HPCMASPA 2014
 Workshop on Monitoring and Analysis for High Performance Computing 
 Systems Plus Applications

in conjunction with IEEE Cluster 2014
Madrid, Spain

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 actual user 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 main goal of this workshop is to provide an opportunity for researchers to exchange 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.
Topics

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)
  • 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
Important dates
  • May 31 AOE - Abstract due
  • June 7 AOE - Papers due (contingent upon abstract submission by 5/31) 
  • June 30 - Acceptance notification
  • July 21 - Camera ready papers due
  • Sept 26 - Workshop 
Format
HPCMASPA 2014 welcomes submissions for the following:

Technical Papers (30 minute presentation):
These submissions consist of work not previously published nor under review by another conference or journal. Accepted papers will be included in the workshop proceedings published by IEEE. 

Mini-talks (15 minute presentation):
These submissions can consist of previously published work and can address work-in-progress, highlight gap areas, motivate research areas, etc. Accepted mini-talks will not be published in the proceedings.

Submission Guidelines:
    • Submissions (either type) must be compliant with the IEEE Xplore format for publication. LaTeX (preferred) and Word Templates are available here. Additional instructions can be found on the IEEE Cluster site.
    • Maximum 8 pages for Technical Papers
    • Maximum 4 pages descriptive text and figures for Mini-talks
    • Web-based submission through EasyChair. 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. 


Organization
Organizing Committee
  • Benjamin Allan, Sandia National Laboratories
  • Jim Brandt, Sandia National Laboratories
  • Ann Gentile, Sandia National Laboratories
  • Cory Lueninghoener, Los Alamos National Laboratory
  • Nichamon Naksinehaboon, Open Grid Computing
  • Boyana Norris, University of Oregon
  • Narate Taerat, Open Grid Computing
Program Committee
  • Jon Cook, New Mexico State University
  • Narayan Desai, Argonne National Laboratory
  • Richard Gerber, NERSC
  • Forest Godfrey, Cray
  • Yun (Helen) He, Lawrence Berkeley National Laboratory
  • Karen Karavanic, Portland State University
  • Zhiling Lan, Illinois Institute of Technology
  • Box Leangsuksun, Louisiana Tech University
  • Mike Mason, Los Alamos National Laboratory
  • Henry Neeman, Oklahoma University Supercomputing Center for Education & Research
  • Martin Schulz, Lawrence Livermore National Laboratory
  • Mike Showerman, NCSA 
  • David Thompson, Kitware
  • Ziming Zheng, HP Vertica