Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

Journal Article · · IEEE Transactions on Visualization and Computer Graphics
 [1];  [2];  [3];  [3];  [3];  [2]
  1. Univ. of Utah, Salt Lake City, UT (United States); University of Utah
  2. Univ. of Utah, Salt Lake City, UT (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
+ Show Author Affiliations

There presently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Additionally, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results proposed in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.

Research Organization:
Univ. of Utah, Salt Lake City, UT (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21). Scientific Discovery through Advanced Computing (SciDAC); National Science Foundation (NSF)
Grant/Contract Number:
NA0002375; AC52-07NA27344; SC0007446; SC0010498
OSTI ID:
1591612
Journal Information:
IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 1 Vol. 25; ISSN 1077-2626
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

Cited By (1)

Compression Challenges in Large Scale Partial Differential Equation Solvers journal September 2019

Figures / Tables (19)

Figure 1(p. 1)figure Figure 1
Figure 2(p. 3)figure Figure 2
Table 1(p. 3)table Table 1
Algorithm 1(p. 4)figure Algorithm 1
Algorithm 2(p. 5)figure Algorithm 2
Fig. 3(p. 5)figure Fig. 3
Table 2(p. 5)table Table 2
Fig. 4(p. 6)figure Fig. 4
Fig. 5(p. 6)figure Fig. 5
Fig. 6(p. 6)figure Fig. 6
Fig. 7(p. 7)figure Fig. 7
Fig. 8(p. 7)figure Fig. 8
Fig. 9(p. 7)figure Fig. 9
Fig. 10(p. 8)figure Fig. 10
Fig. 11(p. 8)figure Fig. 11
Fig. 12(p. 8)figure Fig. 12
Fig. 13(p. 9)figure Fig. 13
Fig. 14(p. 9)figure Fig. 14
Fig. 15(p. 9)figure Fig. 15

Similar Records

Bit Grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+)
Journal Article · Mon Sep 19 00:00:00 EDT 2016 · Geoscientific Model Development (Online) · OSTI ID:1328486
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Conference · Sat Nov 13 23:00:00 EST 2021 · OSTI ID:1834048
TTHRESH: Tensor Compression for Multidimensional Visual Data
Journal Article · Thu Mar 07 23:00:00 EST 2019 · IEEE Transactions on Visualization and Computer Graphics · OSTI ID:1644746

Related Subjects

97 MATHEMATICS AND COMPUTING
Data Management
Large Data
bit ordering
data analysis
data compression
multi-resolution