Compiler System for Vector Processor Code Compilation
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Solution Overview
Problem
Existing compiler technologies face challenges in efficiently compiling source code for execution by processors, particularly in optimizing code for vector processors to achieve high-performance processing.
Innovation Solution
The development of a compiler system that includes a Vector Micro-Code Processor (VMP) OpenCL compiler, utilizing a Low Level Virtual Machine (LLVM) based compilation scheme, to efficiently compile source code into target code optimized for vector processors. This involves a front-end for parsing OpenCL C-code, a middle-end for optimizations, and a back-end for generating target-specific machine code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional compiler is used to compile source code for vector processors, then the compilation process is simple, but the processing performance and resource utilization are insufficient
Solution Approach 1:
The compiler is divided into distinct modules: front-end for parsing OpenCL C-code, middle-end for optimizations, and back-end for generating target-specific machine code. This segmentation allows each module to specialize in specific tasks, improving overall compilation efficiency and processing performance while managing complexity through modular design.
Solution Approach 2:
An intermediate representation (IR) is introduced as a mediator between the source code and target machine code. The IR serves as a common format that facilitates optimizations in the middle-end and enables efficient code generation in the back-end, thereby improving processing performance without proportionally increasing complexity.
2Productivity
If code is optimized for vector processors, then processing efficiency improves, but the compilation complexity increases
Solution Approach 1:
The compiler applies different optimization strategies tailored to vector processor characteristics in the middle-end. Specific optimizations are applied locally to code regions that benefit from vectorization, improving resource utilization while avoiding unnecessary complexity in parts of the code that don't require such optimizations.
Solution Approach 2:
The back-end transforms the intermediate representation into target-specific machine code by changing parameters such as instruction encoding, register allocation, and memory access patterns to match vector processor architecture. This parameter transformation enables efficient resource utilization on vector processors while keeping the optimization logic separate from code generation.
Data Source
AI summary
For example, a compiler may be configured to identify a first plurality of instructions based on a source code to be compiled into a target code to be executed by a target processor. For example, the compiler may be configured to determine, based on the first plurality of instructions, an instruction to Arithmetic Logic Unit (ALU) (instruction-ALU) allocation to allocate a second plurality of instructions to a plurality of ALU's of the target processor. For example, the second plurality of instructions may be based on the first plurality of instructions. For example, the compiler may be configured to generate the target code based on compilation of the source code. For example, the target code may be based on the second plurality of instructions allocated to the plurality of ALUs.