OpenMP: An Industry-Standard API for Shared-Memory Programming
IEEE Computational Science & Engineering
LCR '00 Selected Papers from the 5th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
An infrastructure for adaptive dynamic optimization
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Data Centric Cache Measurement on the Intel ltanium 2 Processor
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
An Event-Driven Multithreaded Dynamic Optimization Framework
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Hardware profile-guided automatic page placement for ccNUMA systems
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
COBRA: An Adaptive Runtime Binary Optimization Framework for Multithreaded Applications
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
Capturing performance knowledge for automated analysis
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Open Source Software Support for the OpenMP Runtime API for Profiling
ICPPW '09 Proceedings of the 2009 International Conference on Parallel Processing Workshops
Memory Affinity for Hierarchical Shared Memory Multiprocessors
SBAC-PAD '09 Proceedings of the 2009 21st International Symposium on Computer Architecture and High Performance Computing
Hi-index | 0.00 |
Developing shared memory parallel programs using OpenMP is straightforward, but getting good performance in terms of speedup and scalability can be difficult. This paper demonstrates the functionality of a collector-based dynamic optimization framework called DARWIN that uses collected performance data as feedback to affect the behavior of the program through the OpenMP runtime, thus able to optimizing certain aspects. The DARWIN framework utilizes the OpenMP Collector API to drive the optimization activity and various open source libraries to support its data collection and optimizations.