Parallel Computing Presented by Justin Reschke

Overview Concepts and Terminology Parallel Computer Memory Architectures Parallel Programming Models Designing Parallel Programs Parallel Algorithm Examples Conclusion

Concepts and Terminology: What is Parallel Computing? Traditionally software has been written for serial computation. Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem.

Concepts and Terminology: Why Use Parallel Computing? Saves time – wall clock time Cost savings Overcoming memory constraints It’s the future of computing

Concepts and Terminology: Flynn’s Classical Taxonomy Distinguishes multi-processor architecture by instruction and data SISD – Single Instruction, Single Data SIMD – Single Instruction, Multiple Data MISD – Multiple Instruction, Single Data MIMD – Multiple Instruction, Multiple Data

Flynn’s Classical Taxonomy: SISD Serial Only one instruction and data stream is acted on during any one clock cycle

Flynn’s Classical Taxonomy: SIMD All processing units execute the same instruction at any given clock cycle. Each processing unit operates on a different data element.

Flynn’s Classical Taxonomy: MISD Different instructions operated on a single data element. Very few practical uses for this type of classification. Example: Multiple cryptography algorithms attempting to crack a single coded message.

Flynn’s Classical Taxonomy: MIMD Can execute different instructions on different data elements. Most common type of parallel computer.

Concepts and Terminology: General Terminology Task – A logically discrete section of computational work Parallel Task – Task that can be executed by multiple processors safely Communications – Data exchange between parallel tasks Synchronization – The coordination of parallel tasks in real time

Concepts and Terminology: More Terminology Granularity – The ratio of computation to communication Coarse – High computation, low communication Coarse – High computation, low communication Fine – Low computation, high communication Fine – Low computation, high communication Parallel Overhead Synchronizations Synchronizations Data Communications Data Communications Overhead imposed by compilers, libraries, tools, operating systems, etc. Overhead imposed by compilers, libraries, tools, operating systems, etc.

Parallel Computer Memory Architectures: Shared Memory Architecture All processors access all memory as a single global address space. Data sharing is fast. Lack of scalability between memory and CPUs

Parallel Computer Memory Architectures: Distributed Memory Each processor has its own memory. Is scalable, no overhead for cache coherency. Programmer is responsible for many details of communication between processors.

Parallel Programming Models Exist as an abstraction above hardware and memory architectures Examples: Shared Memory Shared Memory Threads Threads Messaging Passing Messaging Passing Data Parallel Data Parallel

Parallel Programming Models: Shared Memory Model Appears to the user as a single shared memory, despite hardware implementations. Locks and semaphores may be used to control shared memory access. Program development can be simplified since there is no need to explicitly specify communication between tasks.

Parallel Programming Models: Threads Model A single process may have multiple, concurrent execution paths. Typically used with a shared memory architecture. Programmer is responsible for determining all parallelism.

Parallel Programming Models: Message Passing Model Tasks exchange data by sending and receiving messages. Typically used with distributed memory architectures. Data transfer requires cooperative operations to be performed by each process. Ex.- a send operation must have a receive operation. MPI (Message Passing Interface) is the interface standard for message passing.

Parallel Programming Models: Data Parallel Model Tasks performing the same operations on a set of data. Each task working on a separate piece of the set. Works well with either shared memory or distributed memory architectures.

Designing Parallel Programs: Automatic Parallelization Automatic Compiler analyzes code and identifies opportunities for parallelism Compiler analyzes code and identifies opportunities for parallelism Analysis includes attempting to compute whether or not the parallelism actually improves performance. Analysis includes attempting to compute whether or not the parallelism actually improves performance. Loops are the most frequent target for automatic parallelism. Loops are the most frequent target for automatic parallelism.

Designing Parallel Programs: Manual Parallelization Understand the problem A Parallelizable Problem: A Parallelizable Problem: Calculate the potential energy for each of several thousand independent conformations of a molecule. When done find the minimum energy conformation. A Non-Parallelizable Problem: A Non-Parallelizable Problem: The Fibonacci Series All calculations are dependent All calculations are dependent

Designing Parallel Programs: Domain Decomposition Each task handles a portion of the data set.

Designing Parallel Programs: Functional Decomposition Each task performs a function of the overall work

Parallel Algorithm Examples: Array Processing Serial Solution Perform a function on a 2D array. Perform a function on a 2D array. Single processor iterates through each element in the array Single processor iterates through each element in the array Possible Parallel Solution Assign each processor a partition of the array. Assign each processor a partition of the array. Each process iterates through its own partition. Each process iterates through its own partition.

Parallel Algorithm Examples: Odd-Even Transposition Sort Basic idea is bubble sort, but concurrently comparing odd indexed elements with an adjacent element, then even indexed elements. If there are n elements in an array and there are n/2 processors. The algorithm is effectively O(n)!

Parallel Algorithm Examples: Odd Even Transposition Sort Initial array: 6, 5, 4, 3, 2, 1, 0 6, 5, 4, 3, 2, 1, 0 6, 4, 5, 2, 3, 0, 1 4, 6, 2, 5, 0, 3, 1 4, 2, 6, 0, 5, 1, 3 2, 4, 0, 6, 1, 5, 3 2, 0, 4, 1, 6, 3, 5 0, 2, 1, 4, 3, 6, 5 0, 1, 2, 3, 4, 5, 6 Worst case scenario. Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1

Other Parallelizable Problems The n-body problem Floyd’s Algorithm Serial: O(n^3), Parallel: O(n log p) Serial: O(n^3), Parallel: O(n log p) Game Trees Divide and Conquer Algorithms

Conclusion Parallel computing is fast. There are many different approaches and models of parallel computing. Parallel computing is the future of computing.

References A Library of Parallel Algorithms, www- 2.cs.cmu.edu/~scandal/nesl/algorithms.html Internet Parallel Computing Archive, wotug.ukc.ac.uk/parallel Introduction to Parallel Computing, Parallel Programming in C with MPI and OpenMP, Michael J. Quinn, McGraw Hill Higher Education, 2003 The New Turing Omnibus, A. K. Dewdney, Henry Holt and Company, 1993