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

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

Whether modifying an existing application or writing entirely new code, parallel applications can be much more challenging to work with than their sequential counterparts. Without a doubt, the ...

Hi, I’m James Reinders, and today we’re going to talk about threading building blocks. In fact, this is part one of a look at threading building blocks, which is an interesting template library for ...

Dr. Guy Blelloch of Carnegie Mellon University has written an article for the folks at CilkArts analyzing why parallel programming seems to be more difficult than sequential programming. He quickly ...

This week is the eighth annual International Workshop on OpenCL, SYCL, Vulkan, and SPIR-V, and the event is available online for the very first time in its history thanks to the coronavirus pandemic.

In high performance computing, machine learning, and a growing set of other application areas, accelerated, heterogeneous systems are becoming the norm. With that state come several parallel ...

A hands-on introduction to parallel programming and optimizations for 1000+ core GPU processors, their architecture, the CUDA programming model, and performance analysis. Students implement various ...

I'm wondering if anyone has any recommendations for good resources to learn parallel/concurrent/multicore programming. I know this is a pretty damn vague question, so bear with me. I've been a ...

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 ...