Generic Reduction Parallelization via Compiler Annotations
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Solution Overview
Problem
Current parallel programming languages lack mechanisms to express and execute generic reduction operations beyond basic associative and commutative operations like addition and multiplication, limiting the parallel processing capabilities for complex reduction tasks.
Innovation Solution
A system that detects and supports generic reduction operations in parallel programming languages by identifying reduction variables, initial values, and merge operations, and compiles source code to facilitate parallel execution, using annotations and an updated runtime execution system to manage thread-private variables and merge functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parallel programming languages support only simple reduction operations for a limited set of operators, then the language implementation remains simple and execution is reliable, but the versatility and applicability to complex reduction tasks is severely limited
Solution Approach 1:
The patent implements a universal reduction mechanism that can handle any associative operation through a generic framework. The system defines reduction operations through annotations that specify the operation type, variables, and merge functions, allowing the same parallel execution infrastructure to support diverse reduction tasks including max/min, addition, multiplication, and custom operations without requiring separate implementation mechanisms for each operation type.
2Adaptability or versatility
If the system implements support for generic reduction operations with annotations and runtime systems, then the versatility and range of supported operations increases, but the device complexity and implementation overhead increases
Solution Approach 1:
The patent introduces annotations as an intermediary layer between the source code and the execution system. These annotations provide metadata about reduction operations (operation type, variables, initial values, merge functions) that the compiler and runtime system can process automatically. This intermediary mechanism allows the system to support generic reduction operations without requiring complex hard-coded logic for each operation type, as the annotations carry all necessary operational information.
Solution Approach 2:
The system performs preliminary analysis and code generation during compilation based on the annotations. The compiler identifies reduction operations, generates appropriate parallel execution code, and prepares merge functions before runtime execution. This preliminary action shifts complexity from runtime to compile-time, allowing the runtime system to execute pre-prepared parallel reduction operations efficiently without needing to interpret or dynamically generate complex logic during execution.
3Productivity
If basic commutative and associative operations are supported in parallel, then parallel execution performance is improved for simple operations, but complex reduction tasks cannot be executed in parallel
Solution Approach 1:
The patent implements a dynamic reduction framework where the operation type, variables, and merge functions are specified through annotations rather than being fixed at language design time. This allows the parallel execution system to adapt to different reduction operations (simple or complex) by interpreting the annotation metadata and generating appropriate execution code dynamically, enabling both simple operations like addition and complex custom reduction tasks to execute in parallel with the same infrastructure.
Data Source
AI summary
One embodiment of the present invention provides a system that supports parallelized generic reduction operations in a parallel programming language, wherein a reduction operation is an associative operation that can be divided into a group of sub-operations that can execute in parallel. During operation, the system detects generic reduction operations in source code. In doing so, the system identifies a set of reduction variables upon which the generic reduction operation will operate, along with a set of initial values for the variables. The system additionally identifies a merge operation that merges partial results from the parallel generic reduction operations into a final result. The system then compiles the program's source code into a form which facilitates executing the generic reduction operations in parallel. By supporting the parallel execution of such generic reduction operations in this way, the present invention extends parallel execution for reduction operations beyond basic commutative and associative operations such as addition and multiplication.