The shared memory concept makes it easier to write parallel programs, but tuning the application to reduce the impact of frequent long latency memory accesses still requires substantial programmer effort. Researchers have proposed using compilers, operating systems, or architectures to improve performance by allocating data close to the processors that use it. The Cache-Only Memory Architecture (COMA) increases the chances of data being available locally because the hardware transparently replicates the data and migrates it to the memory module of the node that is currently accessing it. Each memory module acts as a huge cache memory in which each block has a tag with the address and the state. The authors explain the functionality, architecture, performance, and complexity of COMA systems. They also outline different COMA designs, compare COMA to traditional nonuniform memory access (NUMA) systems, and describe proposed improvements in NUMA systems that target the same performance obstacles as COMA.