Recently there has been a lot of effort in providing cost-effective Shared Memory systems by employing software only solutions on clusters of high-end workstations coupled with high-bandwidth, low-latency commodity networks. Much of the work so far has focused on improving protocols, and there has been some work on restructuring applications to perform better on SVM systems. The result of this progress has been the promise for good performance on a range of applications at least in the 16-32 processor range. New system area networks and network interfaces provide significantly lower overhead, lower latency and higher bandwidth communication in clusters, inexpensive SMPs have become common as the nodes of these clusters, and SVM protocols are now quite mature. With this progress, it is now useful to examine what are the important system bottlenecks that stand in the way of effective parallel performance; in particular, which parameters of the communication architecture are most important to improve further relative to processor speed, which ones are already adequate on modern systems for most applications, and how will this change with technology in the future. Such information can assist system designers in determining where to focus their energies in improving performance, and users in determining what system characteristics are appropriate for their applications. We find that the most important system cost to improve is the overhead of generating and delivering interrupts. Improving network interface (and I/O bus) bandwidth relative to processor speed helps some bandwidth-bound applications, but currently available ratios of bandwidth to processor speed are already adequate for many others. Surprisingly, neither the processor overhead for handling messages nor the occupancy of the communication interface in preparing and pushing packets through the network appear to require much improvement.
['Angelos Bilas', 'Jaswinder Pal Singh']
The Effects of Communication Parameters on End Performance of Shared Virtual Memory Clusters