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I'm writing a library that has a few dependencies that are pulled in via CMake's FetchContent. In my toplevel CMakeLists.txt, close to the top I have:

include(external/dependencies.cmake) which contains

The following minimal benchmark rebuilds single-threaded code with -O3 -march=native on each machine, multiplying matrices that are either square or highly non-square (one dimension = 2).

When multiplying two data-mapped matrices (Eigen::Map) I notice a significant performance difference depending on how the memory was allocated. When using memory coming from a custom allocation, it's almost twice as fast compared to using (also aligned) memory coming from an std::vector with data allocated also by Eigen::aligned_allocator.

Minimal benchmark:

Embedded experts, is this possible without major modifications?

I'm building firmware that has both a Linux kernel and a minimal RTOS that runs when resuming from sleep. The same toolchain, aarch64-linux-gnu, is used for both Linux and RTOS code. The reason why it doesn't need a separate bare metal toolchain for RTOS is because the vendor has their own stripped down C runtime which they use instead of glibc.

But that poorly made C runtime is missing a lot of functions, so we want to use a more full featured one. We could use newlib but that would require a 2nd toolchain, which want to avoid.

But I can't find any bare metal or RTOS project using glibc. Currently, it can build with glibc, but crashes real soon, almost certainly because we didn't call the glibc initialization code:

I am working on improving my C++ skills - specifically the use of templates. I am creating a filtering mathematical library that through the use of templates is compatible for different vector/matrix classes including Boost and Eigen. So far I am very happy with the library, but am encountering issues as I am expanding functionality.

Eigen and Boost are different in that for example the latter does not have multiply operators (see 1) so this presented a choice in my implementation. I decided to make sure that the matrix/vector methods and operators used within the template library use those that are defined for Eigen so that at least one library works really well with the mathematical library. In my library I would use * to multiply matrices which is unavailable for boost. For boost I created a wrapper class for both matrix and vector to allow the use of * through custom operators. Example of wrapper for matrix is below:

Can I use pybind1 to pass a three-dimensional numpy array to a c++ function accepting an Eigen::Tensor as argument. For example, consider the following c++ function:

I've been successfully using cppyy for automatic python bindings for a C++ project I'm working on. I recently included the Eigen library, but I'm having trouble using this together with cppyy. Does anyone have any experience doing this, or know how I should do this?

I have the following structure for the repo (only relevant parts shown):

I write a function that takes two DenseBase as arguments.

The function uses .derived().array() to convert both Array and Matrix to Array.

I got tired of writing derived for many times and use auto.

But auto leads to strange error. Eigen complains that x2 and y2 don't have same shape.

If I don't want to write .derived().array() for many times, what can I use?

Eigen is from https://github.com/eigenteam/eigen-git-mirror.git

When going through the glibc code, a line is observed describing " Note that we do not reset the 'used' flag in the 'tid' field. This is done by the kernel" in glibc_source (version 2.21) (link to file in glibc source) at line 760.

As per my understanding glibc reuses the thread stack for T2 maintained in cache stack list. But before reusing that stack, it check for tid field in the thread descriptor of T1 (which is already reset to -1 after pthread_join of T1).