Load balancing using dynamic cache allocation
Proceedings of the 7th ACM international conference on Computing frontiers
Detailed performance analysis using coarse grain sampling
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
Scalable fine-grained call path tracing
Proceedings of the international conference on Supercomputing
Can manycores support the memory requirements of scientific applications?
ISCA'10 Proceedings of the 2010 international conference on Computer Architecture
Concurrency and Computation: Practice & Experience
On the usefulness of object tracking techniques in performance analysis
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Framework for a productive performance optimization
Parallel Computing
| Hi-index | 0.00 |
Analyzing parallel programs has become increasingly difficult due to the immense amount of information collected on large systems. The use of clustering techniques has been proposed to analyze applications. However, while the objective of previous works is focused on identifying groups of processes with similar characteristics, we target a much finer granularity in the application behavior. In this paper, we present a tool that automatically characterizes the different computation regions between communication primitives in message-passing applications. This study shows how some of the clustering algorithms which may be applicable at a coarse grain are no longer adequate at this level. Density-based clustering algorithms applied to the performance counters offered by modern processors are more appropriate in this context. This tool automatically generates accurate displays of the structure of the application as well as detailed reports on a broad range of metrics for each individual region detected.