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 ...

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, ...

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 ...

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 ...

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 ...

This is a schematic showing data parallelism vs. model parallelism, as they relate to neural network training. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases ...

Traditionally data acquisition has been the bottleneck for large scale proteomics. This has also remained one of the limitations in leveraging mass spectrometry within the clinic. PASEF and short ...