The history of computing teaches us that software always and necessarily lags hardware, and unfortunately that lag can stretch for many years when it comes to wringing the best performance out of iron by tweaking algorithms. …
In the absence of hard numbers, we have never been shy about making estimates because educated guesses is all you sometimes have to make a decision or to derive some kind of insight. …
After a decade and a half of ceaseless and focused work, Nvidia has created a modern compute platform, and a unique one at that. …
One of the first tenets of machine learning, which is a very precise kind of data analytics and statistical analysis, is that more data beats a better algorithm every time. …
There is no denying that GPUs have incredible potential to accelerate workloads of all kinds, but developing applications that can scale across two, four, or even more GPUs continues to be a prohibitively expensive proposition. …
The modern GPU compute engine is a microcosm of the high performance computing datacenter at large. …
At this point in supercomputing, it’s becoming an anomaly to see an upcoming double-digit petaflops system not using AMD for CPU and GPU, but the National Renewable Energy Laboratory will be taking a more traditional route for the “Kestrel” machine. …
Sponsored When it comes to compute engines and network interconnects for supercomputers, there are lots of different choices available, but ultimately the nature of the applications — and how they evolve over time — will drive the technology choices that organizations make. …
It is a good thing for Nvidia that most of the hyperscalers in the world – or at least the ones that matter – also have substantial public cloud businesses. …
If the world was not a complex place, and if all machine learning training looked more or less the same, then there would only be one accelerator to goose training workloads. …
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