About super computer

1. Super computer

2. Contents
 Introduction
 History
 Need&application
 Examples
 Technology
 Architecture
 Amdahl's Law

3. Introduction
 A supercomputer is a computer with a
high level of computational
capacity compared to a general-purpose
computer

4. History
 The history of supercomputing goes back to the
1960s, with the Atlas at the University of
Manchester designed by Seymour Cray
 The ”Atlas” was a joint venture between ’Ferranti’ and
the ‘Manchester University’ and was operated at
processing speed of one microsecond per
instruction, about one million instructions per
second.
 The first “Atlas” was officially commissioned on 7
December 1962 as one of the world's first
supercomputers and is equivalent to four IBM 7094s.

5. Need & Application
 Used for solving highly calculation intensive tasks
like in
 Weather forecasts
 Explosions - detonation of nuclear weapons
 Analysis of data
 Astronomical observations
 Integrate design of engineering products

6. Examples of super computers
 Ex: CDC6600
 1-10 MFlops
 Single CPU
 60-bit word length
 RISC
 Central Processor has 10 parallel functional units
-superscalar.
 Implemented 10 peripheral processors
 no pipelining

7. EX:
 CDC7600 (1969-1975)
 10-100 MFlops
 Instruction Pipelining
 Pipelined execution on functional units in parallel 8
processors
( Cray series)
Worked on new design - vector processors load large
data at once.

8. Technology in super conductors
 Performance of a supercomputer is measured
in floating-point operations per second (FLOPS)
instead of million instructions per second (MIPS)
 parallel computing is a type of computation in
which many calculations or the execution
of processes are carried out simultaneously.
 Using N processors, we expect the execution
time to come down N times ideally hence
speedup by N

9.  1 processor::T(1) = s + p = 1 i.e., p = 1 - s
 n processors:: T(n) = s + (p/n)
 Scalability (Speed Up) = T(1)/T(n) = 1/(s+(1-s)/n)
Amdahl's law

10. Architecture of super computer
 Different architectures to implement parallel
algorithm
 1 Shared-memory systems
 Uniform Memory Access (UMA)
 cache coherent Non-Uniform Memory Access
(ccNUMA)
 2 Distributed-memory systems
 Massively Parallel Processing
 Cluster computing
 Each has a standardized programming model

11. Shared memory systems
 Two basic categories of shared memory systems
 Uniform Memory Access (UMA): Memory is
equally accessible to all processors with the same
performance (Bandwidth & Latency)
 Simplest example - dual core processor
 cache-coherent Non Uniform Memory Access
(ccNUMA): Memory is physically distributed but
appears as a single address space: Performance
(Bandwidth & Latency) is different for local and
remote memory access.
 Examples: AMD Opteron nodes, SGI Altix, Intel
Nehalem nodes – all modern multi-socket nodes

12. Distributed memory systems
 Pure distributed-memory parallel
computer
 No global cache-coherent shared
address space No Remote Memory
Access (NORMA)
 Data exchange between nodes:
Passing messages via network

13. Architecture of UMA
Architecture of
CCNUMA

14. Massively parallel
 Independent microprocessors that run in parallel
 Each processor has its own memory
 All processing elements are connected to form
one large computer
 A number of processors work on same task
 Examples : Blue Gene

15. clusters
 Completely independent computers combined to
a unified system through software and networking
 Components connected through fast LANs -
relies heavily on network speed
 Easily upgraded with addition of new systems
 Eg. 1100 Apple XServe G5 2.3 GHz dual-
processor machines (4 GB RAM, 80 GB SATA
HD) running Mac OS X and using Infinite Band
interconnect

16. Message passing interface
format

17. Top 5 super computers

18. conclusion
 Supercomputers of today are the
general purpose computers of tomorrow
 Many important applications need the
better main memory bandwidth and
computing power that are available only
in supercomputers