Decode Time Instruction Grouping for Reduced Data Dependencies
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Solution Overview
Problem
Current pipelined processing techniques face inefficiencies in optimizing instructions due to limitations in grouping and recombining instructions for improved code execution, particularly in decode time optimization, which can lead to suboptimal performance in processors like those based on z/Architecture and Power Architecture.
Innovation Solution
A computer program product facilitates instruction grouping based on optimization criteria, forming new groups of instructions at decode time to enable better execution by combining instructions with fewer data dependencies, thereby optimizing code execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If instructions are grouped and recombined at decode time to reduce data dependencies, then code execution efficiency is improved, but processor architecture complexity increases
Solution Approach 1:
The patent applies preliminary action by performing instruction grouping and optimization analysis during the decode stage, before execution. The processor identifies candidate instructions for grouping, analyzes their compatibility, and forms optimized instruction groups in advance, reducing data dependencies before the instructions reach the execution stage. This allows execution units to process optimized groups with fewer dependencies, improving overall code execution efficiency without adding complexity to the execution architecture itself.
Solution Approach 2:
The patent segments the instruction stream into distinct groups that can be independently optimized and processed. By dividing the sequential instruction flow into manageable groups based on compatibility criteria and data dependency analysis, the processor can apply optimization techniques to each group separately. This segmentation approach allows complex optimization logic to be contained within the decode stage, preventing complexity from propagating to other processor components.
2Loss of time
If multiple instructions are combined into fewer internal instructions, then the number of execution steps is reduced, but instruction decoding complexity increases
Solution Approach 1:
The decode stage performs preliminary analysis to identify instructions that can be combined into fewer internal instructions. By examining instruction patterns, operands, and potential optimizations before execution, the processor consolidates multiple instructions into optimized groups, reducing the total number of execution steps required. This preliminary combining action occurs during decoding, allowing the execution stage to process fewer, more efficient instruction groups.
Solution Approach 2:
The patent changes the parameters of instruction groups by modifying how instructions are organized and represented internally. Instructions are transformed from individual sequential operations into grouped structures with modified dependency relationships. This parameter change in instruction representation allows the execution units to process multiple original instructions as fewer consolidated operations, reducing execution time while containing decoding complexity within manageable bounds through systematic transformation rules.
3Productivity
If instructions are recombined with fewer data dependencies, then processing throughput is improved, but group formation complexity increases
Solution Approach 1:
The processor performs preliminary analysis during the decode stage to identify instructions suitable for grouping based on compatibility criteria and data dependency relationships. By conducting this analysis before execution, the system can form optimized instruction groups that minimize data dependencies, allowing execution units to process multiple instructions in parallel or with reduced serialization. This preliminary group formation improves processing throughput by preparing optimized instruction sets in advance.
Solution Approach 2:
The patent implements feedback mechanisms where the decode stage monitors instruction characteristics, dependency relationships, and grouping effectiveness. This feedback information is used to dynamically adjust group formation strategies, identifying patterns that lead to successful optimization while avoiding configurations that increase complexity unnecessarily. The feedback loop allows the system to learn from previous grouping outcomes and refine its group formation logic, improving throughput while managing complexity through experience-driven optimization.
Data Source
AI summary
Instructions are grouped into instruction groups based on optimizations that may be performed. An instruction is obtained, and a determination is made as to whether the instruction is to be included in a current instruction group or another instruction group. This determination is made based on whether the instruction is a candidate for optimization, such as decode time instruction optimization. If it is determined that the instruction is to be included in another group, then the other group is formed to include the instruction.