Is CUDA or OpenCL faster?

A study that directly compared CUDA programs with OpenCL on NVIDIA GPUs showed that CUDA was 30\% faster than OpenCL. OpenCL is rarely used for machine learning. As a result, the community is small, with few libraries and tutorials available.

What do you use CUDA or OpenCL for?

The general consensus is that if your app of choice supports both CUDA and OpenCL, go with CUDA as it will generate better performance results. The main reason for this is that Nvidia provide top quality support to app developers who choose to use CUDA acceleration, therefore the integration is always fantastic.

Where is CUDA used?

CUDA-compatible GPUs are available every way that you might use compute power: notebooks, workstations, data centers, or clouds. Most laptops come with the option of NVIDIA GPUs. NVIDIA enterprise-class GPUs Tesla and Quadro—widely used in datacenter and workstations—are also CUDA-compatible.

Where is OpenCL used?

OpenCL makes it possible to write and deploy applications that use multiple types of devices for processing. They include not just GPUs, but also CPUs (which, again, can be used effectively in conjunction with OpenCL in certain scenarios), Digital Signal Processors (DSPs) and other types of hardware accelerators.

What is CUDA programming good for?

Compute unified device architecture (CUDA) programming enables you to leverage parallel computing technologies developed by NVIDIA. The CUDA platform and application programming interface (API) are particularly helpful for implementing general purpose computing on graphics processing units (GPU).

How much faster is CUDA compared to OpenCL?

The CUDA-based simulation speed is about 2x to 5x faster than the OpenCL-based simulation, except GTX 1050Ti, which is 1-to-1. From other papers comparing CUDA and OpenCL, the speed difference found in our study is quite high.

Is OpenCL faster on a GPU than a CPU?

For example, combinational logic is much faster on AMD GPUs. Not all of above algorithms work best on a GPU by definition: OpenCL on a CPU can be a good choice when memory-bandwidth is not the bottleneck (a CPU does ~30GB/s, a GPU up to ~300GB/s).

How do I compile a CUDA application on a 64 bit platform?

CUDA runtime applications compile the kernel code to have the same bitness as the application. On a 64-bit platform try compiling the CUDA application as a 32-bit application. Your use of double has nothing to do with the bitness of the application or kernel code.

What is Cuda and how does it work?

CUDA which stands for Compute Unified Device Architecture, is a parallel programming paradigm which was released in 2007 by NVIDIA. CUDA while using a language which is similar to the C language is used to develop software for graphic processors and a vast array of general-purpose applications for GPU’s which are highly parallel in nature.