Workshop on Monitoring and Analysis for
High Performance Computing Systems Plus Applications
https://sites.google.com/site/
In conjunction with IEEE Cluster 2014, Sept 26, Madrid, Spain
------------------------------
HPCMASPA 2014 seeks both original Research Papers and informational Mini-talks on new ideas, research, techniques, and tools in the area of HPC system level monitoring, analysis, and feedback as it relates to increasing efficiency with respect to energy, resource utilization, and application run-time.
SUBMISSION DEADLINE: May 23, 2014
Research Papers will be published in IEEE Cluster Workshop Proceedings.
Topics include, but are not limited to the following areas:
Data collection, transport, and storage
Design of systems and frameworks for HPC monitoring which address HPC requirements such as:
Extreme scalability
Run time data collection and transport
Analysis on actionable timescales
Feedback on actionable timescales
Minimal application impact
Extraction and evaluation of resource utilization and state information from current and next generation components (e.g., GPU, MICS)
Monitoring methodologies and results for all HPC system components and support infrastructure (e.g., compute, network, storage)
How not to do it, with explanations, benchmarks, or analysis of code to save the rest of us from trying it again
Analysis of monitored data and system information
Extraction of meaningful information from raw data, such as system and resource health, contention, or bottlenecks
Methodologies and applications of analysis algorithms on large scale HPC system data
Visualization techniques for large scale HPC data (addressing size, timescales, presentation within a meaningful context)
Evaluation of correlative relationships between system state and application performance via use of monitored system data
Response to and utilization of processed data and system information
Mechanisms for feedback and response to applications and system software (e.g., informing schedulers, down-clocking CPUs)
HPC application design and implementation that take advantage of monitored system data (e.g., dynamic task placement or rank-to-core mapping)
System-level and Job-level feedback and responses to monitored system data
Job Scheduling and Allocation based on monitored system information (e.g. contention for storage or network resources)
Use of monitored system data for evaluation of future systems specifications and requirements
Use of monitored system data for validation of systems simulations
Additional Details at https://sites.google.com/site/
--
Boyana Norris
norris@cs.uoregon.edu
High Performance Computing Systems Plus Applications
https://sites.google.com/site/
In conjunction with IEEE Cluster 2014, Sept 26, Madrid, Spain
------------------------------
HPCMASPA 2014 seeks both original Research Papers and informational Mini-talks on new ideas, research, techniques, and tools in the area of HPC system level monitoring, analysis, and feedback as it relates to increasing efficiency with respect to energy, resource utilization, and application run-time.
SUBMISSION DEADLINE: May 23, 2014
Research Papers will be published in IEEE Cluster Workshop Proceedings.
Topics include, but are not limited to the following areas:
Data collection, transport, and storage
Design of systems and frameworks for HPC monitoring which address HPC requirements such as:
Extreme scalability
Run time data collection and transport
Analysis on actionable timescales
Feedback on actionable timescales
Minimal application impact
Extraction and evaluation of resource utilization and state information from current and next generation components (e.g., GPU, MICS)
Monitoring methodologies and results for all HPC system components and support infrastructure (e.g., compute, network, storage)
How not to do it, with explanations, benchmarks, or analysis of code to save the rest of us from trying it again
Analysis of monitored data and system information
Extraction of meaningful information from raw data, such as system and resource health, contention, or bottlenecks
Methodologies and applications of analysis algorithms on large scale HPC system data
Visualization techniques for large scale HPC data (addressing size, timescales, presentation within a meaningful context)
Evaluation of correlative relationships between system state and application performance via use of monitored system data
Response to and utilization of processed data and system information
Mechanisms for feedback and response to applications and system software (e.g., informing schedulers, down-clocking CPUs)
HPC application design and implementation that take advantage of monitored system data (e.g., dynamic task placement or rank-to-core mapping)
System-level and Job-level feedback and responses to monitored system data
Job Scheduling and Allocation based on monitored system information (e.g. contention for storage or network resources)
Use of monitored system data for evaluation of future systems specifications and requirements
Use of monitored system data for validation of systems simulations
Additional Details at https://sites.google.com/site/
--
Boyana Norris
norris@cs.uoregon.edu
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