How to improve ML algorithms or rewrite optimised 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.

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.