Dynamic Translator Shadow Memory JIT Compilation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Dynamic translation systems face inefficiencies due to the time required to translate non-native instructions into native instructions during runtime, as they involve retrieval and parsing of instructions, which can lead to overhead in decoding processes.
Innovation Solution
The implementation of a dynamic translation system that uses instruction code translation and just-in-time (JIT) compilation, where non-native instructions are loaded into memory, interpreted to generate instruction codes, and precompiled code fragments are selected from shadow memory for execution, allowing for the dynamic choice between executing instruction codes or compiled native instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic translation is used to translate non-native instructions to native instructions during runtime, then the system can execute non-native code without pre-compilation, but the translation time and overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-compiling translation code sequences before they are needed. A compilation queue maintains precompiled native code sequences corresponding to non-native instruction sequences. When non-native code is encountered, the system checks if a precompiled sequence exists, avoiding runtime translation overhead. This resolves the contradiction by preparing translation mappings in advance while maintaining the ability to execute non-native code dynamically.
Solution Approach 2:
The patent implements dynamics by creating a hybrid translation system that adapts between different execution modes. The system dynamically selects between executing precompiled native sequences and performing runtime translation based on availability. A compilation queue dynamically manages which non-native sequences need precompilation, allowing the system to balance between adaptation capability and execution speed based on runtime conditions.
2Manufacturing precision
If runtime parsing of non-native instructions is performed to determine opcode, source, and destination locations, then the translation can be accurate, but the decoding overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-analyzing and compiling non-native instruction sequences into native code before execution. The compilation queue stores preprocessed translation mappings that include opcode, source, and destination location information. During runtime, the system retrieves these precompiled sequences without performing parsing operations, maintaining translation accuracy while eliminating decoding overhead.
Solution Approach 2:
The patent introduces an intermediary compilation queue that acts as a buffer between non-native code and native execution. This intermediary structure stores precompiled translation sequences, allowing the system to separate the complex parsing and compilation work from the execution path. The intermediary maintains translation accuracy by preserving detailed instruction mappings while improving productivity by providing direct access to compiled sequences during runtime.
3Ease of manufacture
If a loop-based emulator translates each non-native instruction individually, then the translation process is simple to implement, but the execution performance deteriorates
Solution Approach 1:
The patent applies merging by combining multiple individual instruction translations into batch processing. Instead of translating each non-native instruction individually through a simple loop, the system groups sequences of non-native instructions and compiles them together into native code blocks. This merging approach maintains implementation simplicity by building on the loop-based structure while dramatically improving execution performance through reduced translation overhead and optimized native code generation.
Solution Approach 2:
The patent applies preliminary action by performing compilation work in advance of execution. The compilation queue identifies and compiles sequences of non-native instructions before they need to be executed. This preliminary compilation transforms the simple loop-based translation approach into a two-phase system: a compilation phase that prepares optimized native code, and an execution phase that runs the precompiled code directly, thereby improving performance without complicating the core translation logic.
Data Source
AI summary
Systems and methods for executing non-native instructions in a computing system having a processor configured to execute native instructions are provided. A dynamic translator uses instruction code translation in parallel with just-in-time (JIT) compilation to execute the non-native instructions. Non-native instructions may be interpreted to generate instruction codes, which may be stored in a shadow memory. During a subsequent scheduling of a non-native instruction for execution, the corresponding instruction code may be retrieved from the shadow memory and executed, thereby avoiding reinterpreting the non-native instruction. In addition, the JIT compiler may compile instruction codes to generate native instructions, which may be made available for execution, further speeding up the execution process. A system implementing the aforementioned translator may improve performance and speed associated with executing non-native instructions by optimizing the use of instruction codes and JIT-compiled native instructions corresponding to the instruction codes when executing the non-native instructions.


