Mixing Condition Numbers and Oracles for Accurate Floating-point Debugging

Recent advances have made numeric debugging tools much faster by using double-double oracles, and numeric analysis tools much more accurate by using condition numbers. But these techniques have downsides: double-double oracles have correlated error so miss floating-point errors while condition numbers cannot cleanly handle over- and underflow. We combine both techniques to avoid these downsides. Our combination, Explanifloat,computes condition numbers using double-double arithmetic, which avoids correlated errors. To handle over-and underflow, it introduces a separate logarithmic oracle. As a result, Explanifloatachieves a precision of 80.0% and a recall of 96.1 % on a collection of 546 difficult numeric benchmarks: more accurate than double-double oracles yet dramatically faster than arbitrary-precision condition number computations.

Authors

Institutions

Publication Details

Published
2025-05-04
DOI
https://doi.org/10.1109/arith64983.2025.00025
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mixing Condition Numbers and Oracles for Accurate Floating-point Debugging

Pavel Panchekha, B. D. Kulkarni
Parallel Computing and Optimization Techniques
article

Mixing Condition Numbers and Oracles for Accurate Floating-point Debugging

Pavel Panchekha, B. D. Kulkarni
article en

Abstract

Recent advances have made numeric debugging tools much faster by using double-double oracles, and numeric analysis tools much more accurate by using condition numbers. But these techniques have downsides: double-double oracles have correlated error so miss floating-point errors while condition numbers cannot cleanly handle over- and underflow. We combine both techniques to avoid these downsides. Our combination, Explanifloat,computes condition numbers using double-double arithmetic, which avoids correlated errors. To handle over-and underflow, it introduces a separate logarithmic oracle. As a result, Explanifloatachieves a precision of 80.0% and a recall of 96.1 % on a collection of 546 difficult numeric benchmarks: more accurate than double-double oracles yet dramatically faster than arbitrary-precision condition number computations.

University of Utah (US)
Openalex Percentile: Top 85%
Parallel Computing and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.