Accelerating Cross-correlation Applications via Parallel Computing

Software dealing with large-scale signal processing takes long time even on modern hardware. Cross -correlation applicat ions are mostly algorithms rather than data-intensive (that is, they are more CPU- bound than I/O-bound). Parallel imp lementation of the cross-correlation execution over the local network, or in some cases over a Wide Area Net work (WAN), helps reducing the processing time. The aim of this paper is to discuss the possibility of distributing the cross- correlation co mputational process over the available PCs in the local network. Moreover, the algorithm portion that is sent to a remote PC, within the LA N, will be redistributed over the available CPU cores on that computer yield ing to maximu m utilizat ion of all available cores in the local area network. The load balancing problem will be addressed as well.