Granularity must be large enough to offset the overhead that will be needed to manage the tasks and threads □ More computation: higher granularity
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[PDF] Concurrency and Parallelism
Granularity must be large enough to offset the overhead that will be needed to manage the tasks and threads □ More computation: higher granularity
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Carlos Jaime Barrios Hernandez, PhD.
EISI UIS @carlosjaimebh Concurrency and Parallel Programming Concurrency and Parallel ProgrammingConcurrent and Parallel
La répétition sur la scène, 1874, Edgar Degas, Paris, Musée d'Orsay. PlanTraditional Way
Designing and Building Parallel Programs, by Ian Foster in http://www.mcs.anl.gov/~itf/dbpp/Design Spaces of Parallel Programming
• Patterns for Parallel Programming, Timoty Mattson, Beverly A. Sanders and Berna L. Massingill, Software Pattern Series, Addison-Wesley 2004 • Finding Concurrency (Structuring Problem to expose exploitable concurrency) • Algorithm Structure (Structure Algorithm to take advantage of Concurrency) • Supporting Structures (Interfaces between Algorithms and Environments) • Implementation Mechanisms (Define Programming Environments)
Concurrency and Parallelism
Concurrent Programming is all about independent computations that the machine can execute in any order.
Concurrent Programming General Steps
1. Analysis
2. Design and Implementation
3. Tests of Correctness
4. Tune of Performance
Distributed Vs. Shared Memory Programming
Common Features
Work serial algorithm, resulting concurrent version, number of threads / processorsOnly to Shared Memory
is Boss / Worker)Tasks and Data Decomposition
Concurrent Computation from Serial Codes
Sequential Version Parallel / Concurrent Version
What are the tasks and how are defined?
threads. that will be needed to manage the tasks and threads Granularity is the amount of computation done before synchronization is neededTask Granularity
overhead task overhead task overhead task overhead task overhead task overhead task overhead task overhead task Fine-grained decomposition Coarse-grained decompositionTask Dependencies
Order Dependency Data Dependency
Enchantingly Parallel Code: Code without dependenciesHow should you divide data into chunks?
By individual elements By rows By groups of columns By blocksThe Shape of the Chunk
handled during the course of the chunk computations.2 Shared Borders
• Regular shapes : Common Regular data organizations. • Irregular shapes: may be necessary due to the irregular organizations of the data.5 Shared Borders
How should you ensure that the tasks for each chunk have access to all data required for update? chunk. Original split with ghost cells Copying data into ghost cells How are the data chunks (and tasks) assigned to threads? uniform and predictable the computation needed by chunkConcurrent Design Models Features
resources. sizes of data sets.Tasks and Domain Decomposition Patterns
organized around the manipulation of large data structure. data structure. decomposition will be implied by task decomposition.Group and Order Tasks Patterns
structure constraints on the order of execution of a collection of tasks.Data Sharing Pattern
access to some portions of the another task's local data.Design Evaluation Pattern
constraintsNot Parallelizable Jobs, Tasks and Algorithms
The Mythical Man-Month: Essays on Software Engineering. By Fred Brooks. Ed Addison-Wesley Professional, 1995
Algorithm Structures
Algorithm Structure Decision Tree (Major OrganizingPrinciple)
Start Organize By Tasks Linear Task Parallelism Recursive Divide and Conquer Organize By Data Decomposition Linear Geometric Decomposition Recursive Recursive Data Organize By Flow of Data Linear Pipeline Recursive Event-Based Coordination