scholarly journals Synchronization and Ordering Semantics in Hybrid MPI+GPU Programming

2013 ◽  
Author(s):  
Ashwin M. Aji ◽  
Pavan Balaji ◽  
James Dinan ◽  
Wu-chun Feng ◽  
Rajeev Thakur
Keyword(s):  
Gpu Programming ◽  
Ordering Semantics

Region-based memory management for GPU programming languages

2014 ◽  
Vol 49 (10) ◽  
pp. 141-155 ◽  
Author(s):  
Eric Holk ◽  
Ryan Newton ◽  
Jeremy Siek ◽  
Andrew Lumsdaine
Keyword(s):  
Programming Languages ◽  
Memory Management ◽  
Gpu Programming

Fast incremental discovery of pointwise order dependencies

2020 ◽  
Vol 13 (10) ◽  
pp. 1669-1681
Author(s):  
Zijing Tan ◽  
Ai Ran ◽  
Shuai Ma ◽  
Sheng Qin
Keyword(s):  
Real Life ◽  
Effective Algorithm ◽  
Data Set ◽  
Ordering Semantics ◽  
Synthetic Datasets ◽  
Indexing Technique

Pointwise order dependencies (PODs) are dependencies that specify ordering semantics on attributes of tuples. POD discovery refers to the process of identifying the set Σ of valid and minimal PODs on a given data set D. In practice D is typically large and keeps changing, and it is prohibitively expensive to compute Σ from scratch every time. In this paper, we make a first effort to study the incremental POD discovery problem, aiming at computing changes ΔΣ to Σ such that Σ ⊕ ΔΣ is the set of valid and minimal PODs on D with a set Δ D of tuple insertion updates. (1) We first propose a novel indexing technique for inputs Σ and D. We give algorithms to build and choose indexes for Σ and D , and to update indexes in response to Δ D. We show that POD violations w.r.t. Σ incurred by Δ D can be efficiently identified by leveraging the proposed indexes, with a cost dependent on log (| D |). (2) We then present an effective algorithm for computing ΔΣ, based on Σ and identified violations caused by Δ D. The PODs in Σ that become invalid on D + Δ D are efficiently detected with the proposed indexes, and further new valid PODs on D + Δ D are identified by refining those invalid PODs in Σ on D + Δ D. (3) Finally, using both real-life and synthetic datasets, we experimentally show that our approach outperforms the batch approach that computes from scratch, up to orders of magnitude.

Formal analysis techniques for reliable GPU programming

2017 ◽  
pp. 3-21
Author(s):  
A.F. Donaldson ◽  
G. Gopalakrishnan ◽  
N. Chong ◽  
J. Ketema ◽  
G. Li ◽  
...  
Keyword(s):  
Formal Analysis ◽  
Gpu Programming ◽  
Analysis Techniques

Efficient Graph Component Labeling on Hybrid CPU and GPU Platforms

2014 ◽  
Vol 596 ◽  
pp. 276-279
Author(s):  
Xiao Hui Pan
Keyword(s):  
High Performance ◽  
General Purpose ◽  
Gpu Programming ◽  
Data Parallel ◽  
Graphical Processing Units ◽  
Architectural Features ◽  
Graph Coloring Problem ◽  
Graphical Processing ◽  
And Performance ◽  
Performance Results

Graph component labeling, which is a subset of the general graph coloring problem, is a computationally expensive operation in many important applications and simulations. A number of data-parallel algorithmic variations to the component labeling problem are possible and we explore their use with general purpose graphical processing units (GPGPUs) and with the CUDA GPU programming language. We discuss implementation issues and performance results on CPUs and GPUs using CUDA. We evaluated our system with real-world graphs. We show how to consider different architectural features of the GPU and the host CPUs and achieve high performance.

COURSE CONTENT FOR LEARNING GPU PROGRAMMING

2020 ◽  
Author(s):  
Gabriel Camporredondo ◽  
Lourdes Muñoz ◽  
Mathieu Legrand ◽  
Ramon Barber
Keyword(s):  
Gpu Programming ◽  
Course Content

CUDA Based GPU Programming to Simulate 3D Tissue Deformation

2010 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Jianhui Zhao ◽  
Zhiyong Yuan ◽  
Yihua Ding ◽  
Chengjiang Long ◽  
...  
Keyword(s):  
Gpu Programming ◽  
Tissue Deformation
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