Minimizing data movement through query transformation

2014 ◽  
Author(s):  
Patrick Leyshock ◽  
David Maier ◽  
Kristin Tufte
Keyword(s):  
Data Movement ◽  
Query Transformation

scholarly journals Numerical algorithms for high-performance computational science

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190066 ◽  
Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Floating Point ◽  
Important Criterion ◽  
Data Movement ◽  
Floating Point Arithmetic ◽  
High Performance Computers ◽  
Point Arithmetic ◽  
Speed And Accuracy

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

scholarly journals Categorification of the Müller-Wichards System Performance Estimation Model: Model Symmetries, Invariants, and Closed Forms

Systems ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6
Author(s):  
Allen D. Parks ◽  
David J. Marchette
Keyword(s):  
Closed Form ◽  
System Performance ◽  
Algebraic Method ◽  
Expressive Power ◽  
Computer Applications ◽  
Performance Estimation ◽  
Parallel Computer ◽  
Estimation Model ◽  
Data Movement ◽  
Fundamental Symmetry

The Müller-Wichards model (MW) is an algebraic method that quantitatively estimates the performance of sequential and/or parallel computer applications. Because of category theory’s expressive power and mathematical precision, a category theoretic reformulation of MW, i.e., CMW, is presented in this paper. The CMW is effectively numerically equivalent to MW and can be used to estimate the performance of any system that can be represented as numerical sequences of arithmetic, data movement, and delay processes. The CMW fundamental symmetry group is introduced and CMW’s category theoretic formalism is used to facilitate the identification of associated model invariants. The formalism also yields a natural approach to dividing systems into subsystems in a manner that preserves performance. Closed form models are developed and studied statistically, and special case closed form models are used to abstractly quantify the effect of parallelization upon processing time vs. loading, as well as to establish a system performance stationary action principle.

scholarly journals Classifying Data to Reduce Long-Term Data Movement in Shingled Write Disks

2016 ◽  
Vol 12 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Stephanie N. Jones ◽  
Ahmed Amer ◽  
Ethan L. Miller ◽  
Darrell D. E. Long ◽  
Rekha Pitchumani ◽  
...  
Keyword(s):  
Data Movement ◽  
Term Data

scholarly journals Reducing Data Movement on Large Shared Memory Systems by Exploiting Computation Dependencies

2018 ◽  
Author(s):  
Isaac Sánchez Barrera ◽  
Miquel Moretó ◽  
Eduard Ayguadé ◽  
Jesús Labarta ◽  
Mateo Valero ◽  
...  
Keyword(s):  
Shared Memory ◽  
Memory Systems ◽  
Data Movement

Hardware-Accelerated Dual-Split Trees

2020 ◽  
Vol 3 (2) ◽  
pp. 1-21
Author(s):  
Daqi Lin ◽  
Elena Vasiou ◽  
Cem Yuksel ◽  
Daniel Kopta ◽  
Erik Brunvand
Keyword(s):  
Ray Tracing ◽  
Hardware Acceleration ◽  
Memory Storage ◽  
Compact Representation ◽  
Space Partitioning ◽  
Data Movement ◽  
Bounding Volume ◽  
Bounding Boxes ◽  
Split Trees ◽  
Bounding Volume Hierarchies

Bounding volume hierarchies (BVH) are the most widely used acceleration structures for ray tracing due to their high construction and traversal performance. However, the bounding planes shared between parent and children bounding boxes is an inherent storage redundancy that limits further improvement in performance due to the memory cost of reading these redundant planes. Dual-split trees can create identical space partitioning as BVHs, but in a compact form using less memory by eliminating the redundancies of the BVH structure representation. This reduction in memory storage and data movement translates to faster ray traversal and better energy efficiency. Yet, the performance benefits of dual-split trees are undermined by the processing required to extract the necessary information from their compact representation. This involves bit manipulations and branching instructions which are inefficient in software. We introduce hardware acceleration for dual-split trees and show that the performance advantages over BVHs are emphasized in a hardware ray tracing context that can take advantage of such acceleration. We provide details on how the operations needed for decoding dual-split tree nodes can be implemented in hardware and present experiments in a number of scenes with different sizes using path tracing. In our experiments, we have observed up to 31% reduction in render time and 38% energy saving using dual-split trees as compared to binary BVHs representing identical space partitioning.

Development of a flow-based planning support system based on open data for the City of Atlanta

2017 ◽  
Vol 46 (2) ◽  
pp. 207-224
Author(s):  
Ge Zhang ◽  
Wenwen Zhang ◽  
Subhrajit Guhathakurta ◽  
Nisha Botchwey
Keyword(s):  
Support System ◽  
Open Data ◽  
Community Planning ◽  
Relevant Information ◽  
Easy Access ◽  
Data Movement ◽  
Planning Support ◽  
Planning Support System ◽  
Analytical Tools ◽  
The City

Open data have come of age with many cities, states, and other jurisdictions joining the open data movement by offering relevant information about their communities for free and easy access to the public. Despite the growing volume of open data, their use has been limited in planning scholarship and practice. The bottleneck is often the format in which the data are available and the organization of such data, which may be difficult to incorporate in existing analytical tools. The overall goal of this research is to develop an open data-based community planning support system that can collect related open data, analyze the data for specific objectives, and visualize the results to improve usability. To accomplish this goal, this study undertakes three research tasks. First, it describes the current state of open data analysis efforts in the community planning field. Second, it examines the challenges analysts experience when using open data in planning analysis. Third, it develops a new flow-based planning support system for examining neighborhood quality of life and health for the City of Atlanta as a prototype, which addresses many of these open data challenges.

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