HPCN Europe 2000 Proceedings of the 8th International Conference on High-Performance Computing and Networking
Multilayer Online-Monitoring for Hybrid DSM Systems on Top of PC Clusters with a SMiLE
TOOLS '00 Proceedings of the 11th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
A proposal for a new hardware cache monitoring architecture
Proceedings of the 2002 workshop on Memory system performance
Monitoring concepts for parallel systems: an evolution towards interoperable tool environments
On-line monitoring systems and computer tool interoperability
Owl: next generation system monitoring
Proceedings of the 2nd conference on Computing frontiers
Monitoring cache behavior on parallel SMP architectures and related programming tools
Future Generation Computer Systems
Memory access behavior analysis of NUMA-based shared memory programs
Scientific Programming
Core monitors: monitoring performance in multicore processors
Proceedings of the 6th ACM conference on Computing frontiers
Monitoring cache behavior on parallel SMP architectures and related programming tools
Future Generation Computer Systems
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Modern low-latency and high-bandwidth interconnects like the Scalable Coherent Interface (SCI) deliver high communication performance for parallel and distributed systems. However, the performance of an SCI-based compute cluster with NUMA characteristics depends on the efficient use of local memory accesses. Therefore, programming and tool environments for such systems with distributed shared memory (DSM) should enable and exploit data locality.In this paper, we present an event-driven hybrid monitoring approach for a SCI-based PC cluster with hardware-supported DSM. The core of that concept is a hardware monitor which is able to observe the fine-grained nature of the communication in such a parallel system with minimal impact to the system. The hardware monitor delivers in real-time detailed information about the communication and runtime of an examined program. The monitoring system allows the user an optimal evaluation of the network behavior and hence of the data locality of that parallel program.