iowarp-core 1.9.7

IOWarp Context Management Platform

CLIO Core

A Comprehensive Platform for Context Management in Scientific Computing

Overview · Installation · Components · Quickstart · Documentation · Contributing

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Overview

CLIO Core is a unified framework that integrates multiple high-performance components for context management, data transfer, and scientific computing. CLIO Core enables developers to build efficient data processing pipelines for HPC, storage systems, and near-data computing applications.

It provides:

• High-performance context management for computational contexts and data transformations.
• Heterogeneous-aware I/O with multi-tiered, dynamic buffering.
• A modular runtime with dynamically loadable processing modules.
• Shared-memory data structures that work across host, CUDA, and ROCm.
• Distributed-by-construction scaling from single node to clusters.

Architecture

┌──────────────────────────────────────────────────────────────┐
│                      Applications                            │
│          (Scientific Workflows, HPC, Storage Systems)        │
└──────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
┌───────────────┐   ┌──────────────────┐   ┌────────────────┐
│   Context     │   │    Context       │   │   Context      │
│  Exploration  │   │  Assimilation    │   │   Transfer     │
│    Engine     │   │     Engine       │   │    Engine      │
└───────────────┘   └──────────────────┘   └────────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              │
                    ┌─────────────────┐
                    │  CLIO Runtime   │
                    │  (Module System)│
                    └─────────────────┘
                              │
                ┌─────────────────────────┐
                │  Context Transport      │
                │  Primitives             │
                │  (Shared Memory & IPC)  │
                └─────────────────────────┘

Installation

Pip (recommended)

The pip wheel ships a portable, self-contained build with all dependencies statically linked. No system installs are required beyond glibc and Python 3.10+.

pip install iowarp-core

The wheel includes the CLIO runtime, the clio_run CLI, the CTE, CAE, and CEE engines, and the clio_cee Python bindings. A default configuration is seeded at ~/.clio/clio.yaml on first import.

Switch to a source build below if you need any of:

• NVIDIA GPU (CUDA) or AMD GPU (ROCm) acceleration
• MPI for distributed multi-node deployment
• HDF5 / ADIOS2 adapters
• FUSE adapter
• Compression backends (LibPressio, Blosc, etc.)
• Custom ChiMods built against the C++ headers
• Sanitizer or debug builds

Source build (Conda)

git clone --recurse-submodules https://github.com/iowarp/clio-core.git
cd clio-core
bash install.sh release

release corresponds to a variant under installers/conda/variants/. Other variants (cuda, rocm, mpi, full, release-fuse, debug, ...) enable the corresponding features.

For a full bare-metal source build (without Conda) with the per-feature apt / dnf dependency list and the complete CLIO_*_ENABLE_* flag checklist, see INSTALL.md. Other methods (Docker, Spack) are documented in docs/getting-started/installation.

Components

Component	Location	Purpose
Context Transport Primitives	context-transport-primitives/	Shared-memory containers, allocators, sync primitives, networking (ZMQ / libfabric / Thallium). GPU-aware (CUDA, ROCm).
CLIO Runtime	context-runtime/	Coroutine-based modular runtime (< 10 µs task latency). Hosts ChiMods and provides admin + bdev modules.
Context Transfer Engine	context-transfer-engine/	Multi-tiered, heterogeneous-aware I/O buffering. Tag + blob storage with adapters for POSIX, HDF5 (VFD/VOL), ADIOS2, MPI-IO, FUSE3, GDS.
Context Assimilation Engine	context-assimilation-engine/	OMNI-YAML-driven data ingestion (binary, HDF5, Globus) into CTE.
Context Exploration Engine	context-exploration-engine/	High-level C++ and Python (clio_cee) API for bundling, querying, and retrieving data. Includes an MCP server for AI agents.

Quickstart

Start the runtime

Installation seeds a default configuration at ~/.clio/clio.yaml, so the runtime works out of the box:

clio_run start          # foreground
clio_run start &        # background

To override the configuration, point CLIO_SERVER_CONF at your own YAML file:

export CLIO_SERVER_CONF=/path/to/my_config.yaml
clio_run start

Python: bundle, query, retrieve

import clio_cee as cee

ctx_interface = cee.ContextInterface()

# Assimilate a file into IOWarp storage.
ctx = cee.AssimilationCtx(
    src="file::/path/to/data.bin",
    dst="iowarp::my_dataset",
    format="binary",
)
ctx_interface.context_bundle([ctx])

# Query for blob names matching a regex.
blobs = ctx_interface.context_query("my_dataset", ".*", 0)

# Retrieve blob payloads.
data = ctx_interface.context_retrieve("my_dataset", ".*", 0)

# Clean up.
ctx_interface.context_destroy(["my_dataset"])

C++ (CTE, direct)

For direct CTE put/get from C++, see the canonical example and operation reference in the Context Transfer Engine README.

Testing

cd build/release
ctest -VV                    # all unit tests
ctest -R context_transport   # CTP tests
ctest -R runtime             # runtime tests
ctest -R cte                 # CTE tests
ctest -R omni                # CAE tests
ctest -R context             # CEE tests

Benchmarking

CLIO Core ships two main benchmarks; pass --help to either for the full parameter list.

• clio_run_thrpt_bench — runtime task throughput and latency (bdev_io, bdev_allocation, bdev_task_alloc, latency test cases).
• clio_cte_bench — CTE Put / Get / PutGet throughput across threads, async depth, I/O size, and key-space cardinality.

Example:

clio_run_thrpt_bench --test-case bdev_io --threads 8 --duration 30
clio_cte_bench       --op PutGet --threads 8 --depth 16 --io-size 1m --io-count 200

Documentation

• AGENTS.md — unified development guide and coding standards.
• INSTALL.md — bare-metal source-build instructions.
• Context Transport Primitives
• CLIO Runtime
• Context Transfer Engine — canonical C++ CTE API reference.
• Context Assimilation Engine
• Context Exploration Engine
• Full documentation site: https://grc.iit.edu/docs/category/iowarp

Use Cases

Scientific computing: data processing pipelines, near-data computing, custom storage engines, workflows with context management.

Storage systems: distributed file system backends, object storage, multi-tiered caches, high-throughput I/O buffering.

HPC and data-intensive workloads: accelerated I/O, ingestion and transformation pipelines, heterogeneous computing with GPU support, real-time streaming analytics.

Performance Characteristics

• Task latency: < 10 µs for local task execution.
• Memory bandwidth: up to 50 GB/s with the RAM bdev backend.
• Scalability: single node to multi-node clusters.
• Concurrency: thousands of concurrent coroutine-based tasks.

Contributing

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/amazing-feature.
3. Follow the standards in AGENTS.md.
4. ctest --test-dir build/release before opening a PR.
5. Submit a pull request against iowarp/clio-core.

License

CLIO Core is licensed under the BSD 3-Clause License. See LICENSE for the full text.

Copyright (c) 2024, Gnosis Research Center, Illinois Institute of Technology

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Acknowledgements

CLIO Core is developed at the GRC lab at Illinois Institute of Technology as part of the IOWarp project, supported by the National Science Foundation (NSF) to advance next-generation scientific computing infrastructure.

• IOWarp project: https://grc.iit.edu/research/projects/iowarp
• IOWarp organization: https://github.com/iowarp
• Documentation hub: https://grc.iit.edu/docs/category/iowarp