HPCMASPA 2019: Workshop on Monitoring and Analysis for HPC Systems Plus Applications Albuquerque, NM, United States, September 23, 2019 |
Conference website | https://sites.google.com/site/hpcmaspa2019/home |
Submission link | https://easychair.org/conferences/?conf=hpcmaspa2019 |
Short and Work In Progress paper deadline | July 29, 2019 |
Submission deadline | July 29, 2019 |
In conjunction with IEEE Cluster 2019
Modern processors and operating systems being used in HPC systems expose a wealth of information about how system resources, including energy, are being utilized. Lightweight tools that gather and analyze this information could provide feedback, including run-time, to increase application performance; optimize system resource utilization; and drive more efficient future HPC system design.
HPCMASPA 2019 welcomes submissions of original work not previously published nor under review by another conference or journal. All categories of papers will be peer-reviewed and published in proceedings arranged for by IEEE Cluster.
Submission categories
- Technical Papers
8 page + additional reference-only pages
Addressing completed research, best practice whitepapers, and other in-depth research and experience, etc.- Submissions Open: Jun 1
- Papers: Jul 8
- Notification: Jul 24
- Camera Ready: Aug 12
- Short and Work in Progress Papers
4 page + additional reference-only pages
At least one session will be dedicated to Short/WIP papers encouraging interactive audience discussion.- Submissions Open: Jul 25
- Papers: Jul 29
- Notification: Aug 7
- Camera Ready: Aug 12
Topics
Including, but not limited to:
Data collection, transport, and storage
- Monitoring methodologies and results for all HPC system components and support infrastructure (e.g., compute, network, storage, power, facilities)
- 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
Analysis of large-scale 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 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 analysis results and insights
- 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)
- Integration of system and facilities data for system and site operational decisions
- Use of monitored system data for evaluation of future systems specifications and requirements
- Use of monitored system data for validation of systems' simulations
Experience reports and System operations
- Design and implementation of monitoring tools as part of HPC operations
- Experiences with monitoring and analysis methodologies and tools in HPC applications
- Note this is not meant to include application performance analysis tools such as open|speedshop or craypat
- Experiences with monitoring and analysis tools for HPC systems specification/selection
- Sub-optimal approaches taken because there currently isn’t another way (include associated gap analysis)
- How not to do it, with explanations, benchmarks, or analysis of code to save the rest of us from trying it again