“In Deep Learning what we do is try to minimize the amount of hand engineering and get the neural nets to learn, more or less, everything. Instead of programing computers to do particular tasks, you program the computer to know how to learn. And then you can give it any old task, and the more data and the more computation you provide, the better it will get.”
“This talk will introduce these three debugging techniques and provide some suggestions on selecting the optimal approach for a variety of debugging scenarios such as hangs, numerical errors, and crashes. Specific examples will be given using the TotalView debugger but the concepts covered may apply to other debugging tools such as GDB and the NVIDIA NSIGHT debugger.”
Today ArrayFire announced the release of Version 3.0 of their high-speed software library for GPU computing. The new version features major changes to ArrayFire’s visualization library, a new CPU backend, and dense linear algebra for OpenCL devices. It also includes improvements across the board for ArrayFire’s OpenCL backend.
A new paper from ORNL’s Sparsh Mittal and Jeffrey Vetter seeks to change the mindset of researchers using GPUs. Entitled, “A Survey of CPU-GPU Heterogeneous Computing Techniques,” the paper contends that merely offloading computational tasks to GPUs is not optimal, instead, using both CPU and GPU can lead to potentially higher speedup.