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 ...

This is a schematic showing data parallelism vs. model parallelism, as they relate to neural network training. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases ...

Intel director James Reinders explains the difference between task and data parallelism, and how there is a way around the limits imposed by Amdahl's Law... I'm James Reinders, and I'm going to cover ...

In the task-parallel model represented by OpenMP, the user specifies the distribution of iterations among processors and then the data travels to the computations. In data-parallel programming, the ...

As hardware designers turn toward multicore processors to improve computing power, software programmers must find new programming strategies that harness the power of parallel computing. One technique ...

In this slidecast, Torsten Hoefler from ETH Zurich presents: Data-Centric Parallel Programming. The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the ...

Achieving autonomous driving safely requires near endless hours of training software on every situation that could possibly arise before putting a vehicle on the road. Historically, autonomy companies ...