Scientific applications like weather forecasting require high performance and fast response time. But this ideal requirement has always been constrained by peculiarities of underlying platforms specially distributed platforms. One such constraint is the efficiency of communication between geographically dispersed and physically distributed processes running these applications, that is the efficiency of inter process communication (IPC) mechanisms. This paper provides hard evidence that an operating system kernel-level implementation of IPC on multi-computers reduces the execution time of a weather forecasting model by nearly half on average compared to when the IPC mechanism is implemented at library level. A well known non-hydrostatic version of the Penn state/NCAR mesoscale model, called MM5, is executed on a networked cluster. The performance of MM5 is measured with two distributed implementations of IPC, a kernel-level implementation called DIPC2006 and a renowned library level implementation called MPI. It is both shown how and argued why the performance of MM5 on a DIPC2006 configured cluster is by far better than its performance on an MPI configured similar cluster. Even ignoring the favorable points of kernel-level implementations, like safety, privilege, reliability, and primitiveness, the insight is twofold. Scientist may look for more efficient distributed implementations of IPC to run their simulations faster, and computer engineers may try harder to develop more efficient distributed implementations of IPC for scientists.

Parallel programming and distributed programming are two solutions for scientific applications to provide high performance and fast response time in parallel systems and distributed systems. Parallel and distributed systems must provide inter process communication (IPC) mechanisms like message passing mechanism as underlying platforms to enable communication between local and especially geographically dispersed and physically distributed processes. Communication overhead is the major problem in these systems and there are a lot of efforts to develop more efficient message passing mechanisms or to improve the network communication speed. This paper provides hard evidence that an efficient implementation of message passing mechanism on multi-computers reduces the execution time of a molecular dynamics code. A well-known program for macromolecular dynamics and mechanics called CHARMm is executed on a networked cluster. The performance of CHARMm is measured with two distributed implementations of message passing, namely a kernel-level implementation called DIPC2006 and a renowned library level implementation called MPI. It is shown that the performance of CHARMm on a DIPC2006 configured cluster is by far better than its performance on an optimized MPI configured similar cluster. Even ignoring the favorable points of kernel-level implementations, like safety, privilege, reliability, and primitiveness, the insight is twofold. Scientists are nowadays faced with more computational complexity and look for more efficient systems and mechanisms. Efficient distributed IPC mechanisms have direct effect on running scientistspsila simulations faster, and computer engineers may try harder to develop more efficient distributed implementations of IPC.