PDF Large data anlalysis and visualisation

Large-Scale Data Visualization Using

Parallel Data Streaming

James Ahrens and Kristi Brislawn, Los Alamos National Laboratory.

Ken Martin, Berk Geveci, and C. Charles Law Kitware. Michael Papka Argonne National Laboratory.

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● In engineering and product design, Computer simulation continues to replace physical prototypes, resulting in reduced design cycle times and costs.

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Why Computer Simulation ?

Agenda

● Why parallel Computing ?

● Why Data Streaming ?

● How Does It Work ?

● Mixed Topologies

● Supporting Parallelism

● Sort-Last Parallel Rendering

● Result

● Conclusion

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Glossary :

Visualization Pipeline : Streamlines :

Message Passing Interfaces (MPI’s) : Data Streaming :

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Visualization pipeline :

★ The visualization pipeline describes the way from data acquisition to picture generation

★ The role of the visualization pipeline is to transform information into graphical data

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Streamlines :

A streamline is a line that is tangent to the velocity vector at every point

Eg. Streamlines in the astrophysics dataset seeded outside the

proto-neutron star illustrate the nature of the complex magnetic field inside the supernova shock front

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Message Passing interface (MPI) :

Message Passing Interface (MPI) is a standardized and portable

message-passing system developed for distributed and parallel computing

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Why Parallel Computing ?

● In the big data era, datasets can grow in enormous sizes and therefore it may be impossible to load them into a single machine.

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In parallel computing, such datasets can take advantage of multiple computer machines in order to load them in a distributed fashion, by partitioning them.

Systems Supporting Parallel Computing :

1. Open Data Explorer(OpenDX)

2. Application Visualization System(AVS) 3. Demand Driven Visualizer(DDV)

4. SCIRun

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These Systems Provide a pipeline infrastructure and can support Parallel execution

OpenDX and AVS

● OpenDX(Formerly IBM Data Explorer) and AVS are Dataflow-based visualization systems,providing numerous visualization and analysis algorithms for their users

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● Both Systems’ architectures rely on a centralized executive to some degree to initiate modules, allocate memory, and execute modules.

SCIRun

● SCIRun is a dataflow-based simulation and visualization system that supports interactive computational steering.

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● SCIRun provides threaded-task and data parallelism on shared-memory multiprocessors.

● An extension to SCIRun permits distributed-memory task parallelism.

DDV (Demand Driven Visualizer)

● DDV provides a pipeline-based, demand-driven execution model that handles large data sets by requesting only the minimum amount of data required to produce the results.

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● This is a significant advantage for data sets with a large number of stored or computed fields.

Look’s good but…

● All the approaches we describe here lack the ability to stream data in memory when the data set topologies change.

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● Because many visualization techniques can change the data's topology, this is an important consideration.

Streaming Data :

Streaming data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes

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Why data streaming ?

Streaming Data through a Visualization pipeline offers two main benefits :

❏ Run Visualization Data that wouldn’t normally fit into memory

❏ Run Visualizations with a smaller memory footprint resulting in higher cache hits and little swapping to disk.

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Advantages of Data Streaming :

➔ Real-Time Insights

➔ Scalability

Streaming data allows for real-time processing and analysis

This makes it possible to scale up the visualization pipeline to handle increasing amounts of data as needed

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Advantages of Data Streaming :

➔ Flexibility

➔ Interactivity

A visualization pipeline can be designed to handle a wide variety of data types and formats

Streaming data through a

visualization pipeline enables the creation of interactive

visualizations that allow users to explore the data in real-time

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Condition for Data Streaming :

The Data Set Should Be : Separable

Mappable

Result Invariant

The algorithm must be able to break the dataset into pieces efficiently

We must be able to determine what portion of the input data we need to generate a given portion of the output

The result has to be independent of the number of pieces the data

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Going smooth ?

What About Streaming UNSTRUCTURED DATA

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Mixed Topologies :

With regularly sampled volumetric data, such as images, we can use an extent defined as (imin, imax, jmin, jmax, kmin, kmax), but this doesn’t work for unstructured data

One approach is to use a geometric extent, such as (xmin, xmax, ymin, ymax, zmin, zmax), where each coordinate represents a point in the space that bounds the data

However, determining these bounding coordinates can be computationally expensive

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Unstructured and Curvilinear

Addressing these Challenges Becomes even more intricate when dealing with datasets that are both unstructured and Curvilinear

Curvilinear Data Structured Data Unstructured Data

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Unstructured extent

A more Practical approach is defining an Unstructured extent as M out of N Possible Pieces, we split the dataset into N pieces but we don’t have any control over what cells a piece will contain

This raises an issue of how to support unstructured algorithms that require neighborhood information…..

K-Nearest Neighbor (K-NN) Algorithm

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K-Nearest Neighbors Algorithm

This Assigns the new Data to a Class or Category Based on it’s K nearest neighbors

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Ghost cells

● Imagine you have a dataset that's organized as an unstructured grid, where the data points are connected in a way that doesn't follow a regular pattern like a grid of squares or cubes.

● Now, some algorithms need more information than just the main data points.

They might need "ghost cells," which are extra cells that surround the main cells. These ghost cells provide extra context for calculations and analyses.

They're like a buffer zone that helps algorithms work better.

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Ghost Cells:

So the Solution is that we use Ghost cells which are included when algorithms require it i.e all unstructured grid data should be capable of producing ghost cells.

● Points on the edge between different sections can end up being counted twice, leading to duplicated shapes when processed.

● To solve this, we indicate which points in an extent are owned by that extent versus the ones that are ghost points

Breaking up a sphere into a piece (red) and ghost-level cells and points

(blue and green) ²⁵

Conversion Between Structured and Unstructured Data :

1. Structured Data To Unstructured Data

2. Unstructured Data To structured Data

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Structured to Unstructured Data

For most Operations that take in Structured and produces Unstructured Data, The architecture can use Block-Based Division to divide Structured Data into pieces Until There are N pieces as Requested

Structured Data Unstructured Data

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Unstructured to Structured Data

We can convert it but it’s inappropriate for most of algorithms that convert unstructured data to structured data.

To convert unstructured grid to structured grid a structured input is needed to define the geometry of desired output.

This conversion process involves resampling the Data from unstructured grid onto geometry of structured input.

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Supporting Parallelism

❖ Most large-scale simulations use parallel processing and often results are distributed across many processing Nodes.

❖ Parallelism requires some of these Conditions :- Data Separability

Result Invariance

Asynchronous Execution Collection

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Reason for asynchronous execution :

We require asynchronous execution so that one process isn’t unnecessarily blocked, waiting for input from another process.

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Asynchronous execution :

● A naive approach would be to simply ask each input to generate its data in order. The

problem is that while Filter 3 is waiting for Filter 1 to compute its data, Filter 2 is idle

● Modifications done are : Trigger

Asynchronous Update - essentially, this method traverses upstream in the pipeline, and when it encounters a port, the port calls Update data on its input

● And second is to use locality of the input to determine in what order to invoke Update data on them.

Locality = 1 ,if input is generated in the same process = 0 ,if input is generated in a different process = X ,x ∈ (0, 1) if input is partially generated in Different processes

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Data-parallel visualization example and image resulting from it

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Encapsulation of Initialization and Communication

● Process initialization and communication tasks have been encapsulated into a class. So that this shields users from dealing with low-level

initialization and communication details

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Concrete Subclasses for Different Process Types :

● Concrete subclasses have been created for distributed-memory and shared-memory processes.

● MPI (Message Passing Interface) and pthreads are used for distributed-memory and shared-memory parallelism.

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Sort-Last Parallel Rendering :

Sort-Last Parallel Rendering is a technique used in computer graphics to render large data sets on tile displays

➔ It is a type of parallel rendering, which is the application of parallel programming to the computational domain of computer graphics.

➔ Interprocess communication is utilized to gather parallel renderings and composite them into a final image.

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Centralized Rendering :

● For centralized rendering, polygonal data is collected using ports between

processes, are connected to an append filter in the collection process

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Result

● The results here are based on using an in-memory

analytic function as a data source.We organised the data as a regular volumetric data

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● This also avoids dealing with the issues of massively parallel I/O, which are beyond the scope of this article

Result :

● The initial visualization example involves a data-parallel pipeline performing tasks like computing an isosurface and gradient

magnitude is then applied as colors to the isosurface using a probe filter, and the result is rendered using a parallel rendering

technique

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●

The tests were conducted on data sizes of 39 Gbytes, 1.1 Tbytes and

0.9 Pbytes utilizing processor configurations ranging from 1 to 1,024

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● Results are measured in terms of efficiency vs. processor count.

Results of a 39-Gbyte data-parallel visualization.

● It is worth noting that it took 360,000 seconds for 0.9 Pbyte run on 1,024 processors

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● The second visualization example demonstrates task parallelism, where there are multiple independent visualization pipelines and each performing different tasks in an asynchronous manner

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● The first pipeline is the

isosurface pipeline that we used in the first example

● The second pipeline computes a gradient vector field from the input data

● The third pipeline extracts a cut from the input data and displays it

Conclusions

● In many simulations with distributed data, the ghost cells can only be obtained from other processes. Currently, there isn’t a standard mechanism for one

process to determine where to find specific ghost cells

● Certain algorithms like streamlines need special versions for parallel

processing to handle situations where the streamline crosses from one data section to another and keep track of this transition.

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