optimizations
simd-booster is a repository dedicated to optimizing and accelerating sluggish C++ code by leveraging SIMD (Single Instruction, Multiple Data) operations. SIMD enables parallel computation by applying the same operation to multiple data elements of the same type simultaneously, resulting in enhanced performance.
simd-booster provides a collection of examples and alternatives that optimize commonly inefficient code sections using SIMD. The repository covers a wide range of operations such as character tests, string manipulation, data processing, and loop operations, all optimized with SIMD to deliver faster execution.
Key features of simd-booster include:
- Accelerating sluggish code and enhancing performance.
- Harnessing SIMD operations for data parallelism.
- Optimizing common operations and algorithms with SIMD.
- Showcasing examples of fast and efficient C++ code.
In the simd-booster repository, you can find SIMD-optimized alternatives for common operations like isspace. simd-booster serves as a valuable resource for writing high-performance C++ code and accelerating existing codebases.
By leveraging simd-booster, you can optimize your C++ projects and execute previously sluggish code more efficiently. The repository provides the necessary tools to harness SIMD operations and unlock the potential of high-performance C++ code.
Boost your C++ applications with simd-booster, transforming slow and inefficient code into fast and powerful SIMD-enabled implementations.
Functions Included in simd-booster Repository
simd-booster repository contains various functions optimized with SIMD (Single Instruction, Multiple Data) operations. These functions can accelerate and optimize different operations and algorithms. Some of the functions that can be included are:
- SIMD-powered character tests (e.g.,
isspace,isalpha,isdigit) - SIMD-accelerated string operations (e.g.,
strcmp,strcpy,strlen) - SIMD-based data manipulation (e.g., vector operations, bit manipulation)
- SIMD-optimized loop operations (e.g., vectorized loops, parallel computations)
- SIMD-powered mathematical operations (e.g., vector operations, matrix computations)
- SIMD-accelerated data processing and filtering functions (e.g., data compression, image processing)
This is just a sample list, and the simd-booster repository can include many other functions that can be optimized with SIMD. Depending on your specific needs and project requirements, you can identify the operations that can benefit from SIMD optimization and find their accelerated versions in the simd-booster repository.