While minimizing the memory requirements of operating systems for standard computers has been important, it has been absolutely essential for small, inexpensive, specialized devices such as personal digital assistants (PDAs), "smart" cellular telephones, portable devices for listening to compressed music files, and Internet kiosks.
Exploiting bitmaps not only minimizes the memory consumption, but also reduces CPU costs.
For instance, minimizing the memory distance between two functions that frequently communicate reduces the energy consumption of target application [32].
Matching information of already computed patterns is materialized as bitmaps, a technique that not only minimizes the memory consumption, but also the CPU time.
To solve this problem, research [12] has often proposed a data reuse alternative with the aim of minimizing the memory traffic between GPU and CPU.
We also propose a rule categorization algorithm that partitions an input ruleset by considering the field features of rules to minimize the memory requirement.
For example, many previous studies focusing on minimizing the memory usage have developed on-disk indexing schemes, leading to lower performance.
However, the 'S'-shaped strategy seems to us to be followed to minimize the memory load of the task at the same time as maximizing the probability of an exhaustive search (Gilchrist et al., 2001).
Minimizing the memory use per particle has the nice side benefit of increasing performance in the tree building and tree walking phases of the code that are strongly affected by the efficiency of transferring to and from memory.
In [30], a hardware accelerator for image reconstruction in digital holographic imaging is presented that focuses to maximize the computational efficiency and minimize the memory transfer overhead to the external SDRAM.
