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1 Advanced Hardware Parallel/Distributed Processing High Performance Computing Top 500 list Grid computing CMPE 478, Parallel Processing picture of ASCI.

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Presentation on theme: "1 Advanced Hardware Parallel/Distributed Processing High Performance Computing Top 500 list Grid computing CMPE 478, Parallel Processing picture of ASCI."— Presentation transcript:

1 1 Advanced Hardware Parallel/Distributed Processing High Performance Computing Top 500 list Grid computing CMPE 478, Parallel Processing picture of ASCI WHITE, the most powerful computer in the world (2001)

2 2 Von Neumann Architecture CPU RAM Device sequential computer BUS

3 3 History of Computer Architecture 4 Generations (identified by logic technology) 1.Tubes 2.Transistors 3.Integrated Circuits 4.VLSI (very large scale integration)


5 5 Traditional mainframe/supercomputer performance 25% increase per year But … microprocessor performance 50% increase per year since mid 80’s.

6 6 Moore’s Law “Transistor density doubles every 18 months” Moore is co-founder of Intel. 60 % increase per year Exponential growth PC costs decline. PCs are building bricks of all future systems.

7 7 VLSI Generation

8 8 Bit Level Parallelism (upto mid 80’s) 4 bit microprocessors replaced by 8 bit, 16 bit, 32 bit etc. doubling the width of the datapath reduces the number of cycles required to perform a full 32-bit operation mid 80’s reap benefits of this kind of parallelism (full 32- bit word operations combined with the use of caches)

9 9 Instruction Level Parallelism (mid 80’s to mid 90’s) Basic steps in instruction processing (instruction decode, integer arithmetic, address calculations, could be performed in a single cycle) Pipelined instruction processing Reduced instruction set (RISC) Superscalar execution Branch prediction

10 10 Thread/Process Level Parallelism (mid 90’s to present) On average control transfers occur roughly once in five instructions, so exploiting instruction level parallelism at a larger scale is not possible Use multiple independent “threads” or processes Concurrently running threads, processes

11 11 Sequential vs Parallel Processing physical limits reached easy to program expensive supercomputers “raw” power unlimited more memory, multiple cache made up of COTS, so cheap difficult to program

12 12 Amdahl’s Law The serial percentage of a program is fixed. So speed-up obtained by employing parallel processing is bounded. Lead to pessimism in in the parallel processing community and prevented development of parallel machines for a long time. Speedup = 1 s + 1-s P In the limit: Spedup = 1/s s

13 13 Gustafson’s Law Serial percentage is dependent on the number of processors/input. Broke/disproved Amdahl’s law. Demonstrated achieving more than 1000 fold speedup using 1024 processors. Justified parallel processing

14 14 Hillis’ Thesis ‘85 Piece of silicon Sequential computer Parallel computer proposed “The Connection Machine” with massive number of processors each with small memory operating in SIMD mode. CM-1, CM-2 machines from Thinking Machines Corporation (TMC)were examples of this architecture with 32K-128K processors. Unfortunately, TMC went out of business.

15 15 Grand Challenge Applications Important scientific & engineering problems identified by U.S. High Performance Computing & Communications Program (’92)

16 16 Flynn’s Taxonomy classifies computer architectures according to: 1.Number of instruction streams it can process at a time 2.Number of data elements on which it can operate simultaneously Data Streams Single Multiple Single Multiple Instruction Streams SISD SIMD MIMD MISD

17 17 SPMD Model (Single Program Multiple Data) Each processor executes the same program asynchronously Synchronization takes place only when processors need to exchange data SPMD is extension of SIMD (relax synchronized instruction execution) SPMD is restriction of MIMD (use only one source/object)

18 18 Parallel Processing Terminology Embarassingly Parallel: -applications which are trivial to parallelize -large amounts of independent computation -Little communication Data Parallelism: -model of parallel computing in which a single operation can be applied to all data elements simultaneously -amenable to SIMD or SPMD style of computation Control Parallelism: -many different operations may be executed concurrently -require MIMD/SPMD style of computation

19 19 Parallel Processing Terminology Scalability: -If the size of problem is increased, number of processors that can be effectively used can be increased (i.e. there is no limit on parallelism). -Cost of scalable algorithm grows slowly as input size and the number of processors are increased. - Data parallel algorithms are more scalable than control parallel algorithms Granularity: -fine grain machines: employ massive number of weak processors each with small memory -coarse grain machines: smaller number of powerful processors each with large amounts of memory

20 20 Shared Memory Machines Shared Address Space process (thread) process (thread) process (thread) process (thread) process (thread) Memory is globally shared, therefore processes (threads) see single address space Coordination of accesses to locations done by use of locks provided by thread libraries Example Machines: Sequent, Alliant, SUN Ultra, Dual/Quad Board Pentium PC Example Thread Libraries: POSIX threads, Linux threads.

21 21 Shared Memory Machines can be classified as: -UMA: uniform memory access -NUMA: nonuniform memory access based on the amount of time a processor takes to access local and global memory. Inter- connection network/ or BUS Inter- connection network Inter- connection network P.. P M.. M P M P M.. P M P M P M.. P M.. M (a) (b) (c)

22 22 Distributed Memory Machines Network process M M M M M Each processor has its own local memory (not directly accessible by others) Processors communicate by passing messages to each other Example Machines: IBM SP2, Intel Paragon, COWs (cluster of workstations) Example Message Passing Libraries: PVM, MPI

23 23 Beowulf Clusters Use COTS, ordinary PCs and networking equipment Has the best price/performance ratio PC cluster

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46 46 The Future Most Powerful Computer ? (now operational and #1)

47 47 TOP 500 LIST: June 2002

48 48 Grid Computing provide access to computing power and various resources just like accessing electrical power from electrical grid Allows coupling of geographically distributed resources Provide inexpensive access to resources irrespective of their physical location or access point Internet & dedicated networks can be used to interconnect distributed computational resources and present them as a single unified resource Resources: supercomputers, clusters, storage systems, data resources, special devices

49 49 Grid Computing the GRID is, in effect, a set of software tools, which when combined with hardware, would let users tap processing power off the Internet as easily as the electrical power can be drawn from the electricty grid. Examples of Grid projects: - Seti@home : search for extraterrestial intelligenceSeti@home - Entropia : company to broker processing power of idle computers, about 30,000 volunteer computers and total processing power 1 Tflop. -Xpulsar@home : sifts astronomical data for pulsarsXpulsar@home - Folding@home : protein foldingFolding@home - Evolutionary@home : population dynamicsEvolutionary@home

50 50 Seti@homeSeti@home Project Screen-saver program Sifts through signals recorded by the giant Arecibo radio telescope in Puerto Rico 3 million people downloaded screen saver and run it. Program periodically prompts its host to retrieve a new chunk of data from the Internet and sends latest processed results back to SETI. Equivalent of more than 600,000 years of PC processing time has already clocked up.

51 51 More Grid Projects GriPhyN: grid developed by consortium of American labs for physics projects Earth System Grid: make huge climate simulations spanning hundreds of years. Earthquake Engineering Simulation Grid : Particle Physics Data Grid : Information Power Grid : supported by NASA for massive engineering calculations DataGrid : European, coordinated by CERN. Aim is to develop middleware for research projects in biological sciences, earth observation and high energy physics.

52 52 Gordon Bell & Jim Gray on “What’s next in High Performance Computing” Beowulf ’s economics and sociology are poised to kill off the other architectural lines Computational Grid can federate systems into supercomputers far beyond the power of any current computing center The centers will become super-data and super-application centers Clusters (currently) perform poorly on applications that require large shared memory

53 53 Gordon Bell & Jim Gray on “What’s next in High Performance Computing” Now individuals and laboratories can assemble and incrementally grow any-size super-computer anywhere in the world. By 2010, the cluster is likely to be the principal computing structure. Seti@home does not run Linpack, so does not qualify in the top500 list. But Seti@home avarages 13 Tflops making it more powerful than the top 3 of top500 machines combined.Seti@home GRID and P2P computing using the Internet is likely to remain the world’s most powerful supercomputer.

54 54 Gordon Bell & Jim Gray on “What’s next in High Performance Computing” Concerned that traditional supercomputer architecture is dead and a supercomputer mono-culture is being born. Recommend increased investment in peta-scale distributed databases. By 2010, the cluster is likely to be the principal computing structure. Research programs that stimulate cluster understanding and training are a good investment for laboratories that depend on highest performance machines.

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