Parallel computing
Related papers: 20
About
Parallel computing refers to the simultaneous execution of multiple computational tasks across two or more processors or processing units, as opposed to sequential single-processor execution. In robotics and AI, parallel computing is used to accelerate computationally intensive workloads such as robot-arm control calculations, deep neural network inference, real-time semantic segmentation, path planning, and sensor processing. By distributing tasks across multiple processors — whether in multiprocessor systems, systolic arrays like the Warp machine, or specialized hardware accelerators — systems can meet strict real-time deadlines that single processors cannot satisfy alone. Scheduling algorithms determine how tasks are efficiently assigned across processors to minimize overall execution time. Parallel computing matters because modern robotics and AI applications demand enormous computational throughput: autonomous vehicles must process sensor data and run perception models in milliseconds, while training large neural networks requires handling billions of arithmetic operations efficiently. Without parallel computing architectures and intelligent scheduling strategies, the real-time performance and scalability required by contemporary intelligent systems would be fundamentally unachievable.
Top Researchers
Top Institutes
Top Cited Papers
Bounds on Multiprocessing Timing Anomalies
Ron Graham
Citations: 2363 • 1969
DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!
Citations: 771 • 2024
Parallel Robots
Jean‐Pierre Merlet
Citations: 705 • 2000
A genetic algorithm for multiprocessor scheduling
E.S.H. Hou, Nirwan Ansari, Hongliang Ren
Citations: 685 • 1994
Type Synthesis of Parallel Mechanisms
Xianwen Kong
Citations: 552 • 2007
SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training
Eric Qin, Ananda Samajdar, Hyoukjun Kwon, Vineet Nadella, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul, Tushar Krishna
Citations: 462 • 2020
Another efficient algorithm for convex hulls in two dimensions
Alex M. Andrew
Citations: 442 • 1979
Interconnection networks for large-scale parallel processing: theory and case studies (2nd ed.)
Howard Jay Siegel
Citations: 377 • 1985
Pheromone Robotics
David W. Payton, Mike Daily, Regina Estowski, Craig Lee
Citations: 334 • 2001
Description and Theoretical Analysis (Using Schemata) of Planner: A Language for Proving Theorems and Manipulating Models in a Robot
Carl Hewitt
Citations: 318 • 1972
The Warp Computer: Architecture, Implementation, and Performance
Marco Annaratone, E. Arnould, Thomas Groß, H. T. Kung, Monica S. Lam, O. Menzilcioglu, Jon A. Webb
Citations: 310 • 1987
Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation
Ching‐Chih Tsai, Hsu‐Chih Huang, Cheng-Kai Chan
Citations: 295 • 2011
ClearPath
Stephen J. Guy, Jatin Chhugani, Changkyu Kim, Nadathur Satish, Ming C. Lin, Dinesh Manocha, Pradeep Dubey
Citations: 291 • 2009
Time-Efficient Maze Routing Algorithms on Reconfigurable Mesh Architectures
Fikret Erçal, Hsi-Chieh Lee
Citations: 264 • 1997
Strategies to maximize heterologous protein expression in Escherichia coli with minimal cost
Wolfgang Peti, Rebecca Page
Citations: 227 • 2006
Entanglement-Based Machine Learning on a Quantum Computer
Xiang Cai, Dian Wu, Zu-En Su, M.-C. Chen, Xi‐Lin Wang, Li Li, N.-L. Liu, Chao‐Yang Lu, Jian-Wei Pan
Citations: 210 • 2015
Time‐decomposed parallel time‐integrators: theory and feasibility studies for fluid, structure, and fluid–structure applications
Charbel Farhat, Marion Chandesris
Citations: 206 • 2003
Parallel processing of robot-arm control computation on a multimicroprocessor system
Hironori Kasahara, S. Narita
Citations: 193 • 1985
Convolutional Neural Network for Trajectory Prediction
Nishant Nikhil, Brendan Morris
Citations: 183 • 2019
A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving
Mennatullah Siam, Mostafa Gamal, Moemen Abdel-Razek, Senthil Yogamani, Martin Jägersand, Hong Zhang
Citations: 178 • 2018