
The GPU Database Evolves Into An Analytics Platform
As the name of this publication suggests, we are system thinkers and we like to watch the evolution of a collection of tools into a platform. …
As the name of this publication suggests, we are system thinkers and we like to watch the evolution of a collection of tools into a platform. …
In the long run, provided there are enough API pipes into the code, software as a service might be the most popular way to consume applications and systems software for all but the largest organizations that are running at such a scale that they can command almost as good prices for components as the public cloud intermediaries. …
While it is always best to have the right tool for the job, it is better still if a tool can be used by multiple jobs and therefore have its utilization be higher than it might otherwise be. …
There is an arms race in the nascent market for GPU-accelerated databases, and the winner will be the one that can scale to the largest datasets while also providing the most compatibility with industry-standard SQL. …
It is not news that offloading work from CPUs to GPUs can grant radical speedups, but what can come as a surprise is that scaling of these workloads doesn’t change just because they run faster. …
Having made the improbable jump from the game console to the supercomputer, GPUs are now invading the datacenter. …
While GPUs are commonly used to accelerate massively parallel compute jobs that are behind simulations, media rendering, or machine learning algorithms, the next wave of growth could come from databases, thereby upsetting the balance of power in get another part of the datacenter infrastructure. …
In-memory databases are all the rage for very fast query processing, but you have to have the right balance of compute and memory for queries against in-memory databases to really scream. …
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