LUCID, the dataflow programming language
LUCID, the dataflow programming language
Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
Performance Measurement for Parallel and Distributed Programs: a Structured and Automatic Approach
IEEE Transactions on Software Engineering
Shared-memory performance profiling
PPOPP '97 Proceedings of the sixth ACM SIGPLAN symposium on Principles and practice of parallel programming
IPS-2: The Second Generation of a Parallel Program Measurement System
IEEE Transactions on Parallel and Distributed Systems
Adaptive Bidding Load Balancing Algorithms in Heterogeneous Distributed Systems
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Ant Colony Optimization
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition)
Performance technology for parallel and distributed component software: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Principles of Timing Anomalies in Superscalar Processors
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
Diagnosing distributed systems with self-propelled instrumentation
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Tools and strategies for debugging distributed stream processing applications
Software—Practice & Experience
HPCTOOLKIT: tools for performance analysis of optimized parallel programs http://hpctoolkit.org
Concurrency and Computation: Practice & Experience - Scalable Tools for High-End Computing
The Scalasca performance toolset architecture
Concurrency and Computation: Practice & Experience - Scalable Tools for High-End Computing
Message Driven Programming with S-Net: Methodology and Performance
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
| Hi-index | 0.00 |
Stream-based Coordination is a promising approach to execute programs on parallel hardware such as multi-core systems. It allows to reuse sequential code at component level and to extend such code with concurrency-handling at the coordination level. In this paper we identify the monitoring information required to enable the calculation of performance metrics, automatic load balancing, and bottleneck detection. The monitoring information is obtained by implicitly instrumenting multiple levels: the runtime system and the operating system. We evaluate the monitoring overhead caused by different use cases on S-Net as it is a challenging monitoring benchmark with a flexible and fully asynchronous execution model, including dynamic mapping and scheduling policies. The evaluation shows that in most cases the monitoring causes a negligible overhead of less than five percent.