Task Multiplicity & Parallelism

Common Patterns

• Same analysis, multiple datasets

• Parameter sweeps

• Ensemble simulations

• Independent pipeline stages

The Challenge

• Manual execution doesn’t scale

• Task dependencies need coordination

• Resource allocation optimization

✓ Opportunity

• Idle cores await tasks

• Independent tasks run simultaneously

• Significant speedup potential

✗ Challenges

• Code must support parallelism

• Avoid data conflicts

• Overhead costs

Parallelism Types: Embarrassingly parallel → Shared memory → Distributed

Data Challenges

• Race conditions

• Data consistency

• Output organization

• Intermediate storage

Workflow Orchestration

• Task scheduling

• Failure handling

• Resource allocation

• Progress tracking