Fix bad grammar

Author: ningli

doc/shared_memory.md

@@ -11,7 +11,7 @@ This feature has been implemented within the communication library as a black bo
 - The first version is based on code supplied by David Tanqueray of Cray Inc., who initially applied this idea to several molecular dynamics applications. This code accesses platform-dependent information<a href="#note1" id="note1ref"><sup>1</sup></a> in order to establish the share-memory configurations (such as which MPI rank belongs to which node). It has been tested on Cray hardware only.
 -  The second version is based on the open-source package FreeIPC, created by Ian Bush, a former NAG colleague. FreeIPC is basically a Fortran wrapper for the System V IPC API and it provides a system-independent way to gather shared-memory information. This makes it possible to write more portable shared-memory code. 
 
-Fig. 1 below demonstrates the typical benefit of shared-memory programming. The data was collected on HECToR phase 2a system (Cray XT4 with quad-core AMD Opteron processors) from a series of simulations using 256 MPI ranks over a range of problem sizes. When the problem size is small (so is the message size), the communication routines were called more times so that the total amount of data moving within the system remains a constant. It can be seen that when the problem size is smaller, the overhead of setting up communications is much higher and the shared-memory code can improve communication efficiency by up to 30%. As the problem size increases, the benefit of using shared-memory code becomes smaller. In fact for large message size (> 32Kb in this example), the shared-memory code is actually slower due to the extra memory copying operations required to assemble/disassemble the shared-memory buffers. 
+Fig. 1 below demonstrates the typical benefit of shared-memory programming. The data was collected on HECToR phase 2a system (Cray XT4 with quad-core AMD Opteron processors) from a series of simulations using 256 MPI ranks over a range of problem sizes. When the problem size is small (so is the message size), the communication routines were called more times so that the total amount of data moving within the system remains a constant. It can be seen that when the problem size is smaller, the overhead of setting up communications is much higher and the shared-memory code can improve communication efficiency by up to 30%. As the problem size increases, the benefit of using shared-memory code becomes smaller. For large message size (> 32Kb in this example), the shared-memory code is actually slower due to the extra memory copying operations required to assemble/disassemble the shared-memory buffers. 
 
 <p align="center">
   <img src="images/shm1.png"><br>