An open-source package of scalable building blocks for data movement tailored to the needs of large-scale parallel analysis workloads

Installation (Linux, Mac, IBM and Cray supercomputers):

Download DIY with the following command:

```
git clone https://github.com/diatomic/diy
```

and follow the instructions in the README.

Documentation can be found here.

Description:

Scalable, parallel analysis of data-intensive computational science relies on the decomposition
of the analysis problem among a large number of data-parallel subproblems, the efficient data
exchange among them, and data transport between them and the memory/storage hierarchy. The
abstraction enabling these capabilities is block-based parallelism; blocks and their message
queues are mapped onto processing elements (MPI processes or threads) and are migrated between
memory and storage by the DIY runtime. Configurable data partitioning, scalable data exchange,
and efficient parallel I/O are the main components of DIY. The current version of DIY has been
completely rewritten to support distributed- and shared-memory parallel algorithms that can run
both in- and out-of-core with the same code. The same program can be executed with one or more
threads per MPI process and with one or more data blocks resident in main memory.
Computational scientists, data analysis researchers, and visualization tool builders can all
benefit from these tools.

Above: The DIY software structure. The master manages block placement in the memory/storage hieararchy and executes the block operations with one or more threads. The decomposer creates blocks with regularly defined decomposition patterns, and the assigner maps one or more blocks to an MPI process. Communication occurs between blocks using regular patterns. The I/O module provides collective and independent block I/O. Common tasks are provided by a set of ready-to-use algorithms.

DIY has demonstrated strong scaling out to 512K processes on existing machines in a diverse array of science and analysis codes, including cosmology, molecular dynamics, nuclear engineering, co-design, astrophysics, combustion, and synchrotron light source imaging. The figure above shows a benchmark of strong and weak scaling of parallel Delaunay tessellations, one of the libraries built on top of DIY. [SC14 paper]

More information and citing DIY:

Please refer to our LDAV'11 paper,

pdf bibtex

and our LDAV'16 paper,

pdf bibtex

Authors:

DIY is a collaboration between Tom Peterka of Argonne National Laboratory and Dmitriy Morozov of Lawrence Berkeley National Laboratory.

An open-source package for parallelizing Delaunay and Voronoi tessellation over
distributed-memory HPC architecture

Installation (Linux, Mac, IBM and Cray supercomputers):

Download Tess with the following command:

```
git clone https://github.com/diatomic/tess2
```

and follow the instructions in the README.

Description:

Computing a Voronoi or Delaunay tessellation from a set of points is a core part
of the analysis of many simulated and measured datasets: N-body simulations,
molecular dynamics codes, and LIDAR point clouds are just a few examples. Such
computational geometry methods are common in data analysis and visualization;
but as the scale of simulations and observations surpasses billions of
particles, the existing serial and shared-memory algorithms no longer suffice.
A distributed-memory scalable parallel algorithm is the only feasible approach.
Tess is a parallel Delaunay and Voronoi
tessellation algorithm that automatically determines which neighbor points need
to be exchanged among the subdomains of a spatial decomposition.
Computing tessellations at scale performs poorly when
the input data is unbalanced, which is why Tess uses k-d trees to evenly
distribute points among processes. The
running times are up to 100 times faster using k-d tree compared with regular
grid decomposition. Moreover, in unbalanced data sets, decomposing the
domain into a k-d tree is up to five times faster than decomposing it into a
regular grid.

Above: Cosmology simulations are a prime example of severe load imbalance, as dark matter particles cluster into halos and voids whose densities can vary by six orders of magnitude. The strong scaling of parallel Delaunay tessellation improves by approximately 100X by decomposing blocks in a DIY k-d tree versus a regular grid.

More information and citing Tess:

Please refer to our SC14 paper,

pdf bibtex

and our SC16 paper,

pdf bibtex

Authors:

Tess is a collaboration between Tom Peterka of Argonne National Laboratory and Dmitriy Morozov of Lawrence Berkeley National Laboratory.

An open-source package of dataflow communication for in situ HPC data analysis
workflows

Installation (Linux, Mac, IBM and Cray supercomputers):

Download Decaf with the following command:

```
git clone https://github.com/tpeterka/decaf
```

and follow the instructions in the README.

Description:

Decaf is a dataflow system for the parallel communication of coupled tasks in
an HPC workflow. The dataflow can perform arbitrary data transformations ranging
from simply forwarding data to complex data redistribution. Decaf does this by
allowing the user to allocate resources and execute custom code in the dataflow.
All communication through the dataflow is efficient parallel message passing
over MPI. The runtime for calling tasks is entirely message-driven; Decaf
executes a task when all messages for the task have been received. Such a
message-driven runtime allows cyclic task dependencies in the workflow graph,
for example, to enact computational steering based on the result of downstream
tasks. Decaf includes a simple Python API for describing the workflow graph.
This allows Decaf to stand alone as a complete workflow system, but Decaf
can also be used as the dataflow layer by one or more other workflow systems
to form a heterogeneous task-based computing environment.

Above: The Decaf software structure. Decaf is a dataflow middleware for coupling HPC simulation and analytics codes. Decaf includes distributed and configurable dataflow links, standard data redistribution patterns, flow control, and automatic data filtering.

Decaf has demonstrated strong scaling out to 1280 processes on existing machines in numerous science and analysis codes, including cosmology, molecular dynamics, and light source imaging. The figure above shows a benchmark of strong scaling of an HPC workflow consisting of computational cosmology, parallel Voronoi tessellation, and parallel density estimation of gravitational lensing due to dark matter.

More information and citing Decaf:

Please refer to our ISAV'15 paper,

pdf bibtex

and our Cluster'16 paper,

pdf bibtex

Authors:

Decaf is a collaboration between Tom Peterka and Orcun Yildiz of Argonne National Laboratory and Matthieu Dreher formerly of Argonne National Laboratory.

An open-source package for modeling scientific data with functional approximations based on high-dimensional
multivariate B-spline and NURBS bases

Installation (Linux, Mac, IBM and Cray supercomputers):

Download MFA with the following command:

```
git clone https://github.com/tpeterka/mfa
```

and follow the instructions in the README.

Description:

Scientific data may be transformed by recasting to a fundamentally different kind of data model than the discrete
point-wise or element-wise datasets produced by computational models. In Multivariate Functional Approximation, or MFA,
scientific datasets are redefined in a hypervolume of piecewise-continuous basis functions. Compared with existing
discrete models, the continuous functional model can save space while affording many of the same spatiotemporal analyses
without reverting back to the discrete form. The MFA model can represent numerous types of data because it is agnostic
to the mesh, field, or discretization of the input dataset. Compared with existing discrete data models, the MFA model
can enable many spatiotemporal analyses, without converting the entire dataset back to the original discrete form. The
MFA often occupies less storage space than the original discrete data, providing some data reduction, depending on data
complexity and intended usage. For example, noise may be intentionally smoothed using a small number of control points
and high-degree basis functions; alternatively, high-frequency data features may be preserved with more control points
and lower degree. Post hoc, the MFA enables analytical closed-form evaluation of points and derivatives, to high order,
anywhere inside the domain, without being limited to the locations of the input data points.

Above: Basis functions and formulation of 1-d curves at top and extension to n-d tensor products below.

Above: MFA has been applied to the modeling of various scientific datasets and scaled on parallel clusters and supercomputers.

More information and citing MFA:

Please refer to our LDAV'18 paper,

pdf bibtex

our Cluster'19 paper

pdf bibtex

our ICCS'19 paper

pdf bibtex

or our CAD'20 paper

pdf bibtex

Authors:

MFA is a collaboration between Tom Peterka, David Lenz, Iulian Grindeanu, Vijay Mahadevan of Argonne National Laboratory; Youssef Nashed formerly of Argonne National Laboratory, and Raine Yeh and Xavier Tricoche of Purdue University.