Subject: IEEE Cluster 2014 Workshop HPCMASPA Sep 26 Madrid Spain Call for
From: Henry Neeman 
Date: Mon, 7 Apr 2014 22:04:12 -0500 (CDT)
OSCER users,

Forwarding from the IEEE Cluster Workshop HPCMASPA 2014.

Please reply directly to them, and please feel free to forward to
anyone who may be interested and appropriate.

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SUMMARY:

IEEE Cluster Workshop HPCMASPA 2014
Call for Papers
Workshop on Monitoring and Analysis for
High Performance Computing Systems Plus Applications
Submission deadline: Friday May 23 2014
https://sites.google.com/site/hpcmaspa2014

In conjunction with IEEE Cluster 2014, Sept 26, Madrid, Spain

DETAILS:

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.

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/hpcmaspa2014

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