Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...
Victor Eijkhout: I see several problems with the state of parallel programming. For starters, we have too many different programming models, such as threading, message passing, and SIMD or SIMT ...
Stanford University, in conjunction with Sun, AMD, NVIDIA, IBM, Intel, and HP, is working to create a new computing model that fully exploits modern, multicore processing. As a feature in Ars Technica ...
Liang Zhao, Assistant Professor, Information Sciences and Technology, and Yue Cheng, Associate Professor, Computer Science, Volgenau School of Engineering, are set to receive funding from the National ...
Two Google Fellows just published a paper in the latest issue of Communications of the ACM about MapReduce, the parallel programming model used to process more than 20 petabytes of data every day on ...
Researchers at two U.S. universities have answered the age-old question of how to use burnt pancakes and E. coli to create more-efficient sorting algorithms. The researchers’ study was conducted by ...
Considering the real-time control of a high-speed parallel robot, a concise and precise dynamics model is essential for the design of the dynamics controller. However, the complete rigid-body dynamics ...
The Integrative Model for Parallelism at TACC is a new development in parallel programming. It allows for high level expression of parallel algorithms, giving efficient execution in multiple ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback