Partitioning and Mapping Algorithms into Fixed Size Systolic Arrays
IEEE Transactions on Computers
Limits to low-latency communication on high-speed networks
ACM Transactions on Computer Systems (TOCS)
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
IEEE Transactions on Parallel and Distributed Systems
Critical Path Profiling of Message Passing and Shared-Memory Programs
IEEE Transactions on Parallel and Distributed Systems
Advanced Computer Architecture: Parallelism,Scalability,Programmability
Advanced Computer Architecture: Parallelism,Scalability,Programmability
Fast Messages: Efficient, Portable Communication for Workstation Clusters and MPPs
IEEE Parallel & Distributed Technology: Systems & Technology
A Case for NOW (Networks of Workstations)
IEEE Micro
IPS-2: The Second Generation of a Parallel Program Measurement System
IEEE Transactions on Parallel and Distributed Systems
Priority Based Messaging for Software Distributed Shared Memory
Cluster Computing
Ultra-high performance communication with MPI and the Sun fire™ link interconnect
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Active messages: an efficient communication architecture for multiprocessors
Active messages: an efficient communication architecture for multiprocessors
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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. In this article, we introduce a family of three dynamic KM algorithms that take into account of the dynamic nature of underlying networks to improve over their predecessor. These three dynamic KM algorithms are: Fixed Critical Path Dynamic KM (FDKM), Centralized Predictor Dynamic KM (CDKM), and Distributed Dynamic KM (DDKM). Our performance study based on simulation showed that in general CDKM performs better and is more stable than FDKM and DDKM under heavy traffic conditions. The performance of DDKM is close to that of CDKM and better than that of FDKM on average. By taking advantages of both FDKM and CDKM, DDKM can be expected to be a promising optimization in a real cluster environment.