Frequent value locality and its applications

Abstract

By analyzing the behavior of a set of benchmarks, we demonstrate that a small number of distinct values tend to occur very frequently in memory. On an average, only eight of these frequent values were found to occupy 48% of memory locations for the benchmarks studied. In addition, we demonstrate that the identity of frequent values remains stable over the entire execution of the program and these values are scattered fairly uniformly across the allocated memory. We present three different algorithms for finding frequent values and experimentally demonstrate their effectiveness. Each of these algorithms is designed to suit a different application scenario. Since the contents of memory exhibit frequent value locality, it is expected that frequent values will be observed in data streams that flow across different points in the memory hierarchy. We exploit this observation for developing two low-power designs: a low-power level-one data cache and a low-power external data bus. In each of these applications a different form of encoding of frequent values is employed to obtain a low-power design. We also experimentally demonstrate the effectiveness of these designs.

A Case Study of a Navigator Optimization Process

“…It is a phenomenon that occurs due to the characteristic of searching a position obtained by receiving various index values from various satellites. This phenomenon is called value locality [5]. In this case, it is possible to compress the entire data size by separating the address and value tables.…”

Dynamic Dictionary-Based Data Compression for Level-1 Caches

“…L2/L3 compression is therefore a useful technique for the foreseeable future and especially for future CMP architectures. Alternatively, compression can be used to free space in the cache which can then be translated to power savings [13,22,23,25].…”

“…One such simple, yet effective, mechanism is the dictionary (or directory) for frequent values [20,23,25,26]. A frequent-value dictionary stores the program's frequent values (e.g., the 32-bit value "0") and replaces all their occurrences in the cache with the respective dictionary indices (e.g., 8-bit indices for a 256-entry dictionary).…”

“…A frequent-value dictionary stores the program's frequent values (e.g., the 32-bit value "0") and replaces all their occurrences in the cache with the respective dictionary indices (e.g., 8-bit indices for a 256-entry dictionary). However, until now, no mechanism has been proposed to implement a dynamic dictionary for caches [23,24]; in other words, a dictionary whose contents can adapt to the requirements of the running program. The dictionaries proposed so far for caches are loaded statically via profiling or are "trained" for a small period of time to detect and store frequent values for the remainder of a program's run [23,25,26].…”

“…However, until now, no mechanism has been proposed to implement a dynamic dictionary for caches [23,24]; in other words, a dictionary whose contents can adapt to the requirements of the running program. The dictionaries proposed so far for caches are loaded statically via profiling or are "trained" for a small period of time to detect and store frequent values for the remainder of a program's run [23,25,26]. In practice, such a static approach is avoided by designers since it is cumbersome in real systems.…”