Lazy Evaluation for Java GPU Program Optimization
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Solution Overview
Problem
Eager evaluation in GPU programming leads to inefficient GPU utilization due to computation of unnecessary results, wasting valuable resources.
Innovation Solution
Implementing a system and method that employs lazy evaluation and a novel jogArray structure to defer GPU program launches until results are explicitly requested, pruning unnecessary invocations and launching only programs that contribute to the final outcome, using a frontend to compile Java bytecode into Intermediate Representation (IR) code and a collector/composer to aggregate IR code for efficient GPU execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If eager evaluation is used to launch GPU programs for each jog.foreach() call, then the implementation is simple and straightforward, but GPU resources are wasted due to computation of unnecessary results
Solution Approach 1:
The system dynamically determines which GPU programs to launch based on runtime information about which results are actually needed. The lazy evaluation mechanism allows the system to adaptively select and execute only the necessary computations, transforming a static eager evaluation approach into a dynamic one that responds to actual needs.
Solution Approach 2:
The invention changes the evaluation parameter from eager (immediate execution) to lazy (deferred execution until needed). This parameter change allows the system to control when GPU programs are launched, enabling optimization by skipping unnecessary computations while maintaining the same Java API interface.
2Loss of energy
If lazy evaluation is implemented to defer GPU program launches until results are needed, then GPU resource utilization is optimized, but the device complexity increases due to the need for tree traversal and IR code composition
Solution Approach 1:
The invention introduces an intermediary layer consisting of IR code representation and a composition mechanism. Instead of directly managing complex lazy evaluation logic, the system uses IR code as an intermediary representation that can be collected, composed, and optimized before GPU execution. This intermediary layer simplifies the overall system architecture while enabling sophisticated optimization.
Solution Approach 2:
The invention extracts the evaluation timing decision from the GPU program launch process itself. By separating the program compilation (eager) from the program execution (lazy), the system can optimize resource usage without requiring complex modifications to the GPU execution model. The extraction of evaluation timing control enables independent optimization of each phase.
3Ease of manufacture
If all GPU programs are launched immediately, then the implementation is straightforward, but processing time is increased due to unnecessary computations
Solution Approach 1:
The invention extracts the evaluation timing decision from the GPU program launch process itself. By separating the program compilation (eager) from the program execution (lazy), the system can optimize resource usage without requiring complex modifications to the GPU execution model. The extraction of evaluation timing control enables independent optimization of each phase.
Solution Approach 2:
The system performs preliminary compilation of Java bytecode to IR code for all potential GPU programs, but defers actual GPU execution until results are needed. This preliminary action prepares the computation graph in advance while postponing resource-intensive GPU launches, enabling both ease of implementation and time optimization.
Data Source
AI summary
A system and method for optimizing multiple invocations of a graphics processing unit (GPU) program in Java. In one embodiment, the system includes: (1) a frontend component in a computer system and configured to compile Java bytecode associated with the a class object that implements a functional interface into Intermediate Representation (IR) code and store the IR code with the associated jogArray and (2) a collector/composer component in the computer system, associated with the frontend and configured to traverse a tree containing the multiple invocations from the result to collect the IR code and compose the IR code collected in the traversing into aggregate IR code when a result of the GPU program is explicitly requested to be transferred to a host.