Download Robert van Liere

OGO 2.1
SGI Origin 2000
Robert van Liere
CWI, Amsterdam
TU/e, Eindhoven
11 September 2001
unite.sara.nl
• SGI Origin 2000
• Located at SARA in Amsterdam
• Hardware configuration :
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128 MIPS R10000 CPUs @ 250 Mhz
64 Gbyte main memory
1 Tbyte disk storage
11 ethernet @ 100 Mbits
1 ethernet @ 1 Gbit
Contents
• Architecture
– Overview
– Module interconnect
– Memory hierarchies
• Programming
– Parallel models
– Data placement
• Pros and cons
Overview - Features
• 64 bit RISC microprocessors
• Large main memory
• “Scalable” in CPU, memory and I/O
• Shared memory programming model
Overview - Applications
• Worldwide : +/- 30.000 systems
– ~ 50 with >128 CPUs
– ~ 100 with 64-128 CPUs
– ~ 500 with 32-64 CPUs
• Computing serving : many CPUs and memory
• Database serving : many disks
• Web serving : many I/O
System architecture – 1 CPU
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CPU + cache
One system bus
Memory
I/O (network + disk)
• Cached data
System architecture – N CPU
• Symmetric multiprocessing (SMP)
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Multi-CPU + caches
One shared bus
Memory
I/O
N CPU – cache coherency
• Problem:
– Inconsistent cached data
• Solution:
– Snooping
– Broadcasting
• Not scalable
Architecture – Origin 2000
• Node board
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2 CPU + cache
Memory
Directory
HUB
I/O
Origin 2000 Interconnect
• Node boards
• Routers
– Six ports
Interconnect Topology
Sample Topologies
128 Topology
Virtual Memory
• One CPU, multi programs
• Page
• Paging disk
• Page replacement
O2000 Virtual Memory
• Multi CPU, Multi progs
• Non-Uniform Memory Access
• Efficient programs:
– Minimize data movement
– Data “close” to CPU
Latencies and Bandwidth
Application performance
• Scientific computing
– LU, ocean, barnes, radiosity
• Linear speedup
– More CPUs -> performance
Programming support
• IRIX operating system
• Parallel programming
– C source level with compiler pragmas
– Posix Threads
– UNIX processes
• Data placement
– dplace , dlock, dperf
• Profiling
– timex, ssrun
Parallel Programs
• Functional Decomposition
– Decompose the problem into different tasks
• Domain Decomposition
– Partition the problem’s data structure
• Consider
– Mapping tasks/parts onto CPUs
– Coordinate work and communication of CPUs
Task Decomposition
• Decompose problem
• Determine dependencies
Task Decomposition
• Map tasks on threads
• Compare:
– Sequential case
– Parallel case
Efficient programs
• Use many CPUs
– Measure speedups
• Avoid:
– Excessive data dependencies
– Excessive cache misses
– Excessive inter-node communication
Pros vs Cons
• Multi-processor (128 )
• Large memory (64 Gbyte)
• Slow integer CPU
• Performance penalty:
• Shared memory
programming
– Data dependencies
– Off board memory