CGRA Context State Loading Using Kernel-Specific Context Masks
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Solution Overview
Problem
Existing computing architectures face challenges in efficiently loading context states for kernel execution in coarse-grained reconfigurable array processors, leading to time-consuming context switching and performance constraints.
Innovation Solution
Implement a context loading mechanism that loads only the context state required for the executing kernel, using a context mask associated with the kernel to identify applicable portions of the context state, stored in main memory and read by a state machine for efficient configuration of the CGRA processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire context state is loaded for each kernel execution, then the CGRA processor can be fully configured, but the context switching time and resource consumption increase significantly
Solution Approach 1:
The context state is segmented into multiple portions, each associated with a specific kernel. Instead of loading the entire context state, the system loads only the portions corresponding to the executing kernel, identified through context masks. This segmentation dramatically reduces context switching time while ensuring only the necessary kernel-specific context is loaded, maintaining execution correctness.
Solution Approach 2:
The invention extracts and loads only the essential context state portions required for kernel execution, discarding unnecessary context data. By using context masks to identify applicable portions, the system extracts minimal required context information from memory, reducing both time and resources spent on context loading while preserving the necessary state for correct kernel operation.
2Reliability
If the entire context state is loaded for each kernel execution, then all processor state is available, but the memory bandwidth and energy consumption increase
Solution Approach 1:
The context state is divided into segmented portions, each tied to specific kernels. The system segments the memory bandwidth utilization by loading only the relevant context portions through targeted memory accesses guided by context masks, rather than performing full context state transfers, thereby reducing energy consumption while maintaining necessary state completeness.
Solution Approach 2:
The invention extracts only the necessary context state portions from memory, using context masks to identify and load minimal required data. This extraction approach reduces memory bandwidth consumption and energy usage while ensuring that complete and accurate processor state is available for kernel execution.
3Productivity
If context masks and selective loading are implemented, then context switching efficiency improves, but the device complexity increases
Solution Approach 1:
The context masks are pre-computed and stored in memory alongside the context state portions. This preliminary action allows the state machine to quickly identify and load the correct context portions without complex real-time computation, improving context switching efficiency while limiting the increase in device complexity to pre-computation and storage operations.
Data Source
AI summary
Devices and techniques for loading contexts in a coarse-grained reconfigurable array processor are described herein. A system or apparatus may include context load circuitry operable to load context for a coarse-grained reconfigurable array processor, where the context load circuitry is configured to: (a) receive a kernel identifier; (b) access a first registry to obtain a context mask base address; (c) determine a context mask address from the context mask base address and the kernel identifier; (d) access a second registry to obtain a context state base address; (e) determine a context state address from the context state base address and the kernel identifier; (f) use a context mask at the context mask address to determine corresponding active context state; and (g) load the corresponding active context state into the coarse-grained reconfigurable array processor.