Concurrent programming is a way of designing and writing programs that can execute multiple tasks or processes at the same time, without waiting for one to finish before starting another.

Parallel programming is a powerful way to speed up your applications, harness the power of multiple processors, and solve complex problems. However, it also comes with its own challenges, such as ...

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 in Python using processes and threads is explored, analyzing their performance in I/O-bound and CPU-bound tasks, with insights into ... This means that in a multi-threaded Python ...

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 ...

CPU-GPU based cluster computing in today's modern world encompasses the domain of complex and high-intensity computation. To exploit the efficient resource utilization of a cluster, traditional ...

The new pseudo-code is implemented as a kernel. A kernel is a function that executes on several GPU cores at the same time. Kernels are launched by a host program controlled from the regular CPU that ...

5 problem sets of parallel programming on CPU and GPU. University projects for High Performance Computing Systems (Fall 2016). - pnikitakis/high-performance-computing ...

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… ...