Data parallelism is an approach towards parallel processing that depends on being able to break up data between multiple compute units (which could be cores in a processor, processors in a computer, ...

A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market. With $31 million in venture funding and ...

Intel director James Reinders explains the difference between task and data parallelism, and how there is a way around the limits imposed by Amdahl's Law... I'm James Reinders, and I'm going to cover ...

As hardware designers turn toward multicore processors to improve computing power, software programmers must find new programming strategies that harness the power of parallel computing. One technique ...

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...

The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized ...

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 ...

Citus Data has launched CitusDB for Hadoop, a service that can process petabytes of data within seconds. The offering shows once again that the new class of analytics databases that can analyze ...

When I wrote about password guessing using GPUs last week, I mentioned that password guessing is an embarrassingly parallel problem, right up there with 3-D rendering, face recognition, Monte Carlo ...

I’m James Reinders, and I’m going to cover to key concepts involved with parallelism today. They are terms that you’ll hear when you start working with parallel programming, when you start looking at ...