Introduction to Parallel Programming

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11/6/17 Introduction to parallel programming Alberto Bosio, Associate Professor – UM Microelectronic Departement [email protected] Definitions l What is parallel programming? l Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem 1 11/6/17 Serial vs parallel l Serial Computing l Traditionally, software has been written for serial computation: To be run on a single CPU; A problem is broken into a discrete series of instructions. Instructions are executed one after another. l Only one instruction may execute at any moment Serial vs parallel l Simultaneous use of multiple compute resources to solve a computational problem: l To be run using multiple CPUs l A problem is broken into discrete parts, solved concurrently l Each part is broken down to a series of instructions l Instructions from each part execute simultaneously on different CPUs 2 11/6/17 Serial vs parallel Why parallel computing? l Save time and/or money l Solve larger problems l Provide concurrency l Use of non-local resources l Limits to serial computing, physical and practical l Transmission speeds l Miniaturization l Economic limitations 3 11/6/17 Shared Memory l Shared memory: all processors access all memory as a global address space. l CPUs operate independently but share memory resources. Changes in a memory location effected by one processor are visible to all other processors. l Two main classes based upon memory access times: UMA and NUMA. Uniform Memory Access l Uniform Memory Access (UMA): Identical processors l Equal access and access times to memory Cache coherent: if one processor updates a location in shared memory, all the other processors know about the update. 4 11/6/17 Non-Uniform Memory Access l Non-Uniform Memory Access (NUMA): l Often made by physically linking two or more SMPs l One SMP can directly access memory of another SMP l Not all processors have equal access time to all memories l Memory access across link is slower Shared Memory l Advantages l Global address space is easy to program l Data sharing is fast and uniform due to the proximity memory/CPUs l Disadvantages l Lack of scalability between memory and CPUs Programmer responsibility for synchronization 5 11/6/17 Distributed Memory l Communication network to connect inter- processor memory. Processors have their own local memory. l No concept of global address space l CPUs operate independently. l Change to local memory have no effect on the memory of other processors. l Cache coherency does not apply. l Data communication and synchronization are programmer's responsibility l Connection used for data transfer varies (es. Ethernet) Distributed Memory 6.

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2.5 Classification of Parallel Computers
52 // Architectures 2.5 Classification of Parallel Computers 2.5 Classification of Parallel Computers 2.5.1 Granularity In parallel computing, granularity means the amount of computation in relation to communication or synchronisation Periods of computation are typically separated from periods of communication by synchronization events. • fine level (same operations with different data) ◦ vector processors ◦ instruction level parallelism ◦ fine-grain parallelism: – Relatively small amounts of computational work are done between communication events – Low computation to communication ratio – Facilitates load balancing 53 // Architectures 2.5 Classification of Parallel Computers – Implies high communication overhead and less opportunity for per- formance enhancement – If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation. • operation level (different operations simultaneously) • problem level (independent subtasks) ◦ coarse-grain parallelism: – Relatively large amounts of computational work are done between communication/synchronization events – High computation to communication ratio – Implies more opportunity for performance increase – Harder to load balance efficiently 54 // Architectures 2.5 Classification of Parallel Computers 2.5.2 Hardware: Pipelining (was used in supercomputers, e.g. Cray-1) In N elements in pipeline and for 8 element L clock cycles =) for calculation it would take L + N cycles; without pipeline L ∗ N cycles Example of good code for pipelineing: §doi =1 ,k ¤ z ( i ) =x ( i ) +y ( i ) end do ¦ 55 // Architectures 2.5 Classification of Parallel Computers Vector processors, fast vector operations (operations on arrays). Previous example good also for vector processor (vector addition) , but, e.g. recursion – hard to optimise for vector processors Example: IntelMMX – simple vector processor.
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