SQream Uses GPUs to Blast Through Big Data 10-Times Faster Than CPUsAugust 21, 2013
Catch SQream and other early-stage GPU innovators this November in Tel Aviv, where we’ll host top execs from dozens of firms using GPUs to push the frontiers of computing.
The trouble with Big Data is it’s so big.
Piling up terabytes of data is easier than ever as the world grows more digital, and those digital services grow ever more connected.
The problem: all too often finding useful information takes racks full of servers. And it takes time.
An Israeli startup, SQreamTechnologies, wants to use GPUs to make the process of sifting through all those terabytes to find something useful faster and more efficient.
And while it may not be intuitive to use a technology developed for video games to crank through phone records or bank transactions, SQream CEO Ami Gal has been noodling around with the idea for more than a decade.
That’s because the parallel computing technology used by GPUs to render lush worlds and fast-paced action is also ideal for cranking through a huge number of problems simultaneously.
Gal, a serial entrepreneur with a knack for out-of-the-box thinking, gave the idea a shot more than a decade ago, in 1997, when he tried to use predecessor of GPUs to accelerate call center apps. It worked, but he couldn’t make it work quickly enough to make a difference.
Since then, GPUs have grown into the parallel processing powerhouses first Gal envisioned. Gal found that out first hand when he ran into Kostya Varkin, who was using the latest NVIDIA GPUs to tear through SQL analytics quickly and efficiently. Gal was so impressed with Varkin’s progress that he joined the company in 2010 as CEO.
The early-stage startup – based in Ramat Gan, near Tel Aviv – has fewer than 20 employees, and just a handful of major pilot customers. But Gal is confident the time is finally right.
The company’s technology can already crank data 10-times faster than with a CPU along, using a skinny server rather than an entire rack full of power-sucking machines, Gal says.
He’s expanding aggressively, looking for developers who know their way around CUDA and GPUs to add to the team. With data piling up all around us at an ever faster rate, Gal is confident he’s found an idea whose time has come.