X: A Comprehensive Analytic Model for Parallel Machines
To continuously comply with Moore’s Law, modern parallel machines become increasingly complex. Effectively tuning application performance for these machines therefore becomes a daunting task. Moreover, identifying performance bottlenecks at application and architecture level, as well as evaluating various optimization strategies, are becoming extremely difficult when the entanglement of numerous correlated factors is being presented. To tackle these challenges, we present a visual analytical model named “X”. It is intuitive and sufficiently flexible to track all the typical features of a parallel machine.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1322524
- Report Number(s):
- PNNL-SA-117063; KJ0403000
- Resource Relation:
- Conference: IEEE International Parallel & Distributed Processing Symposium (IPDPS 2016), May 23-27, 2016 Chicago, Illinois, 242-252
- Country of Publication:
- United States
- Language:
- English
Similar Records
Data Locality Enhancement of Dynamic Simulations for Exascale Computing (Final Report)
Parsl: Pervasive Parallel Programming in Python
Machine Learning-enabled Scalable Performance Prediction of Scientific Codes
Technical Report
·
Fri Nov 29 00:00:00 EST 2019
·
OSTI ID:1322524
Parsl: Pervasive Parallel Programming in Python
Conference
·
Sat Jun 22 00:00:00 EDT 2019
·
OSTI ID:1322524
+9 more
Machine Learning-enabled Scalable Performance Prediction of Scientific Codes
Journal Article
·
Fri Apr 23 00:00:00 EDT 2021
· ACM Transactions on Modeling and Computer Simulation
·
OSTI ID:1322524
+1 more