EDA Workload Scheduling with Dynamic Job Dependency Discovery

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Solution Overview

Problem

Existing cloud computing environments face challenges in managing dynamic job dependencies and resource allocation for Electronic Design Automation (EDA) workloads, leading to inefficiencies such as peak resource loads and idle times due to unknown job dependencies and varying resource requirements.

Innovation Solution

A processor-implemented method using a Graph Explorer tool to discover and record job dependencies, generate job packing schedules, and optimize resource utilization by creating a Directed Acyclic Graph (DAG) of jobs, which includes profiling CPU, memory, and I/O usage to schedule EDA jobs efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If traditional cloud computing environments manage EDA workloads without dynamic job dependency discovery, then implementation complexity is reduced, but resource utilization deteriorates due to peak resource loads and idle times

Engineering Contradiction:
Improveimplementation complexityVSAvoidresource utilization
Core Design Contradiction:
Device complexityVSProductivity

Solution Approach 1:

The system performs preliminary action by discovering and recording job dependencies before scheduling EDA workloads. The Graph Explorer tool proactively builds a Directed Acyclic Graph (DAG) representing job dependencies and resource requirements in advance, enabling the scheduler to optimize resource allocation and avoid peak loads and idle times before they occur.

Inventive Principle:
Principle #10Preliminary action

2Productivity

If job dependencies are discovered and recorded using a Graph Explorer tool, then resource utilization is improved, but computational overhead increases

Engineering Contradiction:
Improveresource utilizationVSAvoidcomputational overhead
Core Design Contradiction:
ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary action by discovering and recording job dependencies before scheduling EDA workloads. The Graph Explorer tool proactively builds a Directed Acyclic Graph (DAG) representing job dependencies and resource requirements in advance, enabling the scheduler to optimize resource allocation and avoid peak loads and idle times before they occur.

Inventive Principle:
Principle #10Preliminary action

3Measurement precision

If a Directed Acyclic Graph (DAG) is created to represent job dependencies, then scheduling accuracy is improved, but system complexity increases

Engineering Contradiction:
Improvescheduling accuracyVSAvoidsystem complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies segmentation by breaking down the complex scheduling problem into manageable components. The job dependency structure is segmented into a Directed Acyclic Graph (DAG) where individual nodes represent jobs and edges represent dependencies. This segmentation enables precise scheduling decisions while keeping the overall system manageable through modular representation.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system performs preliminary action by discovering and recording job dependencies before scheduling EDA workloads. The Graph Explorer tool proactively builds a Directed Acyclic Graph (DAG) representing job dependencies and resource requirements in advance, enabling the scheduler to optimize resource allocation and avoid peak loads and idle times before they occur.

Inventive Principle:
Principle #10Preliminary action

4Loss of time

If job packing schedules are generated based on discovered dependencies, then turnaround time is reduced, but computational processing time increases

Engineering Contradiction:
Improveturnaround timeVSAvoidcomputational processing time
Core Design Contradiction:
Loss of timeVSDuration of action of moving object

Solution Approach 1:

The system performs preliminary action by discovering and recording job dependencies before scheduling EDA workloads. The Graph Explorer tool proactively builds a Directed Acyclic Graph (DAG) representing job dependencies and resource requirements in advance, enabling the scheduler to optimize resource allocation and avoid peak loads and idle times before they occur.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentUS12541395B2Dynamic job dependency discovery and constraints generation to schedule EDA workloads in cloud environments
Publication Date: 2026.02.03 SYNOPSYS INC
  • US12541395B2 patent drawing
  • US12541395B2 patent drawing
  • US12541395B2 patent drawing

AI summary

Systems and methods to receive a computing job from an Electronic Design Automation (EDA) software application, and dynamically determine at least one precedence or successor job constraint for the received computing job, are described herein. Further, an edge inference algorithm is used to determine edges of a Dynamic Acyclic Graph (DAG) representing the EDA software application computing jobs, along with jobs that are dependent on the received computing job. In this way, job dependencies are discovered and scheduled dynamically, reducing turnaround time, and increasing efficiency of computing resources.