Modern software takes computational speed for granted. But modern microprocessors can only speed up by increasing the number of cores. To take full advantage of multiple cores, software developers ...

Recently, I had the good fortune to present a class at the ACM Conference for Computer Science Educators (SIGCSE). While I definitely shared my enthusiasm for parallel programming, I had two key goals ...

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

For example, an engineer may develop a real-time embedded control system at the same time as its human-machine interface. Maybe the system also has a computation-intensive task such as high-speed ...

Modern personal computing devices feature multiple cores. This is not only true for desktops, laptops, tablets and smartphones, but also for small embedded devices like the Raspberry Pi. In order to ...

NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing ...

As modern .NET applications grow increasingly reliant on concurrency to deliver responsive, scalable experiences, mastering asynchronous and parallel programming has become essential for every serious ...

Programming languages are evolving to bring the software closer to hardware. As hardware architectures become more parallel (with the advent of multicore processors and FPGAs, for example), sequential ...

One thing DDS systems do better than most parallel-programming environments is handle transient connections, because they support best-effort delivery. In many applications, it’s sufficient to retain ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...