User guide
- Overview
- Apps
- Futures
- Passing Python objects
- Staging data files
- Execution
- MPI and Multi-node Apps
- Error handling
- Memoization and checkpointing
- Configuration
- Creating and Using Config Objects
- How to Configure
- Heterogeneous Resources
- Accelerators
- Multi-Threaded Applications
- Ad-Hoc Clusters
- Amazon Web Services
- ASPIRE 1 (NSCC)
- Illinois Campus Cluster (UIUC)
- Bridges (PSC)
- CC-IN2P3
- CCL (Notre Dame, TaskVine)
- Expanse (SDSC)
- Improv (Argonne LCRC)
- Perlmutter (NERSC)
- Frontera (TACC)
- Kubernetes Clusters
- Midway (RCC, UChicago)
- Open Science Grid
- Polaris (ALCF)
- Stampede2 (TACC)
- Summit (ORNL)
- TOSS3 (LLNL)
- Further help
- Monitoring
- Example parallel patterns
- Structuring Parsl programs
- Lifted operators
- Join Apps
- Usage Statistics Collection
- Plugins
- Measuring performance with parsl-perf
- Glossary of Parsl Terms
- App:
- AppFuture:
- Bash App:
- Block:
- Checkpointing:
- Concurrency:
- Configuration:
- DataFuture:
- DataFlowKernel (DFK):
- Elasticity:
- Execution Provider:
- Executor:
- Future:
- Job:
- Launcher:
- Manager:
- Memoization:
- MPI App:
- Node:
- Parallelism:
- Parsl Script:
- Plugin:
- Python App:
- Resource:
- Serialization:
- Staging:
- Task:
- Thread:
- Worker:
- Workflow: