Debugging and detecting numerical errors in computation with posits
Sangeeta Chowdhary, Jay P. Lim, Santosh Nagarakatte
Abstract
Posit is a recently proposed alternative to the floating point representation (FP). It provides tapered accuracy. Given a fixed number of bits, the posit representation can provide better precision for some numbers compared to FP, which has generated significant interest in numerous domains. Being a representation with tapered accuracy, it can introduce high rounding errors for numbers outside the above golden zone. Programmers currently lack tools to detect and debug errors while programming with posits.
This paper presents PositDebug, a compile-time instrumentation that performs shadow execution with high precision values to detect various errors in computation using posits. To assist the programmer in debugging the reported error, PositDebug also provides directed acyclic graphs of instructions, which are likely responsible for the error. A contribution of this paper is the design of the metadata per memory location for shadow execution that enables productive debugging of errors with long-running programs. We have used PositDebug to detect and debug errors in various numerical applications written using posits. To demonstrate that these ideas are applicable even for FP programs, we have built a shadow execution framework for FP programs that is an order of magnitude faster than Herbgrind.
• Software and its engineering → Software maintenance tools.
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Install the CLIlune papers fulltext f981a986-acf1-47b1-adb4-2af2ca35fb25Cited by top-tier papers9
- Parallel shadow execution to accelerate the debugging of numerical errorsSangeeta Chowdhary, Santosh NagarakatteFSE 2021 · 21 citations
- An approach to generate correctly rounded math libraries for new floating point variantsJay P. Lim, Mridul Aanjaneya, John L. Gustafson, Santosh NagarakattePOPL 2021 · 21 citations
- High performance correctly rounded math libraries for 32-bit floating point representationsJay P. Lim, Santosh NagarakattePLDI 2021 · 19 citations
- DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep LearningEliska Kloberdanz, Kyle G. Kloberdanz, Wei LeICSE 2022 · 16 citations
- Fast shadow execution for debugging numerical errors using error free transformationsSangeeta Chowdhary, Santosh NagarakatteOOPSLA 2022 · 13 citations
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