E4S Data & Visualization
The Ecosystem for Scientific Software (E4S) provides a comprehensive collection of open-source libraries and tools for scientific computing, including those focused on data management, input/output (I/O), and visualization. These components are essential for efficiently handling, storing, transferring, and visualizing large-scale simulation and experimental datasets on modern high-performance computing (HPC) platforms.
E4S includes an extensive set of interoperable, performance-portable tools that help applications manage data efficiently and visualize results at scale. These tools support parallel I/O, adaptive data formats, in situ visualization, and interoperability across programming environments and hardware architectures.
The Value of Portable and Scalable Data & Visualization Libraries
As HPC systems evolve toward exascale and beyond, scientific applications generate increasingly large and complex datasets. Portable and scalable data and visualization libraries help users manage this complexity by providing consistent APIs and optimized performance across diverse architectures.
These tools enable scientists and engineers to:
- Write and read large data sets efficiently in parallel, reducing I/O bottlenecks.
- Use standardized data formats (like HDF5, ADIOS2, and PNetCDF) that enable data sharing across applications and institutions.
- Integrate in situ and in transit visualization workflows to analyze results as they are generated, reducing storage requirements.
- Utilize vendor-optimized backends and libraries that exploit GPU acceleration and high-speed interconnects.
By adopting E4S-supported data and visualization tools, applications gain access to proven, community-maintained solutions that improve scalability, reduce redundant effort, and increase scientific reproducibility.
I/O Libraries and Tools
These libraries provide high-performance interfaces for reading and writing data, offering various data models, compression schemes, and optimizations for parallel file systems and hierarchical storage architectures.
| Library | Description | Key Features | Typical Use |
|---|---|---|---|
| HDF5 | Hierarchical Data Format version 5 for structured, portable data storage | Parallel I/O, compression, multi-language APIs | General-purpose scientific data storage |
| ADIOS2 | Adaptable Input/Output System for large-scale data workflows | Streaming I/O, in situ and in transit data movement | Simulation data management and coupling |
| Parallel NetCDF (PNetCDF) | Parallel version of NetCDF classic and 64-bit formats | MPI-IO based, lightweight metadata | Structured gridded datasets |
| Silo | Multi-domain scientific data I/O library | Rich mesh and field abstractions | Visualization and mesh data |
| Exodus | Finite element model data format | Parallel and serial I/O, metadata-rich | Engineering simulation data |
| UnifyCR | User-level file system optimized for burst buffers | Shared namespace, aggregation | Node-local checkpointing |
| SZ | Error-bounded lossy compression framework | GPU-enabled compression, high ratios | Data reduction for large outputs |
Visualization Libraries and Tools
These libraries and tools support visualization, analysis, and rendering of large scientific datasets, including in situ and post hoc visualization workflows.
| Library | Description | Key Features | Typical Use |
|---|---|---|---|
| VTK | Visualization Toolkit for 3D computer graphics, image processing, and visualization | Extensive filters, rendering engines | Custom visualization pipelines |
| ParaView | Scalable parallel visualization application built on VTK | Client-server rendering, in situ coupling | Interactive and batch visualization |
| VisIt | Parallel visualization and analysis tool | Multiple input formats, database plugins | Multi-variable field visualization |
| Ascent | Lightweight in situ visualization and analysis framework | MPI integration, Python/Lua interfaces | In situ analysis during simulation |
| Catalyst | In situ library version of ParaView | Co-processing pipelines, embedded use | Coupled simulation-visualization |
| Cinema | Lightweight, portable visualization data model | Image-based exploration, metadata indexing | Scalable visual data curation |
| yt | Python-based analysis and visualization library | Domain-specific analysis (astrophysics, CFD) | Scripted data exploration |
Vendor-Provided Tools
Hardware vendors provide optimized I/O, data management, and visualization tools designed to leverage specific architectures, such as GPUs or high-bandwidth interconnects.
| Vendor Tool | Vendor | Description | Key Capabilities |
|---|---|---|---|
| NVIDIA IndeX | NVIDIA | GPU-accelerated volume visualization and rendering engine | Distributed rendering, HPC integration |
| NVIDIA Nsight Systems | NVIDIA | Performance analysis and visualization of system-level workloads | Profiling and visualization of I/O and GPU behavior |
| Intel VTune Profiler | Intel | Performance analysis and data access visualization tool | Threading and memory bandwidth visualization |
| Intel oneAPI I/O Toolkit | Intel | Optimized I/O and storage management for exascale | Integration with DAOS and distributed memory systems |
| AMD uProf | AMD | Performance and I/O profiling for AMD CPUs/GPUs | Application performance visualization |
| ARM Forge | ARM | Performance analysis and visualization suite | Scalable profiling, I/O bottleneck visualization |
Summary
E4S integrates a diverse and powerful collection of data and visualization libraries that work across system architectures and scales. Together, these tools support the entire data lifecycle—from acquisition and storage to interactive analysis and visual exploration. By leveraging E4S, developers and researchers can focus on their science while relying on a well-supported, interoperable ecosystem of tools optimized for modern supercomputers.