README.md

basic_iterative_solvers

This is a set of (preconditioned) iterative solvers for Ax = b, where A is typically assumed to be a large sparse matrix.

Usage Examples

./basic_iterative_solvers HPCG-128.mtx -cg

./basic_iterative_solvers Anderson,Lx=100,Ly=100,Lz=50,ranpot=5.0 -gm -p gs

Building basic_iterative_solvers

git clone [email protected]:DanecLacey/basic_iterative_solvers.git
cd basic_iterative_solvers
make

• All user-defined solver parameters and configuration options can be found in config.mk

Features

• Stacked timers around key code regions
• Optional code instrumentation with likwid markers for collecting hardware performance counters already built-in
• Matrices can be read from .mtx files, or generated with SCAMAC library
• Kernel-specific benchmarking found in benchmarks/
  • To use, simply cd benchmarks and make && ./run_benchmarks <your_matrix>
  • Users can toggle performance and accuracy validation against Intel MKL with BENCH_MKL=1 make

Supported Solvers

• Richardson -r
• Jacobi -j
• (Forward/Symmetric)Gauss-Seidel -gs / -sgs
• (Preconditioned) Conjugate-Gradient -cg
• (Preconditioned) (Restarted) GMRES -gm
• (Preconditioned) BiCGSTAB -bi

Supported Preconditioners

• Jacobi -p j
• (Forward/Backward/Symmetric/FFBB) Gauss-Seidel -p (gs/bgs/sgs/ffbbgs)

Notes

• The sparse matrix storage format of A is CRS.
• Only left-preconditioning is implemented.
• This code is mainly for performance investigations.