Instruction Reordering and Register Renaming for Loop Parallelism
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Solution Overview
Problem
Existing instruction processing systems fail to optimize the use of available registers and instruction parallelism, leading to inefficient execution and increased latency due to unutilized processor resources.
Innovation Solution
A code generator system that includes a loop duplicator, reordering component, and renaming component to unroll loops, reorder instructions, and rename registers, optimizing the use of available processor resources to improve parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If instructions are executed in the original order specified in the code, then the code maintains simple structure and ease of generation, but processor resources such as registers and execution units remain underutilized, reducing execution efficiency
Solution Approach 1:
The code generator performs preliminary analysis of the code sequence before final generation, identifying independent instructions that can be executed in parallel. This preliminary reordering of instructions allows the processor to utilize available execution units and registers more effectively, improving throughput without requiring complex runtime decision-making
Solution Approach 2:
The instruction stream is segmented into independent executable units that can be processed in parallel. By identifying and separating independent instructions from dependent ones, the system can issue multiple instructions simultaneously to different execution units, increasing instruction level parallelism while maintaining manageable code structure
2Productivity
If the code generator only uses the minimum number of registers required by the code, then register allocation is simple, but available processor registers remain unused, reducing instruction level parallelism
Solution Approach 1:
The register allocation is made dynamic by allowing the code generator to select from all available registers based on the specific code sequence and execution context. Rather than using a fixed minimum number of registers, the system dynamically assigns registers to maximize parallel execution opportunities, adapting to the specific instruction patterns encountered
Solution Approach 2:
The system changes the parameter of register usage from a static minimum allocation to a dynamic allocation that varies with the code sequence. By adjusting which registers are used and when based on instruction independence analysis, the system increases instruction level parallelism while managing register allocation complexity through systematic rules
3Loss of time
If loops are executed iteratively without unrolling, then the code remains compact and easy to generate, but execution time increases due to repeated loop overhead and reduced parallelism
Solution Approach 1:
The code generator performs preliminary unrolling of loops during code generation, duplicating loop body instructions multiple times to eliminate loop overhead. This preliminary expansion of the loop structure allows subsequent instruction reordering to identify more parallel execution opportunities across what would have been iterative iterations, reducing total execution time despite increased code size
Solution Approach 2:
Loop body instructions are copied multiple times to create unrolled iterations. By creating explicit copies of the loop body rather than using a single iterative structure, the system enables better instruction level parallelism and eliminates the overhead of loop control instructions, with the copied instructions then being reordered for optimal parallel execution
Data Source
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AI summary
A system is disclosed. The system may include a processor including a register and a code memory. A reordering component may reorder a first instruction and a second instruction in a set of instructions of a first type in a list of code stored in the code memory, at least one of the first instruction and the second instruction accessing the register.