The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus. These cores have shared resources including a register file and a shared memory. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.ĬUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. The CPU and GPU are treated as separate devices that have their own memory spaces.

As such, CUDA can be incrementally applied to existing applications. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. Support heterogeneous computation where applications use both the CPU and GPU. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation. Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).ĬUDA was developed with several design goals in mind: Introduction ĬUDA ® is a parallel computing platform and programming model invented by NVIDIA ®. The installation instructions for the CUDA Toolkit on Linux.

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