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  1. Proceedings of the 8th International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM'17)
  2. A Framework for Developing Parallel Applications with high level Tasks on Heterogeneous Platforms
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Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs
TaskInsight: Understanding Task Schedules Effects on Memory and Performance
A high-performance portable abstract interface for explicit SIMD vectorization
PETRAS: Performance, Energy and Thermal Aware Resource Allocation and Scheduling for Heterogeneous Systems
Reduction to Tridiagonal Form for Symmetric Eigenproblems on Asymmetric Multicore Processors
High Performance Detection of Strongly Connected Components in Sparse Graphs on GPUs
Towards Composable GPU Programming: Programming GPUs with Eager Actions and Lazy Views
Assessing One-to-One Parallelism Levels Mapping for OpenMP Offloading to GPUs
A Framework for Developing Parallel Applications with high level Tasks on Heterogeneous Platforms

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A Framework for Developing Parallel Applications with high level Tasks on Heterogeneous Platforms

Content Provider ACM Digital Library
Author Liu, Chao Leeser, Miriam
Abstract Traditional widely used parallel programming models and methods focus on data distribution and are suitable for implementing data parallelism. They lack the abstraction of task parallelism and make it inconvenient to separate the applications' high level structure from low level implementation and execution. To improve this, we propose a parallel programming framework based on the tasks and conduits (TNC) model. In this framework, we provide tasks and conduits as the basic components to construct applications at a higher level. Users can easily implement coarse-grained task parallelism with multiple tasks running concurrently. When running on different platforms, the application main structure can stay the same and only adapt task implementations based on the target platforms, improving maintenance and portability of parallel programs. For a single task, we provide multiple levels of shared memory concepts, allowing users to implement fine grained data parallelism through groups of threads across multiple nodes. This provides users a flexible and efficient means to implement parallel applications. By extending the framework runtime system, it is able to launch and run GPU tasks to make use of GPUs for acceleration. The support of both CPU tasks and GPU tasks helps users develop and run parallel applications on heterogeneous platforms. To demonstrate the use of our framework, we tested it with some kernel applications. The results show that the applications' performance using our framework is comparable to traditional programming methods. Further, with the use of GPU tasks, we can easily adjust the program to leverage GPUs for acceleration. In our tests, a single GPU's performance is comparable to a 4 node multicore CPU cluster.
Starting Page 74
Ending Page 79
Page Count 6
File Format PDF
ISBN 9781450348836
DOI 10.1145/3026937.3026946
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-02-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Task parallelism Tasks&conduits Gpu Shared memory Accelerator
Content Type Text
Resource Type Article
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