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  1. Proceedings of the 2015 International Workshop on Code Optimisation for Multi and Many Cores (COSMIC '15)
  2. The Basic Building Blocks of Parallel Tasks
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Dependence-Based Code Transformation for Coarse-Grained Parallelism
Runtime Support for Multiple Offload-Based Programming Models on Embedded Manycore Accelerators
A Roadmap for a Type Architecture Based Parallel Programming Language
Hardware-Aware Automatic Code-Transformation to Support Compilers in Exploiting the Multi-Level Parallel Potential of Modern CPUs
Exploiting Dynamic Parallelism to Efficiently Support Irregular Nested Loops on GPUs
Cycle-based Model to Evaluate Consistency Protocols within a Multi-protocol Compilation Tool-chain
The Basic Building Blocks of Parallel Tasks
An Evaluation of Memory Sharing Performance for Heterogeneous Embedded SoCs with Many-Core Accelerators

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The Basic Building Blocks of Parallel Tasks

Content Provider ACM Digital Library
Author Jannesari, Ali Atre, Rohit Wolf, Felix
Abstract Discovery of parallelization opportunities in sequential programs can greatly reduce the time and effort required to parallelize any application. Identification and analysis of code that contains little to no internal parallelism can also help expose potential parallelism. This paper provides a technique to identify a block of code called Computational Unit (CU) that performs a unit of work in a program. A CU can assist in discovering the potential parallelism in a sequential program by acting as a basic building block for tasks. CUs are used along with dynamic analysis information to identify the tasks that contain tightly coupled code within them. This process in turn reveals the tasks that are weakly dependent or independent. The independent tasks can be run in parallel and the dependent tasks can be analyzed to check if the dependences can be resolved. To evaluate our technique, different benchmark applications are parallelized using our identified tasks and the speedups are reported. In addition, existing parallel implementations of the applications are compared with the identified tasks for the respective applications.
Starting Page 1
Ending Page 11
Page Count 11
File Format PDF
ISBN 9781450333160
DOI 10.1145/2723772.2723778
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-02-08
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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