ICT 1

SINTEF ICT, Applied Mathematics,Heterogeneous Computing Group

Trond HagenGPU Computing Seminar, SINTEF

Oslo, October 23, 2009

Technology for a better society

ICT

Agenda

12:30 Introduction and welcomingTrond Hagen, SINTEF ICT

12:45 The State of the Art in GPU ComputingJon Hjelmervik, SINTEF ICT

13:45 Commercial OverviewChris Butler, NVIDIA

13:55 The NVIDIA Tesla GPU Compute Platform: Hardware, Software Developer Tools, and Support

Timothy Lanfear, NVIDIA

14:30 Coffee, discussions, and mingling

2

ICT 3

Department of Applied Mathematics

The Simulation group in Trondheim focuses on developing robust and efficient numerical methods for computational mechanics and geophysical flows.

The Simulation group in Oslo develops robust and efficient computational methods for subsurface flow (petroleum, CO2, groundwater).

Group for Heterogeneous Computing (Oslo) performs research on heterogeneous computing, many-core and data-stream processing.

Group for Geometry (Oslo) focuses on computational geometry and development of 3D technology for the IT industry.

Group for Optimization (Oslo) develops advanced optimization methods for applications within, among others; transportation, (maritime) logistics, and health care planning.

ICT

Heterogeneous Computing Group

Seven Research Scientists and PhD students.

4

Geo

met

ry

Sim

ulat

ion,

Osl

o

Sim

ulat

ion,

Trd

.

Opt

imiz

atio

n

Heterogeneous Computing

ICT 5

Heterogeneous Computing

ICT 6

Heterogeneous Computer

A heterogeneous computer (HC) is a tightly coupled system of processing units with distinct characteristics. A modern desktop or laptop computer is an example of such a system, as most systems include both a task-parallel, multi-core CPU and one or more data-parallel processors in the form of programmable graphics processing units(GPUs).

CPU (Intel Nehalem) GPU (NVIDIA Fermi)

ICT 7

Heterogeneous Computing

“Heterogeneous computing is the strategy of using multiple types of processing elements within a single workflow, and allowing each to perform the tasks to which it is best suited.”

aka Hybrid Computingaka Accelerated Computing aka General-Purpose Computing using GPUs (GPGPU)aka GPU Computing

ICT 8

Architecture and Algorithm Interaction

The key to performance is to understand the architecture and algorithm interaction.

Need to know how to utilize the computational power of parallel accelerators in modern computer hardware.

Designing compute intensive algorithms for obsolete hardware will most likely give bad performance. (sequential code / single-core CPUs)

ARCHITECTURE ALGORITHMS

ICT

Research Activities in the HC Group

9

ICT

Research Activities

10

View-dependent tessellation Preparation of finite element models

Soliving partial differential equations

ICT

Research Activities contd.

11

Self-intersection detection of NURBS surfacesSilhouette refinement

Registration of medical data Visualization of algebraic surfaces

ICT

Research Activities contd.

12

Navier-Stokes: Fluid dynamics

Inpainting

ICT

Research Activities contd.

13

Water injection in a fluvial reservoir

Volume visualization

Electric activity in a human heart.

ICT

Research Activities contd.

14

Cluster of GPU’sMatlab Interface to the GPU

Linear algebra / load balancing CPU - GPU

ICT

Research Activities contd.

15

Adaptiv tessellationRay-tracing, reservoir

ICT

Research Activities contd.

16

Reservoir SimulationOperator split: Evaluate the pressure on the CPU in parallel with the GPU transport evaluation.

Proposed a model for simulating in corner-point grids on the GPU:

ICT

High-speed Marching Cubes using Histogram Pyramids

Marching cubes on GPUs:CPU: not interactiveGPU: 200 fps

Extraction of iso-surfaces

Based on joint research with Gernot Ziegler, NVIDIA

HPMCOpen source library released soon

17

ICT

Smoothed Particle Hydrodynamics

18

ICT

Shallow-Water Equations

19

ICT

Heterogeneous Computing in the Cloud

20

Web‐based clients Workstations

Heterogeneous computers 

(CPUs &GPUs)

ICT

Thank you

21