1. OGO 2.1 SGI Origin 2000 Robert van Liere CWI, Amsterdam TU/e, Eindhoven 11 September 2001

2. unite.sara.nl SGI Origin 2000 Located at SARA in Amsterdam Hardware configuration : –128 MIPS R10000 CPUs @ 250 Mhz –64 Gbyte main memory –1 Tbyte disk storage –11 ethernet @ 100 Mbits –1 ethernet @ 1 Gbit

3. Contents Architecture –Overview –Module interconnect –Memory hierarchies Programming –Parallel models –Data placement Pros and cons

4. Overview - Features 64 bit RISC microprocessors Large main memory “Scalable” in CPU, memory and I/O Shared memory programming model

5. 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

6. System architecture – 1 CPU CPU + cache One system bus Memory I/O (network + disk) Cached data

7. System architecture – N CPU Symmetric multi- processing (SMP) Multi-CPU + caches One shared bus Memory I/O

8. N CPU – cache coherency Problem: –Inconsistent cached data Solution: –Snooping –Broadcasting Not scalable

9. Architecture – Origin 2000 Node board 2 CPU + cache Memory Directory HUB I/O

10. Origin 2000 Interconnect Node boards Routers –Six ports

11. Interconnect Topology

12. Sample Topologies

13. 128 Topology

14. Virtual Memory One CPU, multi programs Page Paging disk Page replacement

15. O2000 Virtual Memory Multi CPU, Multi progs Non-Uniform Memory Access Efficient programs: –Minimize data movement –Data “close” to CPU

16. Latencies and Bandwidth

17. Application performance Scientific computing –LU, ocean, barnes, radiosity Linear speedup –More CPUs -> performance

18. 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

19. 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

20. Task Decomposition Decompose problem Determine dependencies

21. Task Decomposition Map tasks on threads Compare: –Sequential case –Parallel case

22. Efficient programs Use many CPUs –Measure speedups Avoid: –Excessive data dependencies –Excessive cache misses –Excessive inter-node communication

23. Pros vs Cons Multi-processor (128 ) Large memory (64 Gbyte) Shared memory programming Slow integer CPU Performance penalty: –Data dependencies –Off board memory