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. …
So you are a system architect, and you want to make the databases behind your applications run a lot faster. …
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. …
When supercomputer maker SGI tweaked its NUMA server technology to try to pursue sales in the datacenter, the plan was not to go it alone but rather to partner with the makers of workhorse Xeon servers that did not – and would not – make their own big iron but who nonetheless want to sell high-end machines to their customers. …
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. …
For the past several years, SGI has been extending itself beyond its traditional supercomputing customer base out into the broader enterprise at large. …
The Spark in-memory processing framework that came out of the AMPLab at the University of California at Berkeley is hotter than hot. …
Here’s an image for you. There is no such thing as a data lake. …
There is a disconnect between the database engines that underpin both relational databases and the SQL front ends that have been grafted onto Hadoop analytics tools and the underlying hardware on which these databases run. …
The engines at the heart of relational database engines are broken, and Deep Information Sciences, which is uncloaking from stealth mode today, thinks it has come up with a way to fix them. …
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