Can AI fine-tuning that once took 10 hours be completed in just three hours? KAIST researchers have developed a technology that selects the most useful training data and distributes it evenly across ...

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, ...

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 ...

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 ...

New data technologies are continually arriving these days. Among the latest entrants are GPU databases. Often associated with graph data processing tasks, these databases tap into the power of the GPU ...

Multi-core processors theoretically can run many threads of code in parallel, but some categories of operation currently bog down attempts to raise overall performance by parallelizing computing. Is ...

Detecting traffic with one sensor emitting traffic events is an acceptable way to collect the traffic data when there's a single-lane bridge. Tim is a true believer of new technologies and interested ...

Achieving autonomous driving safely requires near endless hours of training software on every situation that could possibly arise before putting a vehicle on the road. Historically, autonomy companies ...

Open source data integration vendor Talend is planning to release a master data management product by the end of the year, as well as to offer a massively parallel processing architecture in current ...

Digital parallel and serial converters adapt data transmitted in a parallel port to a serial communication format, or adapt serial port data to parallel communication format. Data converters are ...