Static Partitioning for Heterogeneous Computational Environments

2009 ◽  
Author(s):  
P. Iványi ◽  
B.H.V. Topping
Keyword(s):  
Static Partitioning

scholarly journals Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Manal A. El Sayed ◽  
El Sayed M. Saad ◽  
Rasha F. Aly ◽  
Shahira M. Habashy
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Bin Packing ◽  
Shared Resources ◽  
Energy Aware ◽  
Task Partitioning ◽  
Worst Case ◽  
Real Time Scheduling ◽  
Static Partitioning ◽  
Time Scheduling

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

scholarly journals Combining Static Partitioning with Dynamic Distribution of Threads

1999 ◽  
pp. 85-96 ◽  
Author(s):  
Ronald Moore ◽  
Melanie Klang ◽  
Bernd Klauer ◽  
Klaus Waldschmidt
Keyword(s):  
Dynamic Distribution ◽  
Static Partitioning

scholarly journals Static partitioning and mapping of kernel-based applications over modern heterogeneous architectures

2015 ◽  
Vol 58 ◽  
pp. 79-94 ◽  
Author(s):  
J. Daniel García ◽  
Rafael Sotomayor ◽  
Javier Fernández ◽  
Luis Miguel Sánchez
Keyword(s):  
Heterogeneous Architectures ◽  
Static Partitioning

scholarly journals Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

2020 ◽  
Author(s):  
Benoit Gallet ◽  
Michael Gowanlock
Keyword(s):  
Execution Time ◽  
Performance Metrics ◽  
Performance Model ◽  
Similarity Join ◽  
Load Imbalance ◽  
Test Platform ◽  
Static Partitioning ◽  
Different Characteristics ◽  
Static And Dynamic Work ◽  
Partitioning Strategy

Abstract Given two datasets (or tables) A and B and a search distance $$\epsilon$$ ϵ , the distance similarity join, denoted as $$A \ltimes _\epsilon B$$ A ⋉ ϵ B , finds the pairs of points ($$p_a$$ p a , $$p_b$$ p b ), where $$p_a \in A$$ p a ∈ A and $$p_b \in B$$ p b ∈ B , and such that the distance between $$p_a$$ p a and $$p_b$$ p b is $$\le \epsilon$$ ≤ ϵ . If $$A = B$$ A = B , then the similarity join is equivalent to a similarity self-join, denoted as $$A \bowtie _\epsilon A$$ A ⋈ ϵ A . We propose in this paper Heterogeneous Epsilon Grid Joins (HEGJoin), a heterogeneous CPU-GPU distance similarity join algorithm. Efficiently partitioning the work between the CPU and the GPU is a challenge. Indeed, the work partitioning strategy needs to consider the different characteristics and computational throughput of the processors (CPU and GPU), as well as the data-dependent nature of the similarity join that accounts in the overall execution time (e.g., the number of queries, their distribution, the dimensionality, etc.). In addition to HEGJoin, we design in this paper a dynamic and two static work partitioning strategies. We also propose a performance model for each static partitioning strategy to perform the distribution of the work between the processors. We evaluate the performance of all three partitioning methods by considering the execution time and the load imbalance between the CPU and GPU as performance metrics. HEGJoin achieves a speedup of up to $$5.46\times$$ 5.46 × ($$3.97\times$$ 3.97 × ) over the GPU-only (CPU-only) algorithms on our first test platform and up to $$1.97\times$$ 1.97 × ($$12.07\times$$ 12.07 × ) on our second test platform over the GPU-only (CPU-only) algorithms.

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