Distributed Computing Data Transfer with Dynamic Bit-Length Reduction
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Solution Overview
Problem
Current computing systems face challenges in reducing bit length for numerical data representation, leading to increased power consumption and communication time in distributed computing systems, as existing methods like mixed-precision and data compression have limitations in efficiently managing bit length across different memory hierarchies.
Innovation Solution
The implementation of sensitivity analysis and bit elimination techniques, where the sensitivity parameter identifies garbage bits in binary representations, allowing for dynamic adaptation of bit length during data movement and storage, reduces the number of bits required for data elements by eliminating or replacing less significant bits without affecting computational accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mixed-precision and data compression methods are used to reduce bit length, then storage capacity is maximized, but power consumption and communication time increase
Solution Approach 1:
The patent changes the parameter of bit length dynamically based on the memory hierarchy level. Data elements are stored with reduced bit length in lower-level memories (registers, cache) and full precision in higher-level memories (main memory, storage). This parameter adaptation allows the system to use fewer bits where precision requirements are lower, reducing power consumption for data movement and storage operations.
Solution Approach 2:
The system dynamically adjusts the bit length of data elements as they move through the memory hierarchy. The bit length is not fixed but changes based on the current memory level and computational requirements. This dynamic adaptation enables the system to optimize between storage efficiency and power consumption by using shorter representations when appropriate.
2Quantity of substance
If mixed-precision and data compression methods are used to reduce bit length, then storage capacity is maximized, but communication time increases
Solution Approach 1:
The patent applies parameter changes by adjusting bit length according to memory hierarchy level. By storing data with reduced precision in faster, lower-level memories, the system reduces communication time for data access while maintaining adequate precision for computational tasks. The full-precision data resides in higher-level memories when needed for final computations.
Solution Approach 2:
The system dynamically adapts bit length based on the operational context and memory level. During data movement between memory hierarchies, the bit length is adjusted to match the requirements of the target memory level, optimizing communication time without sacrificing necessary precision for the computational workload.
3Measurement precision
If standard bit length is used for all data elements, then computational accuracy is maintained, but resource usage efficiency decreases
Solution Approach 1:
The patent applies local quality by assigning different bit lengths to different data elements based on their specific requirements and the memory level at which they are stored. Rather than using a uniform bit length for all data, the system tailors the precision locally to match the computational needs and storage characteristics of each memory level, improving resource usage efficiency while maintaining necessary accuracy.
Solution Approach 2:
The system changes the bit length parameter adaptively based on the memory hierarchy level and computational context. Data elements transition between different precision levels as they move through the memory hierarchy, with reduced precision in lower-level memories and full precision in higher-level memories. This parameter adaptation maintains computational accuracy where needed while improving overall resource efficiency.
Data Source
AI summary
Systems and methods for reducing data movement in a computer system. The systems and methods use information or knowledge about the structure of an algorithm, operations to be executed at a receiving processing unit, variables or subsets or groups of variables in a distributed algorithm, or other forms of contextual information, for reducing the number of bits transmitted from at least one transmitting processing unit to at least one receiving processing unit or storage device.