Support for unified memory across CPUs and GPUs in accelerated computing systems is the final piece of a programming puzzle that we have been assembling for about ten years now. …
For exascale hardware to be useful, systems software is going to have to be stacked up and optimized to bend that hardware to the will of applications. …
I have been frequently asked when the OpenMP and OpenACC directive APIs for parallel programming will merge, or when will one of them (usually OpenMP) will replace the other. …
To effectively make use of the level of concurrency in forthcoming exascale systems – hundreds of thousands of compute elements with millions of threads – requires some new thinking, both by programmers and in development tools. …
Every important benchmark needs to start somewhere.
The first round of MLperf results are in and while they might not deliver on what we would have expected in terms of processor diversity and a complete view into scalability and performance, they do shed light on some developments that go beyond sheer hardware when it comes to deep learning training. …
Barefoot Networks is on a mission, and it is a simple one: To give datacenter switches the same kind of openness and programmability that X86 servers have enjoyed for decades in the datacenter. …
OpenMP is probably the most popular tool in the world to parallelize applications running on processors, but ironically it is not a product, but rather a specification that those who make compilers and middleware use to implement their own ways of parallelizing code to run on multicore processors and now, GPU accelerators. …
OpenACC is one of the prongs in a multi-prong strategy to get people to port the parallel portions of HPC applications to accelerators. …
If you want to understand where we are going with computer architectures and the compilers that drive them, it is instructive to look at how compilers have made the leap from architecture to architecture starting six decades ago. …
On the hardware side, the next frontier for deep learning innovation will be in getting the performance, efficiency, and accuracy needed for inference at scale. …
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