Data networks
IEEE Transactions on Parallel and Distributed Systems
Effects of communication latency, overhead, and bandwidth in a cluster architecture
Proceedings of the 24th annual international symposium on Computer architecture
Critical Path Profiling of Message Passing and Shared-Memory Programs
IEEE Transactions on Parallel and Distributed Systems
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Fast Messages: Efficient, Portable Communication for Workstation Clusters and MPPs
IEEE Parallel & Distributed Technology: Systems & Technology
IEEE Transactions on Parallel and Distributed Systems
Priority Based Messaging for Software Distributed Shared Memory
Cluster Computing
CPR: Mixed Task and Data Parallel Scheduling for Distributed Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Ultra-high performance communication with MPI and the Sun fire™ link interconnect
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Dynamic Key Messaging for Cluster Computing
NCA '04 Proceedings of the Network Computing and Applications, Third IEEE International Symposium
Key Messaging on SOME-Bus clusters
Parallel Computing
Communication Contention in Task Scheduling
IEEE Transactions on Parallel and Distributed Systems
Design and Evaluation of Nemesis, a Scalable, Low-Latency, Message-Passing Communication Subsystem
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
FAST TCP: motivation, architecture, algorithms, performance
IEEE/ACM Transactions on Networking (TON)
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The Key Message (KM) approach prioritizes communications along the critical path to speed up the execution of parallel applications in a cluster environment. However, the current KM approaches (i.e, the Static KM (SKM) and the dynamic KM with a central predictor (CDKM)) either lack the capability of adapting to dynamic environment or suffer from the expensive centralized control to predict dynamic critical path rendering them to be suboptimal. In this paper, we introduce a dynamic and distributed KM algorithm (DDKM) that takes this dynamic nature into account while avoiding overheads of central control, thus, overcoming the drawbacks of its predecessors. To evaluate the DDKM algorithm, we implement it and simulate it in a cluster environment whose communication network (e.g., shared bus) can be modeled as an M/D/1. We compare DDKM with current KM approaches. The performance of DDKM is close to that of CDKM, and much better than SKM up to 28.9% when the network is highly congested. These results demonstrate that DDKM is a promising optimization in a real cluster environment.