Parallel programming models and paradigms are increasingly becoming more expressive with a steady increase in the number of cores that can be placed on a single chip. Concurrent data structures for shared memory parallel pro- grams are now being used in operating systems, middle-ware, and device drivers. In such a shared memory model, processes communicate and synchronize by applying primitive operations on memory words. To implement concurrent data structures that are linearizable and possibly lock-free or wait-free, it is often necessary to add additional information to memory words in a data structure. This additional information can range from a single bit to multiple bits that typically represent thread ids, request ids, timestamps, and other application dependent fields. Since most processors can perform compare-And-Set (CAS) or load-link/store-conditional (LL/SC) operations on only 64 bits at a time, current approaches either use some bits in a memory word to pack additional information (packing), or use the bits to store a pointer to an object that contains additional information (redirection), and the original data item. The former approach restricts the number of bits for each additional field and this reduces the range of the field, and the latter approach is wasteful in terms of space. We propose a novel and universal method called a memory word expander in this paper. It caches information for a set of memory locations that need to be augmented with additional information. It supports traditional atomic get, set, and CAS operations, and tries to maintain state for a minimum number of entries. We experimentally demonstrate that it is possible to reduce the runtime memory footprint by 20-35% for algorithms that use redirection. For algorithms that use packing, the use of the EXPANDER can make them feasible. The performance overhead is within 2-13% for 32 threads. When we compare the performance of the EXPANDER based non-blocking algorithms with the version that uses locks, we have a performance gain of at least 10-100X.