By : Veenus A V, Associate GM & Lead NeST-NVIDIA Center for GPU computing, Trivandrum, India

Office: NeST/SFO Technologies, San Jose, CA, www.nestsoftware.com

veenusav @ gmail. com

“ Do not simply believe in anything because

you have heard it.

› No matter that if I

have told it !

Believe only after you observe and

analyze. ”

Reference: Anguttara Nikaya, Vol 1, 188-193

Sri Buddha

Application Architecture policies

› Scientific Visualization

Software blends with the platform

Demands of modern users

Proof of Concept >> Product

We are showing a few technical

experiments for your understanding.

Not a PRODUCT demonstration!

Pre Solver Post

The data structures are plain to process

› May be a few arrays. An under graduate

can understand all these in plain form.

Graphics is not that vast

› Compared to a typical game, it is a simple

deal. Na?!!

A bit serious results – Users will adjust!

.. Let me explain about our background

before continuing ..

It will reveal the way how we

are proceeding so…!

We make

specific

software

solutions for

your scientific

needs.

We are specialized in engineering software development.

NeST-NVIDIA center for GPU computing

› Lab specifically for GPU based technologies

› Inaugurated by Dr. Bill Dally –chief scientist NVIDIA

How to architect the software for your

futuristic hardware and software..

Proof-of-concept to Product

Not giving emphasis on:

› Features of the applications

› Algorithms

Pre Solver Post

In focus: Scientific data visualization

`

Multi physics

Solver

Outer

surfaces Volume

(v or thd)

Results

Volume

(tensor)

Shapes

and

geometry Display

Frame

(image) Analysis model

(boundary & other

params)

`

Multi physics

Solver

Outer

surfaces Volume

(v or thd)

Results

Volume

(tensor)

Known model

(Expert

system db

some cases)

Historical

Experiment

Results

For eg: inverse modeling process

Shapes

and

geometry Display

Frame

(image) Analysis model

(boundary & other

params)

Workstation PC

Multi physics

Solver

Outer

surfaces Volume

tetra

hedron

Known model

(Expert

system db

some cases)

Historical

Experiment

Results

For eg: inverse modeling process

Shapes

and

geometry

PC Display

Frame 1920 x 1080

48.8 KB

591 MB

5.6 GB

154.6GB x 10

5.93 MB

Tablet Display

Frame 1080 X 720

2.22 MB

Results

Volume

2.5 TB 3.2 GB

Workstation PC

CPU

Cores

CPU

RAM

HDD

SSD

SATA

DDR3 PCI Express

Interface Mother board

Bus

GIGABIT

Ethernet

Interface

GPU

Memory

(Global)

GDDR5

GPU

Cache

(2D)

GPU

Cor

es

GPU

Cor

es

GPU

Cor

es

(Shared Memory)

Fast local

Network Intranet User

(Tablet)

Internet Remote User

(Tablet or

Browser)

Global

Memory

Texture Memory

10GB/s

340 MB/s

12 GB/s

5.3 GB/s

42GB/s

350~550 MB/s

70 ~130 MB/s

Algorithms are good.

Mathematics doing fine for centuries…

› Newton’s laws, Maxwell's equations still hold

good.

Proof of concepts might be the best the

world!

The data structures are plain to process

› May be a few arrays. An under graduate

can understand all these at plain form.

Graphics is not that vast

› Compared to a typical game, it is a simple

deal. Na?!!

A bit serious results – Users will adjust!

A popular myth – pci express cannot

give data to monitor.. !

PCIExpress can give good frame rate if

your data is ready in CPU memory

› A lot of points like when you closely watch

the platform facts..

› GPU for FLOPS only…

Multi physics

Solver

Outer

surfaces Volume

tetra

hedron

Known model

(Expert

system db

some cases)

Historical

Experiment

Results

For eg: inverse modeling process

Shapes

and

geometry

PC Display

Frame 1920 x 1080

48.8 KB

591 MB

5.6 GB

154.6GB

5.93 MB

Tablet Display

Frame 1080 X 720

2.22 MB

Results

Volume

2.5 TB 3.2 GB

GPU means - More FLOPS/$, FLOPS/real-estate.

Use GLSL for graphics (SH 5.0 gives you freedom of mesh quality too!)

CUDA syntax is simple, do data flow analysis for maximum throughput

But don’t forget to juice your CPU too!

Offline processing before graphics viewer › Even letting your user to have a coffee before he

starts to analysis.!

› Extra data - Mind HDD space and transfer rate

Spatially order data › viewer will seek like that.

› Processor wait means DELAY! 2D locality of reference

Make an LoD arrangement › User want response not ‘details’ always!

Maximum parallelism, WARP full, threads > cores

Only compute for the device and screen. › Higher resolution is not always needed.

› User wants responsive software

› Pixel shader is your time eater.. Resolution of RT

› GPU utilized for other compute, do these based on real response metrics. Do 2D bicubic instead.

› Texelize .. Texelize….

Read-only data, a knowledge that gives

freedom for GPU cache…

› Use asynchronous system at the maximum

Processor is not the only ‘active’ component in

the board!

Use streams of CUDA or switching of textures…

Its time of BOYD

Do watch software systems on specific

platforms

› For googling: Kepler grid, cloudgaming

Volume viewer – voxelized data

Geometric Editor – Mesh can be perfect!

Preparation for solver - inverse modeling

with GPU (only platform work)

Remote visualization for post processor

Video

Volume resolution and dimensions

› Avoid empty spaces

› Bricking,

› Compression

Quality Graphics demanded

› Phong SM

Video

Algorithm based on Laplacian

The operations involved is as follows. Select a ROI in the mesh on the screen

Draw a sketch on the screen suggesting a edited region of mesh

The model will be reshaped to fit the curve but still retaining the shape.

2D edge tracking to 3D was a challenge

Used modified form of classic algorithms

of CPU.

› In GPU was difficult

› Created regular triangles on the fly to give

neat result

Same area. So isosceles or equilateral

To make the model, real world data used

Huge data inputs › Point cloud, volumetric, high data rate

Inverse modeling techniques used by preparatory algorithm

SVD to avoid non-significant information

Challenge – partial volume correlation

Volume division optimized for maximum threads in gpu and MPI

Model the control flow (limit) as per the locality heuristics (expert system with direction vectors)

Always handle border separate(good for processor)

Each module may not be that fast..! › Win war.. Not every battle…!

Users demand BOYD

Not all features – but subset

KEPLER GRID most awaiting hardware

Features › Html 5 client

› Stream based server

› LoD based RayCaster viewer TO Nvidia iRay

› Serviced on a GPU cluster

Challenge › Time-to-market: Conversion of existing engine

› Multi user support and faster data speed

Proof-of-concept level complexities - Algorithm level research

Development process – How to manage projects which involves scientific stuff and new platform challenges.

Test automation architecture

Deployment scenarios and hardware tune-up at the final level (it is a fact always!)

Remember Kalama Sutta…

› Your questions may transform my

thinking…

Please ask even after the session veenusav@gmail.com

www.nestsoftware.com

Do write to us on technical and business

queries.

› Speaker: veenusav @ gmail.com

› Website: www.nestsoftware.com

› Business queries: hpc@nestgroup.net