How to improve ML algorithms or rewrite optimised multithreading for ARM?

I am trying to understand how can we rewrite optimized multithreading for ARM architecture. Any suggestions will be of great help.

  • Hi there, I have moved your question to the AI & ML forum. Many thanks.

  • the effectiveness of optimizations can vary depending on the specific ARM architecture and processor you're targeting. It's essential to understand the architecture's features and leverage them effectively to achieve the best performance for your multithreaded code.

  • Hello,
    Rewriting optimized multithreading for ARM architecture can be a challenging task, as it requires a good understanding of the features and capabilities of the specific ARM processor you are targeting, as well as the characteristics and requirements of your ML algorithm. However, there are some general strategies and resources that can help you improve your ML algorithms or rewrite optimized multithreading for ARM. Here are some suggestions:

    Compare multiple algorithms: Different ML algorithms may have different performance and accuracy trade-offs on different ARM architectures. You can try to compare different algorithms for your ML task, such as logistic regression, support vector machine, XGBoost, neural network, etc. and find the one that suits your needs and constraints best. You can use some tools or frameworks that support multiple ML algorithms, such as scikit-learn, TensorFlow Lite, or PyTorch Mobile.

    Tune model parameters: Model parameters are the settings that control the behavior and performance of your ML algorithm, such as the learning rate, the number of iterations, the regularization factor, etc. You can try to tune these parameters to find the optimal values that maximize the accuracy and efficiency of your ML algorithm on your ARM architecture. You can use some tools or frameworks that support automatic or manual parameter tuning, such as Optuna, Ray Tune, or scikit-optimize. DogLikesBest

    Improve data quality: Data quality is an important factor that affects the performance and accuracy of your ML algorithm. You can try to improve the quality of your data by applying some techniques, such as data cleaning, data augmentation, data normalization, feature engineering, etc. You can use some tools or frameworks that support data processing and manipulation, such as pandas, [NumPy], or [OpenCV].

    Optimize computation kernels: Computation kernels are the low-level functions that perform the basic operations of your ML algorithm, such as matrix multiplication, convolution, activation, etc. You can try to optimize these kernels for better performance and smaller memory footprint on your ARM architecture. You can use some tools or frameworks that support optimized computation kernels for ARM, such as [CMSIS-NN], [ARM NN], or [ARM Compute Library].

    I hope these suggestions will help you improve your ML algorithms or rewrite optimized multithreading for ARM

  • Rewriting and optimizing multithreaded code for the ARM architecture involves a series of strategies and steps. Here are some suggestions to help you with this process:

    Learn about ARM architecture features:
    In-depth study of the instruction set, registers, memory model, etc. of the ARM architecture.
    Understand the multi-core features and memory consistency model of the ARM architecture.
    Research specific optimization techniques for ARM architecture, such as SIMD (Single Instruction Multiple Data) instructions, cache optimization, etc.
    Analyze existing code:
    Use performance analysis tools such as perf, Valgrind's Cachegrind tool, etc. to identify bottlenecks in existing code.
    Determine which parts are multi-threading relevant and evaluate their performance.
    Thread synchronization optimization:
    Minimize synchronization overhead between threads, use lock-free data structures or fine-grained locks.
    Avoid using global locks and consider using read-write locks or other more fine-grained synchronization mechanisms.
    Evaluate and optimize the use of atomic operations, especially in high-frequency access scenarios.
    Task division and load balancing:
    According to the multi-core characteristics of the ARM architecture, tasks are reasonably divided into different cores.
    Implement load balancing to ensure that the workload on each core is relatively even.
    Take advantage of ARM hardware features:
    Leverage ARM's SIMD instruction set to accelerate data parallel processing.
    Consider using ARM's NEON technology or other acceleration libraries (such as OpenCL, Vulkan, etc.).
    Optimize memory access patterns to reduce cache misses and memory latencies.
    Multi-threaded programming model:
    Choose an appropriate multi-threaded programming model, such as POSIX threads (pthreads), C++11's std::thread, or other parallel computing frameworks.
    Evaluate and select the most appropriate parallel mode (such as task parallelism, data parallelism, etc.) based on application requirements.
    Compiler optimization:
    Use a compiler optimized for the ARM architecture and enable advanced optimization options.
    Evaluate the effectiveness of optimization strategies such as compiler automatic vectorization and loop unrolling.
    Code refactoring and simplification:
    Simplify code logic and reduce conditional branches and complex data structures.
    Simplify memory management using modern C++ features like smart pointers, RAII, and more.
    Test and tune:
    Use benchmarking and performance analysis to evaluate optimization effects.
    Keep iterating and optimizing until you reach satisfactory performance goals.
    Consider platform features:
    There are many different implementations of the ARM architecture, such as ARMv7, ARMv8 (including AArch32 and AArch64), etc. Make sure your optimization strategy is suitable for the target platform.
    Consider hardware-specific optimizations, such as leveraging ARM's large page table (LPAE) feature to reduce page table lookup overhead.

    You can get more help from Baudcom's blog or e1-converter's blog.