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Title: Towards Exascale: High Performance Visualization and Analytics -Project Status Report

Authors:
; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1196790
Report Number(s):
LBNL-5767E
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; hybrid parallelism, parallel volume rendering, parallel streamlines, performance optimization, auto-tuning, extended memory hierarchy

Citation Formats

Bethel, E. Wes, Childs, Hank, Howison, Mark, Krishnan, Hari, Loring, Burlen, Meyer, Joerg, Ruebel, Oliver, Ushizima, Daniela, Weber, Gunther, and Camp, David. Towards Exascale: High Performance Visualization and Analytics -Project Status Report. United States: N. p., 2012. Web. doi:10.2172/1196790.
Bethel, E. Wes, Childs, Hank, Howison, Mark, Krishnan, Hari, Loring, Burlen, Meyer, Joerg, Ruebel, Oliver, Ushizima, Daniela, Weber, Gunther, & Camp, David. Towards Exascale: High Performance Visualization and Analytics -Project Status Report. United States. doi:10.2172/1196790.
Bethel, E. Wes, Childs, Hank, Howison, Mark, Krishnan, Hari, Loring, Burlen, Meyer, Joerg, Ruebel, Oliver, Ushizima, Daniela, Weber, Gunther, and Camp, David. 2012. "Towards Exascale: High Performance Visualization and Analytics -Project Status Report". United States. doi:10.2172/1196790. https://www.osti.gov/servlets/purl/1196790.
@article{osti_1196790,
title = {Towards Exascale: High Performance Visualization and Analytics -Project Status Report},
author = {Bethel, E. Wes and Childs, Hank and Howison, Mark and Krishnan, Hari and Loring, Burlen and Meyer, Joerg and Ruebel, Oliver and Ushizima, Daniela and Weber, Gunther and Camp, David},
abstractNote = {},
doi = {10.2172/1196790},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2012,
month = 4
}

Technical Report:

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  • Query-driven visualization and analytics is a unique approach for high-performance visualization that offers new capabilities for knowledge discovery and hypothesis testing. The new capabilities akin to finding needles in haystacks are the result of combining technologies from the fields of scientific visualization and scientific data management. This approach is crucial for rapid data analysis and visualization in the petascale regime. This article describes how query-driven visualization is applied to a hero-sized network traffic analysis problem.
  • This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high performance analytical pipelines. As the architectures of emerging HPC systems become inherently heterogeneous, there is a need to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of CPUs, GPUs, and SSDs. Furthermore, the power-aware trend drives the advances in our performance-energy tradeoff analysis framework which enables our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.
  • The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we willmore » instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.« less
  • This report discusses the particulars of application simulation and other modeling software.
  • Abstract not provided.