Thread Synchronization Orchestration via Stage Segmentation
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Solution Overview
Problem
Shared memory dependencies in parallel computing platforms, such as GPUs, lead to performance bottlenecks due to costly synchronization operations required to ensure that shared data is current across multiple threads.
Innovation Solution
The proposed solution involves determining which threads in a group will perform prologue and epilogue operations based on their position in execution, allowing for the elimination of several synchronization steps and reducing the overhead of thread management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple threads perform synchronization operations to ensure shared data is current, then data consistency is improved, but execution time increases due to costly synchronization steps
Solution Approach 1:
The patent segments the thread execution into distinct stages: prologue operations (first to execute), body operations (parallel computation), and epilogue operations (last to execute). This segmentation allows threads to perform different operations at different stages, eliminating the need for all threads to perform all synchronization operations, thus reducing execution time while maintaining data consistency.
Solution Approach 2:
The patent introduces prologue operations that are performed by the first thread to execute before the parallel body operations. These prologue operations prepare shared data in advance, allowing subsequent threads to skip certain synchronization steps and directly perform body operations, thereby reducing overall execution time while ensuring data consistency.
2Reliability
If threads perform numerous synchronization operations to ensure shared data is current, then data consistency is improved, but productivity decreases due to reduced parallel efficiency
Solution Approach 1:
By segmenting thread operations into prologue, body, and epilogue stages, the patent enables different threads to execute different portions of the computation simultaneously. This reduces the number of synchronization operations needed across all threads, thereby improving parallel efficiency and productivity while maintaining data consistency through the structured execution model.
Solution Approach 2:
The patent implements a model where threads automatically determine their execution stage and perform appropriate operations without requiring explicit synchronization commands from the host. The first thread performs prologue operations, middle threads perform body operations, and the last thread performs epilogue operations, with the system automatically managing synchronization implications, thus improving parallel efficiency.
3Adaptability or versatility
If all threads perform all operations, then operation completeness is improved, but device complexity increases due to thread management overhead
Solution Approach 1:
The patent segments operations into three distinct types (prologue, body, epilogue) that are performed by different threads at different stages. This segmentation simplifies thread management by eliminating the need for complex synchronization protocols, as each thread only executes the operations appropriate to its stage, thereby reducing device complexity while maintaining operation completeness.
Solution Approach 2:
The prologue operations performed by the first thread prepare shared data in advance, allowing subsequent threads to skip initialization steps and directly execute body operations. This preliminary action reduces the operational complexity for individual threads while ensuring all necessary operations are completed, thereby reducing overall thread management overhead.
Data Source
AI summary
Apparatuses, systems, and techniques to execute data-dependent parallel operations in one or more programs utilizing an application programming interface to perform parallel computing, such as CUDA, without relying on a synchronization operation between said one or more programs. For example, at least one embodiment pertains to processors or computing systems used to determine which thread in a group of threads finishes modifying shared data last, and that thread is selected to perform additional data-dependent computations from said group of threads.