A multi-tiered optimization framework for heterogeneous computing

2014 ◽  
Author(s):  
Andrew Milluzzi ◽  
Justin Richardson ◽  
Alan George ◽  
Herman Lam
Keyword(s):  
Heterogeneous Computing ◽  
Optimization Framework

MARCO: A High-performance Task M apping a nd R outing Co -optimization Framework for Point-to-Point NoC-based Heterogeneous Computing Systems

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-21
Author(s):  
Hui Chen ◽  
Zihao Zhang ◽  
Peng Chen ◽  
Xiangzhong Luo ◽  
Shiqing Li ◽  
...  
Keyword(s):  
Heterogeneous Computing ◽  
Design Space ◽  
State Of The Art ◽  
Computing Systems ◽  
Optimization Framework ◽  
Schedule Length ◽  
Processing Ability ◽  
Routing Design ◽  
Point To Point ◽  
Heterogeneous Computing Systems

Heterogeneous computing systems (HCSs), which consist of various processing elements (PEs) that vary in their processing ability, are usually facilitated by the network-on-chip (NoC) to interconnect its components. The emerging point-to-point NoCs which support single-cycle-multi-hop transmission, reduce or eliminate the latency dependence on distance, addressing the scalability concern raised by high latency for long-distance transmission and enlarging the design space of the routing algorithm to search the non-shortest paths. For such point-to-point NoC-based HCSs, resource management strategies which are managed by compilers, scheduler, or controllers, e.g., mapping and routing, are complicated for the following reasons: (i) Due to the heterogeneity, mapping and routing need to optimize computation and communication concurrently (for homogeneous computing systems, only communication). (ii) Conducting mapping and routing consecutively cannot minimize the schedule length in most cases since the PEs with high processing ability may locate in the crowded area and suffer from high resource contention overhead. (iii) Since changing the mapping selection of one task will reconstruct the whole routing design space, the exploration of mapping and routing design space is challenging. Therefore, in this work, we propose MARCO, the m apping a nd r outing co -optimization framework, to decrease the schedule length of applications on point-to-point NoC-based HCSs. Specifically, we revise the tabu search to explore the design space and evaluate the quality of mapping and routing. The advanced reinforcement learning (RL)algorithm, i.e., advantage actor-critic, is adopted to efficiently compute paths. We perform extensive experiments on various real applications, which demonstrates that the MARCO achieves a remarkable performance improvement in terms of schedule length (+44.94% ∼ +50.18%) when compared with the state-of-the-art mapping and routing co-optimization algorithm for homogeneous computing systems. We also compare MARCO with different combinations of state-of-the-art mapping and routing approaches.

scholarly journals Scheduling Challenges in Mixed Critical Real-Time Heterogeneous Computing Platforms

2013 ◽  
Vol 18 ◽  
pp. 1891-1898
Author(s):  
Chetan Kumar N G ◽  
Sudhanshu Vyas ◽  
Ron K. Cytron ◽  
Christopher D. Gill ◽  
Joseph Zambreno ◽  
...  
Keyword(s):  
Real Time ◽  
Heterogeneous Computing ◽  
Computing Platforms

scholarly journals Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman
Keyword(s):  
Real World ◽  
High Speed ◽  
Analytical Approximation ◽  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Quadrotor Aircraft ◽  
Time Optimal ◽  
Feasibility Constraints ◽  
Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

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