Cell/B.E. Servers: A Platform for Real Time Scalable Computing and Visualization

© 2008 IBM Corporation11 July 2008
IBM T.J. Watson Research Center
Cell/B.E. Servers: A Platform for Real Time
Scalable Computing and Visualization
Bruce D’Amora
Emerging Systems Software
Watson Research Center
Yorktown Heights, NY

2
CBE for High Performance Computing and Scalable Visualization | 11 July 2008
© 2008 IBM Corporation
SW
SW
SW
SW
Workstation deployments are
complex, costly and limit
application scalability
Net-
works
Data SW
Data Center Users
Net-
work
Data
Net-
work
Data
Net-
work
Data
Net-
work
Data
Est. TCO: $2000/month per workstation

3
Interest is converging toward remote, virtualized desktops
Compute intensive solutions
– Interactive Graphics and Dynamic Simulation require performance
scalability not achievable on a workstation
Large secure data models
Cost of ownership driving toward centrally
managed application solutions
Collaborative, interactive design review solutions
– between partners; between teams

4
Why now?
Better network connectivity
Pressure to lower costs
Highly replicated worker stations
Globalized teaming
IP & data confidentiality protection afforded by centralization
Changing cost structure
– SW vs. HW, People vs. equipment
Microprocessor design trends: vectorization and parallelization
Applications are being re-written to take advantage of
multi-threaded and ultimately distributed platforms

5
Scalable, high performance graphics and simulation
Interactive or real-time response time which
generally requires
Low latency and high bandwidth communications
I/O (network, storage system, etc.)
memory
The biggest inhibitors to remote scalable
application solutions are the need for:

6
Cell/B.E. is a building block for homogeneous and
heterogeneous HPC platforms
Homogeneous systems may be appropriate
– HPC applications like seismic processing where large batches of data must be
processed by proprietary image processing pipelines
Heterogeneous application design points
– Client/server systems splitting application so compute intensive tasks on
server; GUI on PC/workstation/handheld
– Application components executing on different server nodes
Accelerator strategy becoming more prevalent
– Cell/B.E., Power, Intel mixed server nodes
– PCI-express accelerators such as GPUs

7
IBM® PowerXCell™ 8i Processor Accelerator Board
IBM
PowerXCell
8i Processor
IBM
South
Bridge
Soldered
DDR
Soldered
DDR
PCI E Connector
Ethernet
IBM
South-
Bridge
IBM
PowerXCell
8i Processor
(2.8 GHz)
DDR2
Memory
Bcom
5704
PCI-x
PCI-e
x16
1 Gb E
1 Gb E
PCI-e Form Factor Board
IBM PowerXCell 8i
processor
2.8 GHz
2 or 4 GB DDR2
I/O
– One PCI-e x16 Bus
– Two 1 Gb Ethernet
– 8 General Purpose I/O’s
Software
– IBM SDK V 3.0
– Fedora 7

8
System Z and Cell/B.E. Vision is a ‘Marriage’ of Two
Technologies that Perfectly Complement Each Other
Integrated and/
or networked
Cell/B.E.
Solution Characteristics
– Mission critical functionality
– Significant amounts of data storage
– Significant transactional content
– Computational intense operations
– Critical need for governance, i.e. security,
auditing, compliance, availability
– High potential for integration with other
applications
– Reduced power and space
Examples
– Financial analytics
– Medical Imaging
– Computer Vision
– Bioinformatics
– Real Time Ray Tracing
– Virtual Worlds
Z today Z tomorrow
QS20, QS21,
QS2x
Preserves the same
programming model between
Network & Integrated
Cell Blade
Digital
Media
Financial
Services
Sector
Digital Video
Surveillance
Aerospace
and Defense
Electronic
Design
Automation
Chemicals &
Petroleum
Information Based
Medicine
Courtesy of Hoplon

9
Cell/B.E. plus AMD equals Roadrunner
Cluster of 18 Connected Units (CU)
• 6,912 AMD dual-core Opterons
• 12,960 IBM Cell/B.E. eDP accelerators
• 9.8 teraflops peak (Opteron)
• 1.33 petaflops peak (Cell/B.E. eDP)
• 1PF sustained Linpack
InfiniBand 4x DDR fabric
• all-optical cables
• 384 GB/s (bi-directional)
• 3.45 TB/s (bidirectional)
80 TB aggregate memory
• 28 TB Opteron
• 52 TB Cell/B.E.
• 216 GB/s sustained File System I/O:
• 216x2 10G Ethernets to Panasas
Software
RHEL and Fedora Linux
IBM SDK for Multicore Acceleration
xCAT Cluster Management
• System-wide GbE network
3.9 MW Power:
• 0.35 GF/Watt
Area:
• 296 racks
• 5500 ft2

10
Cell/B.E. Real-time Application Solutions
PC/Workstations,
consoles,
handhelds
Scalable Network BackboneGbE/IB
IB/GbE
BC-H
IBM TotalStorage
IBMQS2x
IBMQS2x
IBMQS2x
IBMHS2x
IBMQS2x
IBMQS2x
IBMQS2x
IBMQS2x
IBMQS2x
IBMHS2x
IBMQS2x
IBMQS2x
IBMQS2x
IBMQS2x
IBMQS2x
IBMHS2x
IBMQS2x
IBMQS2x
Online Gaming and Rendering
BladeCenter H chassis rack mounted, 4 per rack
Heterogeneous solutions with Cell/B.E. and Intel blade servers
GbE or Infiniband attached storage
PC, Workstation, Game Consoles, Settop box, Handheld clients
Wired or wirelessly connected

11
Game Engine based on Cell/B.E. servers and Intel workstations
Enables the next generation virtual environments
– Current online video games engines perform limited amount of physical simulation
– Not enough client CPU resources
– No distributed physics
– Bandwidth & Latency between processing nodes prohibitive to achieving real time performance
Enable complex visual F/X movies on large servers - Machinima

12
Intel/AMD PCs
Game Consoles
Network
Scene
Management
Simulation
User
Input
Client
Server
Server Model
of World
State
Users View
Client Model
of World
State
Entity State Input
from Server
World State Input
from Client
Entity State
Verification
Input from
Client
Simulation
Online Game Client/Server Execution Flow
Rendering
Rendering
Composite
Render Input
from Server
IBM QS2x Blades

13
Scaling Virtual Worlds
Game Framework
Network Manager
Player Login
Event Handlers Client
Intel/AMD PCs
Consoles
Handhelds
Client Renderer
Interpolator
Local Database
Event Handler
IBM Cell/B.E. Servers
Higher compute power required
.
.
Rigid Body
Zone0Zone1Zonen
Collisions
Rigid Body Collisions
Network Compression
Arithmetic Coding
Network Compression
Arithmetic Coding
Divide Virtual
world into
geographic
zones
Intel/AMD/Power Servers
Lower compute power required
Virtual World
State Manager
Network Compression
Arithmetic Coding
Virtual World
State Manager
Virtual World
State Manager
Server-side State: Distributed Model

14
Scaling Virtual Worlds
IBM Cell/B.E. Servers
Higher compute power required
.
.
Virtual World
State Manager
Game Framework
Network Manager
Blade 0
Rigid Body
Blade 1
Blade n
Node0Node1Noden
Client Renderer
Interpolator
Local Database
Collisions
Player Login
Event Handlers
Network Compression
Arithmetic Coding
Network Compression
Arithmetic Coding
Network Compression
Arithmetic Coding
Dispatch work (elements
of Virtual World) to
Compute Nodes
Event Handler
Intel/AMD/Power/Z Servers
Lower compute power required
More transaction processing
Client
Intel/AMD PCs
Consoles
Handhelds
Server-side State: Centralized Model

15
Server Side Physics Loop on Cell/B.E. Servers
Numerical Integration to obtain
positions of bodies for next step
Sparse Matrix solvers
Perform very well on Cell/B.E.
Collision detection
Bounding box culling
Sorting
Point/triangle
intersection
Even sorting has proven to be very fast on Cell SPUs !!

16
UCSD Scalable City Project (Sheldon Brown, UCSD)

17
Scalable City Pipeline: Landscape creation

18
Scalable City Pipeline: Border Detection

19
Scalable City Pipeline: Space Filling Curve is GeneratedScalable City Pipeline: Space Filling Curve is Generated

20
Scalable City Pipeline: Simple Curves Converted into Road Geometry

21
Before use of cell, this was pre-
computed (before game play
session)
Roads Integrated into Landscape

22
Cell based computation ofCell based computation of
similar pipelinesimilar pipeline –– takes place intakes place in
about 1 second using one bladeabout 1 second using one blade
per landscape (per landscape (demodemo))

23
IRT Rendering Engine: IBM demo technology

24
SPE Partitions
Blade 0
Blade 1
Blade 2
Blade 3
Blade 4
Blade 5
Blade 6
GDC 07 Configuration, Seven QS20 Blades, 3.2 TFLOPS
iRT Blade Performance Scaling
0
2
4
6
8
10
12
14
16
18
20
1 2 3 4 5 6 7
Blades
Frames/Sec
1080p 1
Realtime ray tracing including
• Reflection/transparency
• detailed shadows
• ambient occlusion
• 4x4 jittered multi-sampling
Totaling up to 288 rays/pixel
Lamborghini car model
• 1.6 million polygons
• 1080p hi-def.
• demo
Also Shown at GDC
March 2007
• PS3 node w/ seven
QS20 blades
• 3 Million Triangle
Urban Landscape
• >100 Textures
• 1080p HDTV
GigabitNetwork
Scalability at processor and system level

25
Cell/B.E. vs. G80 –
Ray tracing
Performance
IBM QS20 Blade (Two 3.2 GHz Cell Processors) - IBM Demo ray tracer
Despite comparable theoretical floating point performance:
– One Cell/BE is 4-5x faster than G80.
– One QS20 blade (two Cell/BE processors) is 8-11x faster than G80
AMD Opteron, the defacto industry workhorse for rendering is easily overwhelmed by
Cell/B.E. or G80.
Secondary Ray processing – Incoherent nature makes them difficult to process on G80, but
they are easily handled by Cell/B.E.
Cell/B.E. is half the size of the G80 and produces five times the ray-tracing performance.
Left to Right:
•2.6 GHz AMD Opteron - Saarland Ray-
tracer
•nVIDIA GeForce 8800 GTX - Saarland
Ray-tracer
•Sony Playstation3 (partial 3.2 GHz
Cell/B.E. processor running Linux) - IBM
Demo
•3.2 GHz Cell/B.E. Processor - IBM Demo

26
Cell/B.E. enables scalable, shared architecture
with full consumer to professional potential
Sony
PLAYSTATION©3
(Cell/B.E. + GPU)
IBM Cell/B.E.
Blade
(2 Cell/B.E.)
IBM Roadrunner
(16,000 Cell/B.E.
+ AMD)
Mercury 1u Dual Cell
Sony Cell/B.E.
Computing Unit
(Cell/B.E. + GPU +
AV I/O)
Consumer Professional
High Perf
ComputingBusiness
PowerXcell 8i PCI card
(Cell/B.E. + Host)
Common OS’s, Infrastructure, Tools, Libraries, Code…
Toshiba Spurs
Engine (SPU’s.
+ Host)

27
© Copyright International Business Machines Corporation 2007
All Rights Reserved
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Information in this document concerning non-IBM products was obtained from the suppliers of these products or other public sources. Questions on
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The information contained in this document has not been submitted to any formal IBM test and is provided "AS IS" with no warranties or guarantees
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All examples cited or described in this document are presented as illustrations of the manner in which some IBM products can be used and the
results that may be achieved. Actual environmental costs and performance characteristics will vary depending on individual client configurations and
conditions.
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Many of the features described in this document are operating system dependent and may not be available on Linux. For more information, please
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Any performance data contained in this document was determined in a controlled environment. Actual results may vary significantly and are
dependent on many factors including system hardware configuration and software design and configuration. Some measurements quoted in this
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systems. Some measurements quoted in this document may have been estimated through extrapolation. Users of this document should verify the
applicable data for their specific environment.
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Cell/B.E. Servers: A Platform for Real Time Scalable Computing and Visualization

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