Persistent Hash Table Reuse for Print Color Conversion
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Solution Overview
Problem
Printing systems face inefficiencies due to the computational and memory resource-intensive process of re-initializing caches for color conversions during print jobs, leading to recurring computational processes and additional processing demands.
Innovation Solution
Implementing a hash table where user-defined color conversions are marked as persistent and new conversions as non-persistent, allowing non-persistent locations to be reused during re-initialization while preventing reuse of persistent locations, thereby preserving user-defined conversions and reducing processing burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the cache is re-initialized when space is exhausted, then new color conversions can be stored in the cache, but computational processes recur and additional processing is demanded
Solution Approach 1:
The cache is segmented into two distinct types of locations: persistent locations that retain user-defined color conversions across re-initialization, and non-persistent locations that are cleared and reused. This segmentation allows the system to preserve important data while still making space available for new conversions, eliminating the need to re-process user-defined conversions during cache re-initialization.
2Quantity of substance
If the cache size is limited, then memory resources are conserved, but the cache must be re-initialized frequently causing recurring computational processes
Solution Approach 1:
User-defined color conversions are pre-marked as persistent before cache re-initialization occurs. This preliminary marking ensures that when the cache is re-initialized, the system immediately knows which locations to preserve and which to clear, avoiding the need to re-compute or re-process persistent conversions and reducing the time lost during re-initialization.
3Device complexity
If all cache locations are treated equally during re-initialization, then re-initialization is simple, but user-defined conversions are lost and must be re-processed
Solution Approach 1:
Different locations in the cache are assigned different qualities or properties: persistent locations are marked to retain their data during re-initialization, while non-persistent locations are marked for clearing. This local differentiation allows the system to apply different treatment to different parts of the cache, preserving user-defined conversions where needed while still enabling efficient reuse of other spaces.
Data Source
AI summary
One embodiment comprises a system that stores a pre-defined color conversion table and a hash table. The system receives user-defined color conversions for a print job, stores them in first locations in the hash table, and marks the first locations as persistent. The system processes the print job utilizing the color conversion table to convert colors in the print job to the output color space, and responsive to encountering colors in the print job that do not have color conversions in the color conversion table, interpolates between color conversions in the color conversion table to generate new color conversions, stores the new color conversions in second locations in the hash table, and marks the second locations as non-persistent. The system determines that the hash table is to be re-initialized, allows reuse of the second locations in the hash table, and prevents reuse of the first locations in the hash table.