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GPU Eigensolver for Quantum ESPRESSO package

This library implements a generalized eigensolver for symmetric/hermetian-definite eigenproblems with functionality similar to the DSYGVD/X or ZHEGVD/X functions available within LAPACK/MAGMA. This solver has less dependencies on CPU computation than comparable implementations within MAGMA, which may be of benefit to systems with limited CPU resources or to users without access to high-performing CPU LAPACK libraries.

This implementation can be considered as a "proof of concept" and has been written to target the Quantum ESPRESSO code. As such, this implementation is built only to handle one problem configuration of DSYGVD/X or ZHEGVD/X. Specifically, this solver computes eigenvalues and associated eigenvectors over a specified integer range for a symmetric/hermetian-definite eigenproblem in the following form:

A * x = lambda * B * x

where A and B are symmetric/hermetian-matrices and B is positive definite. The solver expects the upper-triangular parts of the input A and B arguments to be populated. This configuration corresponds to calling DSYGVX/ZHEGVX within LAPACK with the configuration arguments ITYPE = 1, JOBZ = 'V', RANGE = 'I', and UPLO = 'U'.

See comments within dsygvdx_gpu.F90 or zhegvdx_gpu.F90 for specific details on usage.

For additional information about the solver with some performance results, see presentation at the following link: (will be added once available publically on the GTC On-Demand website)

• Compilation of this library requires the PGI compiler version 17.4 or higher.
• Using the provided Makefile will generate a static library object lib_eigsolve.a which can included in your target application.
• Library requires linking to cuBLAS and cuSOLVER. Use -Mcuda=cublas,cusolver flag when linking your application to do this.
• This library also requires linking to a CPU LAPACK library with an implementation of the zstedc function.
• If NVTX is enabled with -DUSE_NVTX flag, also must link to NVTX. Use -L${CUDAROOT}/lib64 -lnvToolsExt flag when linking your application to do this where ${CUDAROOT} is the root directory of your CUDA installation.

An example of using this solver in a program can be found in the test_driver subdirectory. This program does a little performance testing and validation against existing functionality in a linked CPU LAPACK library, cuSOLVER, and MAGMA (if available).

This code is released under an MIT license which can be found in LICENSE.