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 ...

Parallel programming exploits the capabilities of multicore systems by dividing computational tasks into concurrently executed subtasks. This approach is fundamental to maximising performance and ...

In a lot of our programs, we run only one additional thing at a time and wait for it to complete; this is usually referred to with the more generic term "asynchronous programming." Parallel ...

Both parallel programming and asynchronous programming are examples of concurrent programming, meaning more than one operation is running at the same time. Parallel programming is a more specific form ...

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 ...

One of the best features of using FPGAs for a design is the inherent parallelism. Sure, you can write software to take advantage of multiple CPUs. But with an FPGA you can enjoy massive parallelism… ...

The text assumes that readers already have an understanding of basic computer science principles (how to write a correct sequential program, the basics of algorithmic complexity, and the like), and ...

Nvidia has unveiled a new compiler source code to add new languages to its parallel programming and boost the adoption of GPUs.

Programming tools that harness the computing power of CPUs and graphics processors have been updated, bringing more parallel programming capabilities to the table. Standards-setting firm Khronos ...