Index compression has been a major issue in the field of Information Retrieval Systems. In particular, due to the impressive figures involved with Web Search Engines (WSEs) the compression of the index is not an option anymore but it has become a must. The most important index compression methods are designed to work for Inverted File (IF) indexes. These methods are based on the assumption that the posting lists are stored as sequences of d gaps (i.e. differences among successive document identifiers). The compression is thus carried out by using variable length encoding methods which represents smaller number using a smaller number of bits. In this paper, instead of focusing on finding a novel encoding method, we propose an algorithm which allows the assignment of identifiers to documents in a way that minimizes the average values of d gaps. The simulations performed on a real dataset, i.e. the Google contest collection, show that our approach allows to obtain an IF index which is, depending on the d gap encoding chosen, up to 23% smaller than the one built over randomly assigned document identifiers. Moreover, we will show, both analytically and empirically, that the complexity of our algorithm is linear in space and time.

A register file is a critical component of a modern superscalar processor. It has a large number of entries and read/write ports in order to enable high levels of instruction parallelism. As a result, the register file’s area, access time, and energy consumption increase dramatically, significantly affecting the overall superscalar processor’s performance and energy consumption. This is especially true in 64-bit processors. This paper presents a new integer register file organization, which reduces energy consumption, area, and access time of the register file with a minimal effect on overall IPC. This is accomplished by exploiting a new concept, partial value locality, which is defined as occurrence of multiple live value

During a program's runtime, the stack and data segments of the main memory often contain much redundancy, which makes them good candidates for compression. Compression and decompression however require either extra hardware or substantial processing resources. This paper presents a new approach in which a mostly software solution is suggested but without the processing power penalty that usually accompanies such a solution. This is achieved by not compressing all the memory all the time.