coherence controller architectures for smp-based cc-numa multiprocessors
Scalable distributed shared-memory architectures rely on coherence controllers on each processing node to synthesize cache-coherent shared memory across the entire machine. The coherence controllers execute coherence protocol handlers that may be hardwired in custom hardware or programmed in a protocol processor within each coherence controller. Although custom hardware runs faster, a protocol processor allows the coherence protocol to be tailored to specific application needs and may shorten hardware development time. Previous research show that the increase in application execution time due to protocol processors over custom hardware is minimal.With the advent of SMP nodes and faster processors and networks, the tradeoff between custom hardware and protocol processors needs to be reexamined. This paper studies the performance of custom-hardware and protocol-processor-based coherence controllers in SMP-node-based CC-NUMA systems on applications from the SPLASH-2 suite. Using realistic parameters and detailed models of existing state-of-the-art system components, it shows that the occupancy of coherence controllers can limit the performance of applications with high communication requirements, where the execution time using protocol processors can be twice as long as using custom hardware.To gain a deeper understanding of the tradeoff, we investigate the effect of varying several architectural parameters that influence the communication characteristics of the applications and the underlying system on coherence controller performance. We identify measures of applications' communication requirements and their impact on the performance penalty of protocol processors, which can help system designers predict performance penalties for other applications. We also study the potential of improving the performance of hardware-based and protocol-processor-based coherence controllers by separating or duplicating critical components.
coherence control architecture processor process scalability shared memory custom hardware applications developer timing research minimal network paper performance systems model component communication requirements understandability effect influence measurement help predict critic art distributed shared memory cache