Task-Based GPU-Enabled Distributed Eigensolver available in DLA-Future

DLA-Future implements an efficient GPU-enabled distributed eigenvalue solver using asynchronous methods based on the proposed C++ std::execution API. Using a task-based approach reduces the number of synchronization points and allows for simple overlapping of communication and computation which helps improve performance relative to fork-join parallelism techniques as found in other libraries such as LAPACK and ScaLAPACK. In certain cases when multiple algorithms with suitable problem sizes are run independently, they can be co-scheduled to run at the same time producing noticeable improvements in time to solution. We present results of our task-based generalized eigensolver and show the current optimization status on GPU-enabled systems (including both Nvidia and AMD devices). We also present full application results generated with CP2K and SIRIUS, where DLA-Future support was easily added thanks to the C-API provided, which is compatible with the widely used ScaLAPACK interface.