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Title: Analytical Cost Metrics : Days of Future Past

Abstract

As we move towards the exascale era, the new architectures must be capable of running the massive computational problems efficiently. Scientists and researchers are continuously investing in tuning the performance of extreme-scale computational problems. These problems arise in almost all areas of computing, ranging from big data analytics, artificial intelligence, search, machine learning, virtual/augmented reality, computer vision, image/signal processing to computational science and bioinformatics. With Moore’s law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. Therefore the major challenge that we face in computing systems research is: “how to solve massive-scale computational problems in the most time/power/energy efficient manner?”

Authors:
 [1];  [1];  [2]
  1. Colorado State Univ., Fort Collins, CO (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1422949
Report Number(s):
LA-UR-18-21279
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Mathematics; optimization, software/hardware codesign, compiling, performance prediction

Citation Formats

Prajapati, Nirmal, Rajopadhye, Sanjay, and Djidjev, Hristo Nikolov. Analytical Cost Metrics : Days of Future Past. United States: N. p., 2018. Web. doi:10.2172/1422949.
Prajapati, Nirmal, Rajopadhye, Sanjay, & Djidjev, Hristo Nikolov. Analytical Cost Metrics : Days of Future Past. United States. doi:10.2172/1422949.
Prajapati, Nirmal, Rajopadhye, Sanjay, and Djidjev, Hristo Nikolov. Tue . "Analytical Cost Metrics : Days of Future Past". United States. doi:10.2172/1422949. https://www.osti.gov/servlets/purl/1422949.
@article{osti_1422949,
title = {Analytical Cost Metrics : Days of Future Past},
author = {Prajapati, Nirmal and Rajopadhye, Sanjay and Djidjev, Hristo Nikolov},
abstractNote = {As we move towards the exascale era, the new architectures must be capable of running the massive computational problems efficiently. Scientists and researchers are continuously investing in tuning the performance of extreme-scale computational problems. These problems arise in almost all areas of computing, ranging from big data analytics, artificial intelligence, search, machine learning, virtual/augmented reality, computer vision, image/signal processing to computational science and bioinformatics. With Moore’s law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. Therefore the major challenge that we face in computing systems research is: “how to solve massive-scale computational problems in the most time/power/energy efficient manner?”},
doi = {10.2172/1422949},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Feb 20 00:00:00 EST 2018},
month = {Tue Feb 20 00:00:00 EST 2018}
}

Technical Report:

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