In a world with Big Data and diverse workloads, the need for compute seems endless — and now with the influence of AI through new and merged workloads, the need for compute may prove to be endless. The hunger for compute has spurred an onslaught of product competing to help accelerate our workloads – all helping usher in the era of Heterogenous computing. In this new world, it’s not just CPUs or GPUs we have to program, but many devices. There are many reasons for programmers to be concerned.
In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance penalties on matrix operations, can end up costing both performance and memory space.
James Reinders James Reinders likes fast computers and the software tools to make them speedy. With over 30 years in high-performance computing (HPC) and parallel computing, including 27 Years at Intel Corporation (retired June 2016), he is also the author of nine books in the HPC field, numerous papers and blogs.
Last call (July 19 deadline): IXPUG call for presentations - (conference in Geneva, Sept 24-27)
IXPUG is soliciting submissions for technical presentations on innovative work...