Paralle programming 2
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There are two main types of parallel computers: shared memory multiprocessors and distributed memory multicomputers. Shared memory multiprocessors have multiple processors that can access a shared memory address space, while distributed memory multicomputers consist of separate computers connected by an interconnect network that communicate by message passing. Beowulf clusters are a type of distributed memory multicomputer made from interconnected commodity computers that provide high-performance computing at low cost. Programming distributed memory systems requires using message passing libraries to explicitly specify communication between processes on different computers.
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Paralle programming 2
- 1. 1b.1 Types of Parallel Computers Two principal approaches: • Shared memory multiprocessor • Distributed memory multicomputer ITCS 4/5145 Parallel Programming, UNC-Charlotte, B. Wilkinson, 2010. Aug 26, 2010
- 3. 1b.3 Conventional Computer Consists of a processor executing a program stored in a (main) memory: Each main memory location located by its address. Addresses start at 0 and extend to 2b - 1 when there are b bits (binary digits) in address. Main memory Processor Instructions (to processor) Data (to or from processor)
- 4. 1b.4 Shared Memory Multiprocessor System Natural way to extend single processor model - have multiple processors connected to multiple memory modules, such that each processor can access any memory module: Processors Processor-memory Interconnections Memory module One address space
- 5. 1b.5 Simplistic view of a small shared memory multiprocessor Examples: • Dual Pentiums • Quad Pentiums Processors Shared memory Bus
- 6. 1b.6 Real computer system have cache memory between the main memory and processors. Level 1 (L1) cache and Level 2 (L2) cache. Example Quad Shared Memory Multiprocessor Processor L2 Cache Bus interface L1 cache Processor L2 Cache Bus interface L1 cache Processor L2 Cache Bus interface L1 cache Processor L2 Cache Bus interface L1 cache Memory controller Memory Processor/ memory bus Shared memory
- 7. 1b.7 “Recent” innovation • Dual-core and multi-core processors • Two or more independent processors in one package • Actually an old idea but not put into wide practice until recently. • Since L1 cache is usually inside package and L2 cache outside package, dual-/multi-core processors usually share L2 cache.
- 8. 1b.8 Single quad core shared memory multiprocessor L2 Cache Memory controller Memory Shared memory Chip Processor L1 cache Processor L1 cache Processor L1 cache Processor L1 cache
- 9. 1b.9 Examples • Intel: – Core Dual processors -- Two processors in one package sharing a common L2 Cache. 2005-2006 – Intel Core 2 family dual cores, with quad core from Nov 2006 onwards – Core i7 processors replacing Core 2 family - Quad core Nov 2008 – Intel Teraflops Research Chip (Polaris), a 3.16 GHz, 80- core processor prototype. • Xbox 360 game console -- triple core PowerPC microprocessor. • PlayStation 3 Cell processor -- 9 core design. References and more information -- wikipedia
- 10. 1b.10 Multiple quad-core multiprocessors (example coit-grid05.uncc.edu) Memory controller Memory Shared memory L2 Cache possible L3 cache Processor L1 cache Processor L1 cache Processor L1 cache Processor L1 cache Processor L1 cache Processor L1 cache Processor L1 cache Processor L1 cache
- 11. 1b.11 Programming Shared Memory Multiprocessors Several possible ways 1. Thread libraries - programmer decomposes program into individual parallel sequences, (threads), each being able to access shared variables declared outside threads. Example Pthreads 2. Higher level library functions and preprocessor compiler directives to declare shared variables and specify parallelism. Uses threads. Example OpenMP - industry standard. Consists of library functions, compiler directives, and environment variables - needs OpenMP compiler
- 12. 1b.12 3. Use a modified sequential programming language -- added syntax to declare shared variables and specify parallelism. Example UPC (Unified Parallel C) - needs a UPC compiler. 4. Use a specially designed parallel programming language -- with syntax to express parallelism. Compiler automatically creates executable code for each processor (not now common). 5. Use a regular sequential programming language such as C and ask parallelizing compiler to convert it into parallel executable code. Also not now common.
- 13. 1b.13 Message-Passing Multicomputer Complete computers connected through an interconnection network: Processor Interconnection network Local Computers Messages memory
- 14. 1b.14 Interconnection Networks Many explored in the 1970s and 1980s • Limited and exhaustive interconnections • 2- and 3-dimensional meshes • Hypercube • Using Switches: – Crossbar – Trees – Multistage interconnection networks
- 15. 1b.15 Networked Computers as a Computing Platform • A network of computers became a very attractive alternative to expensive supercomputers and parallel computer systems for high-performance computing in early 1990s. • Several early projects. Notable: – Berkeley NOW (network of workstations) project. – NASA Beowulf project.
- 16. 1b.16 Key advantages: • Very high performance workstations and PCs readily available at low cost. • The latest processors can easily be incorporated into the system as they become available. • Existing software can be used or modified.
- 17. 1b.17 Beowulf Clusters* • A group of interconnected “commodity” computers achieving high performance with low cost. • Typically using commodity interconnects - high speed Ethernet, and Linux OS. * Beowulf comes from name given by NASA Goddard Space Flight Center cluster project.
- 18. 1b.18 Cluster Interconnects • Originally fast Ethernet on low cost clusters • Gigabit Ethernet - easy upgrade path More Specialized/Higher Performance • Myrinet - 2.4 Gbits/sec - disadvantage: single vendor • cLan • SCI (Scalable Coherent Interface) • QNet • Infiniband - may be important as infininband interfaces may be integrated on next generation PCs
- 19. 1b.19 Dedicated cluster with a master node and compute nodes User Master node Compute nodes Dedicated Cluster Ethernet interface Switch External network Computers Local network
- 20. 1b.20 Software Tools for Clusters • Based upon message passing programming model • User-level libraries provided for explicitly specifying messages to be sent between executing processes on each computer . • Use with regular programming languages (C, C++, ...). • Can be quite difficult to program correctly as we shall see.
- 21. Next step • Learn the message passing programming model, some MPI routines, write a message-passing program and test on the cluster. 1b.21