# About RHAPSODY

RHAPSODY (<b>R</b>untime for <b>H</b>eterogeneous <b>AP</b>plications, <b>S</b>ervice <b>O</b>rchestration and <b>DY</b>namism) is a cutting-edge runtime system designed to address the evolving challenges of modern scientific computing. As computational workflows become increasingly complex, combining traditional HPC simulations with AI/ML workloads, there is a critical need for systems that can efficiently orchestrate heterogeneous tasks across diverse computing resources.

## Vision

Our vision is to create a unified runtime that seamlessly bridges the gap between traditional high-performance computing and emerging AI/ML paradigms, enabling scientists to focus on their research rather than the complexities of workflow orchestration.

## Core Philosophy

RHAPSODY is built on several key principles:

### Heterogeneity as a First-Class Citizen

Modern scientific workflows are inherently heterogeneous, combining:

- Traditional HPC simulations (CPU-intensive)
- Machine learning training and inference (GPU-intensive)
- Data processing and analysis tasks
- I/O-intensive operations

RHAPSODY treats this heterogeneity as a fundamental characteristic rather than an afterthought.

### Dynamic Adaptation

Scientific workflows are rarely static. RHAPSODY supports:

- Runtime modification of task graphs based on intermediate results
- Adaptive resource allocation
- Dynamic load balancing across heterogeneous resources

### Platform Abstraction

Scientists should not need to be experts in every computing platform. RHAPSODY provides:

- Unified interfaces across different HPC systems
- Abstraction of resource manager specifics
- Portable workflow descriptions

## Technical Innovation

RHAPSODY introduces several technical innovations:

### Pluggable Backend Architecture

The system supports multiple execution backends, each optimized for different scenarios:

- **Concurrent Backend**: For shared-memory parallel execution
- **Dask Backend**: For distributed Python workloads
- **RADICAL-Pilot Backend**: For large-scale HPC execution

### AsyncFlow Integration

Full compatibility with the [AsyncFlow](https://github.com/radical-cybertools/radical.asyncflow) workflow management system, providing:

- Standardized workflow descriptions
- Tool interoperability
- Community ecosystem benefits

### State-of-the-Art Monitoring

Comprehensive monitoring and introspection capabilities for:

- Real-time task execution tracking
- Performance analysis and optimization
- Fault detection and recovery

## Research Impact

RHAPSODY enables breakthrough research in numerous domains:

- **Climate Science**: Coupling atmospheric simulations with ML-based post-processing
- **Materials Science**: Integrating quantum simulations with ML property prediction
- **Computational Biology**: Combining molecular dynamics with deep learning analysis

## Project Timeline

RHAPSODY development follows a structured timeline aligned with NSF project milestones:

- **Phase 1** (2021-2022): Core architecture and backend development
- **Phase 2** (2022-2024): Integration with existing workflow systems
- **Phase 3** (2024-2026): Production deployment and optimization

## Team and Collaboration

RHAPSODY is developed by the [RADICAL Research Team](https://radical.rutgers.edu/) at Rutgers University, in collaboration with:

- DOE National laboratories
- Campus supercomputing centers
- Academic research institutions

## Open Science Commitment

We are committed to open science principles:

- **Open Source**: All code is publicly available under MIT license
- **Open Data**: Benchmarks and performance data are shared publicly
- **Open Standards**: We contribute to community standards and protocols
- **Open Collaboration**: We welcome contributions from the broader community

## Future Directions

Looking ahead, RHAPSODY will continue to evolve to meet emerging needs:

- Enhanced support for quantum computing integration
- Advanced AI/ML workflow optimization
- Edge computing and IoT integration for scientific workflows
- Expanded ecosystem of compatible tools and frameworks
