Dynamic FEU Scheduling for SIMD Thread Divergence
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Solution Overview
Problem
In Single Instruction Multiple Data (SIMD) architecture, conditional branch divergence leads to idle SIMD lanes and performance degradation, as each SIMD lane may follow a different control flow, resulting in inefficient thread management and processing speeds.
Innovation Solution
The implementation of multiple Front End Units (FEUs) and a controller that schedules thread groups based on SIMD lane availability, manages active thread information, and optimizes instruction fetching and distribution to minimize idle lanes and improve performance through dynamic scheduling and time-space division.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single FEU is used to process thread groups in SIMD architecture, then device complexity is reduced, but SIMD lanes become idle during thread divergence leading to performance degradation
Solution Approach 1:
The system dynamically activates or deactivates FEUs based on thread divergence detection and SIMD lane availability. The controller monitors thread execution states and adjusts the number of active FEUs in real-time, transitioning from a static single-FEU configuration to a dynamic multi-FEU configuration only when thread divergence occurs, thereby improving performance while minimizing unnecessary complexity
Solution Approach 2:
The processing system is segmented into multiple independent FEUs that can be selectively activated. Each FEU can independently fetch and execute instructions for different thread groups, allowing the system to partition workloads across multiple execution units when thread divergence creates idle lanes, rather than relying on a single overloaded FEU
2Productivity
If multiple FEUs are activated to process divergent threads, then idle SIMD lanes are reduced, but control complexity increases due to need for scheduling and coordination
Solution Approach 1:
The controller automatically monitors thread divergence states and SIMD lane availability, then self-manages the activation and scheduling of multiple FEUs without external intervention. The system includes built-in mechanisms for detecting when thread groups diverge and autonomously configuring the number of active FEUs based on real-time execution states, reducing the need for complex external control logic
Solution Approach 2:
The system implements feedback mechanisms where the controller continuously monitors thread execution states and SIMD lane utilization, then adjusts FEU activation and scheduling decisions based on this feedback. This closed-loop control allows the system to adaptively optimize throughput by activating additional FEUs only when thread divergence creates idle lanes, rather than maintaining constant high complexity
3Productivity
If threads are scheduled without considering SIMD lane availability, then scheduling simplicity is maintained, but idle lanes increase reducing processing efficiency
Solution Approach 1:
The controller performs preliminary detection of thread divergence and SIMD lane availability before scheduling additional FEUs. By proactively identifying when idle lanes will occur due to thread divergence, the system can pre-configure the number of active FEUs and their assigned thread groups, avoiding the need for complex real-time rescheduling while maintaining high processing efficiency
Data Source
AI summary
Provided are an apparatus and a method for effectively managing threads diverged by a conditional branch based on Single Instruction Multiple-based Data (SIMD). The apparatus includes: a plurality of Front End Units (FEUs) configured to fetch, for execution by SIMD lanes, instructions of thread groups of a program flow; and a controller configured to schedule a thread group based on SIMD lane availability information, activate an FEU of the plurality of FEUs, and control the activated FEU to fetch an instruction for processing the scheduled thread group.