The considerable interest in the HPC community regarding in situ analysis and visualization is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a file system. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPUs and accelerators, in the computation of analysis products.
The workshop brings together researchers, developers and practitioners from industry, academia, and government laboratories developing, applying, and deploying in situ methods in extremescale, high performance computing. The goal is to present research findings, lessons learned, and insights related to developing and applying in situ methods and infrastructure across a range of science and engineering applications in HPC environments; to discuss topics like opportunities presented by new architectures, existing infrastructure needs, requirements, and gaps, and experiences to foster and enable in situ analysis and visualization; to serve as a "center of gravity" for researchers, practitioners, and users/consumers of in situ methods and infrastructure in the HPC space.
In situ infrastructures: Current Systems: production quality, research prototypes; Opportunities; Gaps
System resources, hardware, and emerging architectures: Enabling Hardware; Hardware and architectures that provide opportunities for In situ processing, such as burst buffers, staging computations on I/O nodes, sharing cores within a node for both simulation and in situ processing.
Methods/algorithms/applications/Case studies: Best practices; Analysis: feature detection, statistical methods, temporal methods, geometric methods; Visualization: information visualization, scientific visualization, time-varying methods; Data reduction/compression; Examples/case studies of solving a specific science challenge with in situ methods/infrastructure.
Simulation: Integration, data modeling, software-engineering; Resilience: error detection, fault recovery; Workflows for supporting complex in situ processing pipelines
Requirements: Preserve important elements; Significantly reduce the data size; Flexibility for postprocessing exploration
11月13日
2016
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