HPCMASPA 2020: Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications Kobe, Japan, September 14, 2020 |
Conference website | https://sites.google.com/view/hpcmaspa2020 |
Submission link | https://easychair.org/conferences/?conf=hpcmaspa2020 |
Submission deadline | July 6, 2020 |
Short/WIP paper submission deadline | July 27, 2020 |
HPCMASPA 2020 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.
Categories (All dates AOE)
1. Technical Papers: (8 pages + 2 reference-only pages)
Addressing completed research, best practice whitepapers, and other in-depth research and experience, etc.
- Submissions Open: Jun 5
- Papers: Jul 6
- Notification: Jul 20
- Camera Ready: Aug 14
2. Short and Work in Progress Papers: (4 pages + 1 reference-only page) At least one session will be dedicated to Short/WIP papers encouraging interactive audience discussion.
- Submissions Open: Jul 20
- Papers: Jul 27
- Notification: Aug 3
- Camera Ready: Aug 14
Submissions
- Web-based submissions through EasyChair.
- PDFs only.
- Submissions must be compliant with the format used by IEEE Cluster. LaTex and Word templates can be found here.
- NO additional pages can be purchased for this workshop.
- Submissions must be in English.
- Submission implies the willingness of at least one of the authors to register and present the work associated with submission in accordance with the policies of IEEE Cluster (see the conference website for current remote participation policies).
- Submissions will be evaluated on their technical soundness, significance, presentation, originality of work, and relevance and interest to the workshop scope.
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