Compiler Decompiler Vector Optimization
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Solution Overview
Problem
Existing compilers are unable to optimize vector operations effectively due to their opaque nature, which limits their ability to perform optimizations on vectors and mathematical functions, leading to inefficiencies in compilation and decompilation processes, especially in applications like computer graphics where precision and speed are critical.
Innovation Solution
A vector-based compiler and decompiler system that can see vector components and perform optimizations by using vector calculations internally, incorporating pattern matching methods to recognize mathematical patterns and apply identities, and by implementing mathematical functions within the compiler to evaluate results at compile-time, allowing for efficient data structure handling and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing compilers treat vectors as opaque data structures, then the compiler can handle general data types, but the compiler cannot perform optimizations on vector operations
Solution Approach 1:
The patent changes the parameter of vector representation from opaque to transparent by introducing a new data structure that exposes vector components. This allows the compiler to see individual vector elements and apply optimizations while maintaining compatibility through a translation layer that converts between the new transparent representation and traditional opaque representations.
Solution Approach 2:
The patent introduces an intermediary data structure that acts as a bridge between the compiler's traditional opaque vector handling and the desired transparent vector optimization. This intermediary representation allows the compiler to perform optimizations on vector components while still producing code compatible with existing vector instruction sets and libraries.
2Adaptability or versatility
If existing compilers treat mathematical functions as black boxes, then the compiler can link to any library, but the compiler cannot apply mathematical identities or optimizations
Solution Approach 1:
The patent changes the parameter of mathematical function representation from opaque black boxes to transparent symbolic expressions. By representing mathematical functions with their symbolic forms and properties, the compiler can recognize patterns, apply mathematical identities, and optimize expressions while maintaining the ability to link to external libraries through a translation layer.
Solution Approach 2:
The patent performs preliminary analysis of mathematical expressions by representing them in a transparent symbolic form before code generation. This allows the compiler to apply mathematical identities and simplify expressions in advance, improving optimization capability while preserving library compatibility through the translation layer that converts optimized expressions back to library function calls when necessary.
3Ease of operation
If decompilation translates executables to source code, then reverse engineering is enabled, but the decompiled code may not be equivalent to the original source
Solution Approach 1:
The patent introduces an intermediary representation that preserves more information during the decompilation process. This intermediate form maintains vector component structure and mathematical expression properties, enabling more accurate reconstruction of the original source code while still allowing reverse engineering of executables. The intermediary representation acts as a lossless bridge between binary and source representations.
Data Source
AI summary
A method, system, and computer-readable medium including operations for optimizing computer code is disclosed. A block of mixed intermediate representation (MIR) code is received. A partially-decompiled block of computer code is generated from the MIR code. For each instruction in the block of MIR code, in reverse order, a native expression vector for the instruction is computed. A set of pattern-matching operations is repeated until no transformations occur. A fully-decompiled block of computer code is generated from the partially-decompiled block of computer code, the fully-decompiled block of computer code having a semantic level that is raised. The fully-decompiled block of computer code is provided for deployment on an architecture, the deployment including lowering the semantic of the computer code to a level that corresponds to a CPU or GPU supported by the architecture.