CUDA lets you work with familiar programming concepts while developing software that can run on a GP This is the first of a series of articles to introduce you to the power of CUDA -- through working code -- and to the thought process to help you map applications onto multi-threaded hardware (such as GPUs) to get big performance increases. Of course, not all problems can be mapped efficiently onto multi-threaded hardware, so part of my thought process will be to distinguish what will and what won't work, plus provide a common-sense idea of what might work "well-enough". "CUDA programming" and "GPGPU programming" are not the same (although CUDA runs on GPUs). CUDA permits working with familiar programming concepts while developing software that can run on a GPU. It also avoids the performance overhead of graphics layer APIs by compiling your software directly to the hardware (GPU assembly language, for instance), thereby providing great performance.
The process of writing large parallel programs is complicated by the need to specify both the parallel behaviour of the program and the algorithm that is to be used to compute its result.
In an earlier post Over on the Twisted blog, Duncan McGreggor has asked us to expand a bit on where we think Twisted may be lacking in it’s support for concurrency. I’m afraid this has turned into a meandering essay, since I needed to reference so muc
I participated in the design and development of a couple of concurrency libraries for shared-memory multiprocessors long before such machines were popular. So when I started using java.util.concurrent I was already somewhat comfortable with the concepts.
The C++ standardization committee is hard at work standardizing threads for the next version of C++. Some members recently met to discuss the issues, and The C++ Source was there. Read on to learn what the world’s leading experts on concurrency are plan