Dynamic Data-Flow Analysis for Hardware Accelerator Vectorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing systems face inefficiencies in vector processing due to the need for static determination of data-flow bounds, which limits dynamic vectorization and resource utilization in multi-thread execution environments, especially when passing data between threads.
Innovation Solution
The microarchitecture of a data processing system is enhanced to automatically identify thread-restricted functions by using data indicators and instruction predicates, allowing for accelerated execution on hardware accelerators like vector processors, without requiring explicit vector instructions or static loop bounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static determination of data-flow bounds is used, then vector processing can be performed with known loop bounds at compilation time, but dynamic vectorization and resource utilization are limited
Solution Approach 1:
The patent implements dynamic determination of data-flow bounds during execution rather than static determination at compilation time. The system uses runtime analysis to identify thread-restricted functions and dynamically generates vectorized code, allowing the vectorization to adapt to actual execution conditions and data characteristics, thereby resolving the contradiction between precise bounds determination and dynamic adaptability
Solution Approach 2:
The system changes the parameter of bounds determination from static (compilation time) to dynamic (execution time). By using runtime information about thread restrictions and data-flow characteristics, the system can determine precise bounds while simultaneously adapting to dynamic execution conditions, enabling both accurate measurement and versatility
2Ease of operation
If data is passed between multiple threads using software FIFO buffers, then multi-thread execution is enabled, but transfer latency increases significantly (about 630 cycles for core-to-core, 1500 cycles for socket-to-socket)
Solution Approach 1:
The patent extracts the data passing operation from the general multi-thread execution model by identifying thread-restricted functions where data remains confined to a single thread. By detecting when data does not actually cross thread boundaries despite being in a multi-thread environment, the system can apply specialized vectorized execution to eliminate the overhead and latency associated with inter-thread data transfer mechanisms
Solution Approach 2:
The system introduces an intermediary analysis layer that examines data-flow characteristics to determine whether data truly needs to be passed between threads. This intermediary mechanism identifies cases where data appears to be in a multi-thread context but is actually thread-restricted, allowing the system to optimize these cases by avoiding actual inter-thread transfer and its associated latency
3Productivity
If explicit vector instructions are used, then vector processing efficiency is improved, but programmer complexity increases as they must know in advance when vector operations are to be performed and the amount of data to be processed
Solution Approach 1:
The patent implements self-service by enabling the compiler or runtime system to automatically perform the analysis and generation of vectorized code without requiring explicit programmer intervention. The system autonomously identifies thread-restricted functions, determines data-flow bounds, and generates appropriate vector instructions, thereby maintaining high vector processing efficiency while eliminating the complexity burden from programmers
Solution Approach 2:
The system performs preliminary analysis of data-flow characteristics and thread restrictions during compilation or before execution. By pre-identifying which functions are thread-restricted and can benefit from vectorization, the system prepares the necessary information in advance, allowing efficient vector processing to occur automatically without requiring programmers to have advance knowledge of when vector operations should be applied
Data Source
AI summary
A method and apparatus are provided for dynamically determining when an operation, specified by one or more instructions in a data processing system, is suitable for accelerated execution. Data indicators are maintained, for data registers of the system, that indicate when data-flow from a register derives from a restricted source. In addition, instruction predicates are provided for instructions to indicate which instructions are capable of accelerated execution. From the data indicators and the instruction predicates, the microarchitecture of the data processing system determines, dynamically, when the operation is a thread-restricted function and suitable for accelerated execution in a hardware accelerator. The thread-restricted function may be executed on a hardware processor, such as a vector, neuromorphic or other processor.