scholarly journals Towards Structured Parallel Computing on Architecture-Independent Parallel Algorithm Design for Distributed-Memory Architectures

1996 ◽  
Vol 53 (1) ◽  
pp. 112-128
Author(s):  
Feng Gao
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Distributed Memory ◽  
Algorithm Design ◽  
Parallel Algorithm Design ◽  
Memory Architectures

scholarly journals An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 342
Author(s):  
Alessandro Varsi ◽  
Simon Maskell ◽  
Paul G. Spirakis
Keyword(s):  
Parallel Computing ◽  
Shared Memory ◽  
Time Complexity ◽  
Distributed Memory ◽  
Particle Filters ◽  
Dynamic Models ◽  
State Of The Art ◽  
Novel Approach ◽  
Non Gaussian ◽  
Memory Architectures

Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the run-time of PF applications becomes increasingly slow. Parallel computing can help to address this. However, resampling (and, hence, PFs as well) necessarily involves a bottleneck, the redistribution step, which is notoriously challenging to parallelize if using textbook parallel computing techniques. A state-of-the-art redistribution takes O((log2N)2) computations on Distributed Memory (DM) architectures, which most supercomputers adopt, whereas redistribution can be performed in O(log2N) on Shared Memory (SM) architectures, such as GPU or mainstream CPUs. In this paper, we propose a novel parallel redistribution for DM that achieves an O(log2N) time complexity. We also present empirical results that indicate that our novel approach outperforms the O((log2N)2) approach.

Data Parallel and Scheduling Mechanism Based on Petri Nets

2014 ◽  
Vol 543-547 ◽  
pp. 3264-3267
Author(s):  
Wan Feng Dou ◽  
Jing Zhao ◽  
Kun Yang ◽  
Min Xu
Keyword(s):  
Parallel Computing ◽  
Petri Nets ◽  
Distributed Memory ◽  
Algorithm Design ◽  
Data Partition ◽  
Parallel Methods ◽  
Node Number ◽  
Data Parallel ◽  
Total Data ◽  
Data Granularity

Data-parallel and task-parallel methods are the basic methods frequently used for algorithm design in parallel computing. Data-parallel method as name means is used for partition data to be processed into some small blocks considering storage and computing capacity such as memory size of a computation node, node number to take part in parallel computing and total data size, and etc. On the other hand, data dispensing strategy is an important problem carefully considered to increase the efficiency of computation. According to the characteristics of analysis of digital terrain, petri nets is introduced to describe the parallel relationships within data partitions based on data granularity model considering two kinds of computing modes, shared memory and distributed memory respectively, and corresponding scheduling algorithms are proposed for load balance. The experimental results show that our method is very usable to data partition and dispensation, in particular to distributed memory mode.

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