17947004. Machine Learning with Dynamic Bytecode Transformation simplified abstract (META PLATFORMS, INC.)
Contents
- 1 Machine Learning with Dynamic Bytecode Transformation
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Machine Learning with Dynamic Bytecode Transformation - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Machine Learning with Dynamic Bytecode Transformation
Organization Name
Inventor(s)
Jason Ansel of Mountain View CA (US)
Machine Learning with Dynamic Bytecode Transformation - A simplified explanation of the abstract
This abstract first appeared for US patent application 17947004 titled 'Machine Learning with Dynamic Bytecode Transformation
Simplified Explanation
The abstract describes a system that uses a just-in-time compiler to receive bytecode, extract operations, generate an FX graph, compile the graph using a user-defined compiler, and execute the bytecode based on the compiled function.
- Just-in-time compiler used to modify bytecode dynamically
- Extraction of operations from bytecode
- Generation of an FX graph based on extracted operations
- Compilation of FX graph using a user-defined compiler
- Execution of bytecode based on compiled function
Potential Applications
This technology could be applied in optimizing the performance of software applications by dynamically modifying bytecode before execution.
Problems Solved
This technology solves the problem of inefficient bytecode execution by dynamically modifying and optimizing the operations before compiling and executing them.
Benefits
The benefits of this technology include improved performance, faster execution of bytecode, and better utilization of computing resources.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of high-performance software applications where speed and efficiency are critical.
Possible Prior Art
Prior art in this field may include existing just-in-time compilers, bytecode optimization techniques, and graph-based compilation methods.
Unanswered Questions
How does this technology compare to traditional static compilation methods?
This article does not provide a direct comparison between this technology and traditional static compilation methods.
What impact does this technology have on overall system performance?
The article does not discuss the specific impact of this technology on overall system performance.
Original Abstract Submitted
In one embodiment, a computing system may receive, by a just-in-time compiler, a plurality of bytecode to dynamically modify prior to executing. The computing system may extract, using the just-in-time compiler, sequences of one or more operations from the plurality of bytecode. The computing system may generate, using the just-in-time compiler an FX graph based on the sequences of the one or more operations. The computing system may compile, using a user-defined compiler, the FX graph into a compiled function. The computing system may execute the plurality of bytecode based at least on the compiled function.
