During the past few years, algorithmic improvements alone have shaved almost an order of magnitude off the time required for the direct solution of general sparse systems of linear equations. Combined with a similar increase in the performance to cost ratio due to hardware advances during this period, current sparse solver technology makes it possible to solve those problems quickly and easily that might have been considered impractically large until recently. In this paper, we compare the performance of some commonly used software packages for solving general sparse systems. In particular, we demonstrate the consistently high level of performance achieved by WSMP—the most recent of such solvers. We compare the various algorithmic components of these solvers and show that the choices made in WSMP enable it to run two to three times faster than the best amongst other similar solvers. As a result, WSMP can factor some of the largest sparse matrices available from real applications in a few seconds on 4-CPU workstation.