Literature DB >> 28804690

Towards Program Optimization through Automated Analysis of Numerical Precision.

Michael D Linderman1, Matthew Ho1, David L Dill1, Teresa H Meng1, Garry P Nolan2.   

Abstract

Reducing the arithmetic precision of a computation has real performance implications, including increased speed, decreased power consumption, and a smaller memory footprint. For some architectures, e.g., GPUs, there can be such a large performance difference that using reduced precision is effectively a requirement. The tradeoff is that the accuracy of the computation will be compromised. In this paper we describe a proof assistant and associated static analysis techniques for efficiently bounding numerical and precision-related errors. The programmer/compiler can use these bounds to numerically verify and optimize an application for different input and machine configurations. We present several case study applications that demonstrate the effectiveness of these techniques and the performance benefits that can be achieved with rigorous precision analysis.

Entities:  

Keywords:  D.2.4 [Software Engineering]: Program Verification–Validation; D.3.4 [Programming Languages]: Processors–Optimization; Design; Fixed-Point Numbers; Floating-Point Numbers; G.1.0 [Mathematics of Computing]: Numerical Analysis–Computer Arithmetic; Numerical Precision; Performance; Static Error Analysis; Verification

Year:  2010        PMID: 28804690      PMCID: PMC5552069          DOI: 10.1145/1772954.1772987

Source DB:  PubMed          Journal:  Proc CGO        ISSN: 2164-2397


  2 in total

1.  Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems.

Authors:  Zachary S Zumsteg; Caleb Kemere; Stephen O'Driscoll; Gopal Santhanam; Rizwan E Ahmed; Krishna V Shenoy; Teresa H Meng
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-09       Impact factor: 3.802

2.  HermesB: a continuous neural recording system for freely behaving primates.

Authors:  Gopal Santhanam; Michael D Linderman; Vikash Gilja; Afsheen Afshar; Stephen I Ryu; Teresa H Meng; Krishna V Shenoy
Journal:  IEEE Trans Biomed Eng       Date:  2007-11       Impact factor: 4.538

  2 in total
  1 in total

1.  High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers.

Authors:  Michael D Linderman; Vivek Athalye; Teresa H Meng; Narges Bani Asadi; Robert Bruggner; Garry P Nolan
Journal:  ICS       Date:  2010-06
  1 in total

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