Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications

in conjunction with IEEE Cluster 2015, Chicago, IL

Architects, administrators, and users of modern high-performance computing (HPC) systems strive to meet goals of energy, resource, and application efficiency. Optimizing for any or all of these can only be accomplished through analysis of appropriate system and application information. While application performance analysis tools can provide application performance insight, the associated overhead typically decreases application performance while the tools are being employed. Thus they are typically used for application performance tuning but not for production application runs. Likewise traditional system monitoring tools in conjunction with analysis tools can provide insight into run-time system resource utilization. However, due to overhead and impact concerns, such tools are often run with collection periods on order of minutes or only used to solve problems and not during normal HPC system operation. There are currently few, if any, tools that provide continuous, low impact, high fidelity system monitoring, analysis, and feedback that meet the increasingly urgent resource efficiency optimization needs of HPC systems.

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.

The goal of this workshop is to provide an opportunity for researchers to exchange new ideas, research, techniques, and tools in the area of HPC monitoring, analysis, and feedback as it relates to increasing efficiency with respect to energy, resource utilization, and application run-time.


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, power)

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

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.

Important dates

  • Abstracts due: May 11 Extended May 18 (AOE)
  • Papers due: May 18 Final Extension May 31 (AOE)
  • Acceptance notification: Jul 2
  • Camera ready papers due: Jul 30
  • Workshop: Sep 8, 2015


HPCMASPA 2015 welcomes submissions of original work not previously published nor under review by another conference or journal. Accepted papers will be included in Cluster's workshop proceedings published by IEEE.


  • Full length technical papers. 8 pages max. 30 min presentation.
  • Short technical papers (including experience reports, work in-progress, motivation for research areas, etc). 4 pages max. 20 min presentation.


  • Submissions must be compliant with the IEEE format for conference proceedings. LaTex and Word templates can be found here.
  • Web-based submissions through Easy Chair. PDF's only.
  • 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.
  • Submissions will be evaluated on their originality, technical soundness, significance, presentation, and interest to the workshop attendees.

Attendee and Author Instructions

Registration and Final Submission information can be found at the main conference site.

The workshop consists of a full day including a interactive panel with Q&A.

In support of Cluster 2015’s focus on exascale, HPCMASPA continues to encourage submissions on monitoring of high density components (e.g., GPU, Phi); on hot topics for HPC (e.g., power, network contention); and on large-scale analysis, visualization, and response (e.g., adaptive runtimes) as it pertains to monitoring data.