LLIBS = -acc -gpu=cc70 -cudalib=cublas,cusolver,cufft,nccl -lblas -llapack CUDA_ROOT = /opt/cuda
# Minimal compilation pipeline tar -xzf vasp.5.4.4.tar.gz cd vasp.5.4.4 cp arch/makefile.include.linux_intel makefile.include # Edit makefile.include with your settings make veryclean make all ls -la bin/ This guide synthesizes knowledge from the official VASP manual, Intel HPC documentation, and community contributions on GitHub and research computing forums. Always cite VASP in your publications using the appropriate references as described in the official documentation. vasp 5.4.4 installation
help([[ VASP 5.4.4 compiled with Intel oneAPI 2023.0 ]]) local version = "5.4.4" local base = "/opt/vasp/5.4.4" prepend_path("PATH", pathJoin(base, "bin")) setenv("VASP_HOME", base) For those who need peak performance, consider these tweaks: 7.1 Profile-Guided Optimization (PGO) Compile once with profiling flags ( -prof-gen for Intel), run a short benchmark, then recompile with -prof-use . This can yield 5-10% speedups. 7.2 Cache Size Tuning Adjust -DCACHE_SIZE based on your CPU's L3 cache (in KB). For a typical Xeon with 30MB L3, use -DCACHE_SIZE=30000 . 7.3 Hybrid MPI+OpenMP Add -D_OPENMP and -qopenmp to FFLAGS, set OMP_NUM_THREADS=2 or 4 . Use with MPI processes to reduce memory bandwidth contention. 7.4 Linking Against CUDA-Aware MPI (for GPU builds) Add -DMPI_BLOCK=131072 for better GPU-MPI performance. Use NCCL (NVIDIA Collective Communications Library) by adding -DUSENCCL . Conclusion Installing VASP 5.4.4 is a rite of passage for computational materials scientists. While the process involves many moving parts – compilers, MPI, libraries, and architecture-specific flags – the result is a powerful simulation engine that can tackle problems ranging from catalytic surfaces to battery materials. This can yield 5-10% speedups
LLIBS = -lopenblas -lfftw3 -lscalapack -lmpi LLIBS = -L/usr/lib/x86_64-linux-gnu -lfftw3 -lopenblas -lscalapack use -DCACHE_SIZE=30000 .