Dataflow Operator Mapping Analysis for CGRP Execution Inefficiencies
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Solution Overview
Problem
Existing dataflow computing systems face inefficiencies in executing neural networks and other parallel computing applications due to challenges in mapping operations to hardware resources, particularly in systems utilizing coarse-grain reconfigurable processors (CGRPs), which require optimal allocation and synchronization of computations across multiple processing units and memory elements.
Innovation Solution
A computer-implemented efficiency analyzer selects operators from a dataflow program's intermediate representation and maps them to hardware resources, computing execution metrics, and identifies inefficiencies, enabling improved resource allocation and synchronization within computing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operators are mapped to hardware resources in dataflow computing systems, then computational functionality is achieved, but execution inefficiencies occur due to suboptimal resource allocation and synchronization
Solution Approach 1:
The system performs preliminary analysis of the dataflow program before execution to identify potential inefficiencies in operator mapping and resource allocation. By analyzing the intermediate representation and predicting execution metrics in advance, the system can optimize the mapping strategy before actual hardware execution occurs, preventing time loss during runtime.
Solution Approach 2:
The efficiency analyzer computes predicted execution metrics based on hardware descriptions and operator mappings, then uses this feedback information to identify inefficiencies and suggest improved mappings. This closed-loop feedback mechanism allows continuous optimization of resource allocation and synchronization strategies based on actual or predicted performance data.
2Power
If multiple processing units and memory elements are utilized in CGRPs, then computational capacity increases, but resource allocation complexity increases
Solution Approach 1:
The system segments the complex resource allocation problem into manageable components by analyzing operators individually or in groups and mapping them to specific hardware resources. The efficiency analyzer breaks down the overall mapping task into smaller sub-tasks that can be analyzed and optimized separately, reducing the perceived complexity while maintaining high computational capacity utilization.
Solution Approach 2:
The efficiency analyzer acts as an intermediary between the high-level dataflow program and the underlying hardware resources. It translates abstract operator requirements into concrete hardware mapping decisions, managing the complexity of resource allocation automatically without requiring direct manual intervention, thus enabling high computational capacity with reduced allocation complexity.
3Speed
If operator mapping is optimized for performance, then execution speed improves, but analysis and mapping time increases
Solution Approach 1:
The system applies partial optimization by focusing the efficiency analysis on critical sections or bottlenecks in the dataflow program rather than uniformly optimizing all operators. This selective approach achieves significant execution speed improvements for the most impactful operations while minimizing the total analysis time required, avoiding excessive optimization overhead.
Data Source
AI summary
In a method a computer-implemented efficiency analyzer selects operators from an intermediate representation of a dataflow program. The operators are included in a mapping of the operators to hardware of a computing system to execute the dataflow program. Based on the mapping and a description of the hardware, the efficiency analyzer computes an execution metric associated with executing the operators on the hardware. Based on the execution metric and hardware description, the efficiency analyzer determines an inefficiency metric, and based on the inefficiency metric, the efficiency analyzer determines an inefficiency associated with the dataflow program. The computing system to execute the dataflow program can comprise a coarse grain computing system and the hardware can include a reconfigurable processor of the computing system. A computer program product and a computing system to a the dataflow program can implement the method.