Dynamic Resource Balancing for EDA Compute Engines
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Solution Overview
Problem
The increasing complexity of electronic circuit designs requires significant computational resources, and existing computing systems struggle to efficiently allocate and balance resources during the execution of Electronic Design Automation (EDA) operations, leading to suboptimal performance and efficiency.
Innovation Solution
A dynamic resource balancing system that allocates and reallocates computing resources among compute engines based on operation priorities and idle indicators, utilizing a dynamic resource balancing engine to optimize resource utilization and improve computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are statically allocated to compute engines, then resource allocation is simple and stable, but computational efficiency deteriorates due to inability to adapt to varying operation priorities
Solution Approach 1:
The patent implements dynamic resource allocation where the resource allocation manager continuously monitors compute engine states and operation priorities, reallocating computing resources on-the-fly rather than using static allocation. This allows the system to adapt to varying computational demands and maintain optimal productivity.
Solution Approach 2:
The system employs feedback mechanisms where the resource allocation manager receives status information from compute engines about their current operations and resource utilization, then uses this feedback to make informed reallocation decisions, balancing productivity improvement with controlled complexity.
2Productivity
If computing resources are reallocated frequently to optimize performance, then computational efficiency improves, but system stability deteriorates due to constant resource movements
Solution Approach 1:
The resource allocation manager performs resource reallocation at periodic intervals or at specific trigger points (such as when compute engines become idle or when operation priorities change), rather than continuously. This periodic approach maintains productivity while providing stability by avoiding constant resource movements.
Solution Approach 2:
The system changes resource allocation parameters (such as the amount of computing resources assigned to each compute engine) based on monitored conditions like operation priority levels and compute engine idle states, allowing performance optimization while maintaining system stability through controlled parameter adjustments.
3Reliability
If computing resources are allocated based on operation priority, then critical EDA operations receive sufficient resources, but resource allocation complexity increases due to priority monitoring and decision-making
Solution Approach 1:
The resource allocation manager serves as an intermediary between compute engines and computing resources, centralizing the complexity of priority-based allocation decisions. This intermediary monitors operation priorities and manages resource distribution, ensuring critical operations receive necessary resources while containing allocation complexity in a dedicated management component.
Solution Approach 2:
Compute engines provide information about their current operations and resource needs to the resource allocation manager, enabling priority-based allocation without requiring complex monitoring infrastructure. The system leverages self-reported data from compute engines to simplify the overall allocation complexity while maintaining reliability.
4Productivity
If idle computing resources are utilized for reallocation, then resource utilization efficiency improves, but system complexity increases due to idle detection and dynamic reallocation mechanisms
Solution Approach 1:
Compute engines autonomously report their idle status to the resource allocation manager, eliminating the need for complex system-wide monitoring infrastructure. This self-service approach allows efficient utilization of idle resources while minimizing the complexity of idle detection mechanisms.
Solution Approach 2:
The resource allocation manager performs multiple functions including monitoring compute engine states, determining operation priorities, deciding resource reallocation, and managing idle resources. By consolidating these diverse functions into a single multi-functional component, the system improves resource utilization efficiency while containing overall system complexity.
Data Source
AI summary
A system may include a set of compute engines. The compute engines may be configured to perform electronic design automation (EDA) operations on a hierarchical dataset representative of an integrated circuit (IC) design. The system may also include a dynamic resource balancing engine configured to allocate computing resources to the set of compute engines and reallocate a particular computing resource allocated to a first compute engine based on an operation priority of an EDA operation performed by a second compute engine, an idle indicator for the first compute engine, or a combination of both.