Cache-aware matrix multiplication on multicore systems for IPM-based LP solvers

We profile GLPK, an open source linear programming solver, and show empirically that the form of matrix multiplication used in interior point methods takes a significant portion of the total execution time when solving some of the Netlib and other LP data sets. Then, we discuss the drawbacks of the matrix multiplication algorithm used in GLPK in terms of cache utilization and use blocking to develop two cache-aware implementations. We apply OpenMP to develop parallel implementations with load balancing. The best implementation achieved a median speedup of 21.9 when executed on a 12-core AMD Opteron.