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Interactive Parallel Data Visualization and Exploration March 27, 2013 Mihai Budiu Moises Goldszmidt Jean-Philippe Martin Mark Manasse Alex Andoni Gordon Plotkin Qingzhuo Luo (intern 2012)

Interactive Parallel Data Visualization and Exploration

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Interactive Parallel Data Visualization and Exploration. March 27, 2013 Mihai Budiu Moises Goldszmidt Jean-Philippe Martin Mark Manasse Alex Andoni Gordon Plotkin Qingzhuo Luo (intern 2012 ). Cluster-Based Visualization. Data. RMI. Partial renderings. Rendering overlay. - PowerPoint PPT Presentation

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Page 1: Interactive  Parallel Data  Visualization and  Exploration

Interactive Parallel Data Visualization and Exploration

March 27, 2013

Mihai Budiu

Moises GoldszmidtJean-Philippe Martin

Mark ManasseAlex Andoni

Gordon PlotkinQingzhuo Luo (intern 2012)

Page 2: Interactive  Parallel Data  Visualization and  Exploration

Cluster-Based Visualization

Client GUI

Server workers

RMI

Partial renderings

Multi-coredata

extraction,transformation

rendering

Renderingoverlay

Data

Page 3: Interactive  Parallel Data  Visualization and  Exploration

Data size

Client GUI

Server workers

RMI

Partial renderings

Multi-coredata

extraction,transformation

rendering

Renderingoverlay

O(1)

O(1)

O(n)

~1MPx = O(1)

Page 4: Interactive  Parallel Data  Visualization and  Exploration

Composable Interfaces

IViewIView

IView IView

IViewIView

IView

LocalView LocalView

Server 1

ParallelView

ProxyView ProxyView

ParallelViewIView

IView

LocalView LocalView

Server 2

ParallelViewIView

Client

LocalViewIView

LocalView

Cluster-levelparallelism

Multi-core-levelparallelism

Page 5: Interactive  Parallel Data  Visualization and  Exploration

Computing “Small” Results

IDistributionIDistribution

IDistribution

LocalDistr LocalDistr

ServerParallelDistr

ProxyDistr

ParallelDistrIDistribution

IDistribution

Client

Plot

Plot

Plot PlotImage Image

Image

Image

Image

overlay

overlay

Result sizeindependent of the data size

Plot

Page 6: Interactive  Parallel Data  Visualization and  Exploration

Computing “Big” Results

IDistributionIDistribution

IDistribution

LocalDistr LocalDistr

ServerParallelDistr

ProxyDistr

ParallelDistrIDistribution

IDistribution

Client

Filter

Filter

Filter Filter

Filter

IDistributionIDistribution

IDistribution

LocalDistr LocalDistr

ParallelDistr

ProxyDistr

ParallelDistrIDistribution

IDistribution

Data nevermoves

result

Page 7: Interactive  Parallel Data  Visualization and  Exploration

A Language of Linear Transformations

• f : A → B, f(a + b) = f(a) + f(b)• Various + operations:– DB Tables, with union– Distributions, with union– Histograms, with pointwise addition– Transparent images with overlays– Timeseries, with union– Multisets, with pointwise addition– Top K values, with mergesort

Page 8: Interactive  Parallel Data  Visualization and  Exploration

Software Stack

Domain-specific App

Object transport

RMI

ILocal

IView IDistribution IHistogram IScatter

Plotting

IParallel IProxy

ITimeSeries IColorMap

Built-in structs (Distribution, View, Histogram, ScatterPlot, TimeSeries, ColorMap)

IPlot

Domain-specificData-structures

client side server side

X

Page 9: Interactive  Parallel Data  Visualization and  Exploration

Running on 700 cores

Page 10: Interactive  Parallel Data  Visualization and  Exploration