Fast And Automatic Floating Point Error Analysis With CHEF-FP

Garima Singh, Baidyanath Kundu, Harshitha Menon, Alexander Penev, David J. Lange, Vassil Vassilev

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As we reach the limit of Moore's Law, researchers are exploring different paradigms to achieve unprecedented performance. Approximate Computing (AC), which relies on the ability of applications to tolerate some error in the results to trade-off accuracy for performance, has shown significant promise. Despite the success of AC in domains such as Machine Learning, its acceptance in High-Performance Computing (HPC) is limited due to its stringent requirement of accuracy. We need tools and techniques to identify regions of the code that are amenable to approximations and their impact on the application output quality so as to guide developers to employ selective approximation. To this end, we propose CHEF-FP, a flexible, scalable, and easy-to-use source-code transformation tool based on Automatic Differentiation (AD) for analysing approximation errors in HPC applications.CHEF-FP uses Clad, an efficient AD tool built as a plugin to the Clang compiler and based on the LLVM compiler infrastructure, as a backend and utilizes its AD abilities to evaluate approximation errors in C++ code. CHEF-FP works at the source level by injecting error estimation code into the generated adjoints. This enables the error-estimation code to undergo compiler optimizations resulting in improved analysis time and reduced memory usage. We also provide theoretical and architectural augmentations to source code transformation-based AD tools to perform FP error analysis. In this paper, we primarily focus on analyzing errors introduced by mixed-precision AC techniques, the most popular approximate technique in HPC. We also show the applicability of our tool in estimating other kinds of errors by evaluating our tool on codes that use approximate functions. Moreover, we demonstrate the speedups achieved by CHEF-FP during analysis time as compared to the existing state-of-the-art tool as a result of its ability to generate and insert approximation error estimate code directly into the derivative source. The generated code also becomes a candidate for better compiler optimizations contributing to lesser runtime performance overhead.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9798350337662
StatePublished - 2023
Event37th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023 - St. Petersburg, United States
Duration: May 15 2023May 19 2023

Publication series

NameProceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023


Conference37th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
Country/TerritoryUnited States
CitySt. Petersburg

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems


  • Approximate Computing
  • Automatic Differentiation
  • Clad
  • Clang
  • High-Performance Computing


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