The second 5 percent chunk of server shipment market share for chip maker AMD is probably going to come easier than the first 5 percent share, which was attained as AMD exited the fourth quarter and, significantly, was a stake in the ground that chief executive officer Lisa Su drove into the ground in late 2016 as the “Naples” Epyc ramp got under way. …
This story has been temporarily removed. If you want to learn more about Inspur’s machine learning hardware, check it out here. …
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. …
Sales of various kinds of high performance computing – not just technical simulation and modeling applications, but also cryptocurrency mining, massively multiplayer gaming, video rendering, visualization, machine learning, and data analytics – run on little boom-bust cycles that make it difficult for all suppliers to this market to make projections when they look ahead. …
The twice-annual ranking of distributed computing systems based on the Linpack parallel Fortran benchmark, a widely used and sometimes maligned test, is as much a history lesson as it is an expectation always looking forward, with anticipation, to the next performance milestones in high performance computing. …
Many programs have a tough time spanning across high levels of concurrency, but if they are cleverly coded, databases can make great use of massively parallel compute based in hardware to radically speed up the time it takes to run complex queries against large datasets. …
When it comes to machine learning, a lot of the attention in the past six years has focused on the training of neural networks and how the GPU accelerator radically improved the accuracy of networks, thanks to its large memory bandwidth and parallel compute capacity relative to CPUs. …
Moore’s Law is effectively boosting compute capability by a factor of ten over a five year span, as Nvidia co-founder and chief executive officer Jensen Huang reminded Wall Street this week when talking about the graphics chip maker’s second quarter of fiscal 2019 financial results. …
To a certain extent, the “Knights” family of parallel processors, sold under the brand name Xeon Phi, by Intel were exactly what they were supposed to be: A non-mainstream product that tried out a different architecture than its mainstream Xeon family of server processors and that was aimed at the high performance computing jet set that is, by definition, supposed to take risks on new architectures. …
Even if Nvidia had not pursued a GPU compute strategy in the datacenter a decade and a half ago, the company would have turned in one of the best periods in its history as the first quarter of fiscal 2019 came to a close on April 29. …
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