Integration Ecosystem
CLIO Plugins
30+ built-in integrations. Pipeline-based deployment. Turn complex HPC applications into composable, reproducible workflows in minutes.
How It Works
CLIO Plugins are powered by Jarvis-CD — a unified deployment framework that turns any application into a composable pipeline package.
Discover Resources
Auto-detect storage devices, network topology, and hardware capabilities across your cluster.
jarvis rg build Compose Pipelines
Chain plugins into multi-stage pipelines. Storage + simulation + analysis + visualization — all in one workflow.
jarvis ppl append lammps Deploy Anywhere
Same pipeline definition works on a laptop, a Slurm cluster, or a containerized cloud environment.
jarvis ppl run Plugin Catalog
Every integration is a self-contained package with automated configuration, dependency management, and multi-node support.
🔬 Scientific Simulations
LAMMPS
Molecular dynamics — materials, polymers, biological systems
Gray-Scott
Reaction-diffusion simulation via ADIOS2
Gadget-2
N-body/SPH cosmological simulations
CM1
Cloud-resolving atmospheric model
InCompact3D
High-order incompressible flow solver
Nyx
AMReX-based Lyman-alpha cosmology
Cosmic Tagger
Deep learning for neutrino physics
DDMD
Deep-learning driven molecular dynamics
PyFlexTrkr
Feature tracking for climate science
💾 Storage Systems & I/O
OrangeFS
Parallel file system deployment and management
Redis
In-memory data store for caching and messaging
Spark Cluster
Apache Spark for distributed data processing
Data Stage-In
Automated data staging for pipelines
MkFS
Filesystem provisioning and formatting
📊 Benchmarks & Analysis
IOR
Parallel I/O benchmarking (POSIX, MPI-IO, HDF5)
FIO
Flexible I/O tester for storage performance
FileBench
Workload model-driven filesystem benchmarks
DLIO Benchmark
Deep learning I/O characterization
Darshan
I/O tracing and performance analysis
ParaView
3D scientific visualization pipelines
Compose Real Pipelines
Chain plugins to build end-to-end scientific workflows. Here's a molecular dynamics pipeline with tiered storage and live visualization:
# Initialize Jarvis for your cluster
jarvis init ~/jarvis_conf ~/jarvis_local ~/jarvis_shared
# Discover available storage (NVMe, RAM, PFS)
jarvis rg build
# Build the pipeline: IOWarp runtime → LAMMPS → Darshan → ParaView
jarvis ppl create md_analysis
jarvis ppl append iowarp_runtime
jarvis ppl append lammps
jarvis ppl append darshan
jarvis ppl append paraview
# Deploy across 4 nodes and run
jarvis hostfile set ~/cluster_hosts
jarvis ppl run
Plugin Architecture
Resource Graph
Automatic hardware discovery. Plugins query the resource graph to find available storage devices, network interfaces, GPUs, and compute nodes.
No manual configuration for different machines — Jarvis adapts to what's available.
Pipeline Composition
Plugins chain into multi-stage pipelines with dependency resolution. Each package manages its own lifecycle: configure, build, start, stop, destroy.
Shared/private directories enable multi-node data sharing without manual orchestration.
Interceptors
Transparently intercept I/O calls (POSIX, MPI-IO) to route through IOWarp's storage engine — no application changes required.
Legacy applications get IOWarp benefits with zero code modifications.
Custom Plugins
Build your own plugin in Python. Extend the JarvisPackage base class, define configuration, and plug into any pipeline.
Repository system for sharing plugins across teams and organizations.
Build Your Own Plugin
Every scientific application can become a CLIO Plugin. Start with our example template, define your configuration schema, and your app is ready to compose into any IOWarp pipeline.