Decimal Floating Point Composition via Packed Significand and Biased Exponent
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Solution Overview
Problem
Existing technologies face challenges in converting data from human-readable decimal formats to decimal floating point formats without loss of accuracy, particularly in processing environments where intermediate data types like signed packed decimal and signed binary integer are involved.
Innovation Solution
The implementation of computer-readable program code logic that converts a significand in packed decimal format and a biased exponent in binary integer format into a decimal floating point datum, enabling composition and decomposition of decimal floating point data while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary floating point is used to represent real numbers, then computational processing is efficient, but accuracy is lost when representing decimal fractions like 0.1
Solution Approach 1:
The patent changes the radix parameter from base-2 (binary) to base-10 (decimal) in the floating point representation system. This allows exact representation of decimal fractions like 0.1 while maintaining the floating point structure for efficient computational processing. The significand is stored as decimal digits rather than binary bits, fundamentally changing the numerical base parameter to resolve the accuracy issue.
Solution Approach 2:
The patent introduces an intermediary conversion process that translates between human-readable decimal formats and decimal floating point formats. This intermediary mechanism includes converting decimal character strings to intermediate formats (signed packed decimal for significand, signed binary integer for exponent) and then to decimal floating point format, ensuring accuracy is preserved throughout the transformation.
2Measurement precision
If decimal floating point format is used, then accuracy of decimal fractions is maintained, but conversion complexity increases when dealing with intermediate data types
Solution Approach 1:
The patent segments the conversion process into distinct stages: first converting the significand from human-readable format to signed packed decimal format, then converting the exponent to signed binary integer format, and finally combining these to form the decimal floating point datum. This segmentation of the conversion process into manageable parts reduces overall complexity by handling each component separately with dedicated conversion logic.
Solution Approach 2:
The patent uses intermediate data types (signed packed decimal for significand, signed binary integer for exponent) as mediators in the conversion process. These intermediate formats serve as bridging representations that facilitate accurate transformation from human-readable formats to decimal floating point format, managing complexity through structured intermediate steps rather than direct conversion.
3Productivity
If direct conversion from human-readable format to decimal floating point format is attempted, then processing steps are reduced, but accuracy may be lost without proper intermediate representations
Solution Approach 1:
The patent performs preliminary conversion actions by first transforming the human-readable significand into signed packed decimal format and the exponent into signed binary integer format before final assembly into decimal floating point format. This preliminary structuring of data ensures that accuracy-preserving representations are established early in the process, preventing accuracy loss while maintaining efficient subsequent conversion steps.
Data Source
AI summary
A decimal floating point finite number in a decimal floating point format is composed from the number in a different format. A decimal floating point format includes fields to hold information relating to the sign, exponent and significand of the decimal floating point finite number. Other decimal floating point data, including infinities and NaNs (not a number), are also composed. Decimal floating point data are also decomposed from the decimal floating point format to a different format.