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This article is the first of a two-part series that presents two distinctly different approaches to parallel programming. In the two articles, I use different approaches to solve the same problem: finding the best-fitting line (or regression line) for a set of points.
The two different approaches to parallel programming presented in this and the following Insights article use these technologies:
Single-instruction multiple-thread (SIMT) programming is provided on the Nvidia® family of graphics processing units (GPUs). In SIMT programming, a single instruction is executed simultaneously on hundreds of microprocessors on a graphics card.
Single-instruction multiple data (SIMD) as provided on x64 processors from Intel® and AMD® (this article). In SIMD programming, a single instruction operates on wide registers that can contain vectors of numbers simultaneously.
The focus of this article is my attempt to exercise my computer’s Nvidia card using the GPU Computing Toolkit that Nvidia...
Parallel programming on an NVIDIA GPU involves using the parallel processing power of an NVIDIA graphics processing unit (GPU) to execute multiple tasks simultaneously. This allows for faster and more efficient computing compared to traditional serial processing.
In traditional serial programming, tasks are executed one after the other, while in parallel programming on an NVIDIA GPU, multiple tasks are executed simultaneously. This is achieved through the use of parallel processing techniques, such as dividing a task into smaller sub-tasks and assigning them to different processing cores on the GPU.
Parallel programming on an NVIDIA GPU can result in significant improvements in performance and efficiency, as the GPU is specifically designed for parallel processing tasks. It also allows for the handling of large amounts of data and complex computations in real-time, making it ideal for applications such as scientific simulations, machine learning, and data analytics.
NVIDIA provides a variety of programming languages and tools for parallel programming on their GPUs, including CUDA, OpenACC, and OpenCL. These languages are specifically designed for parallel computing and offer features such as memory management and data parallelism to optimize performance.
While parallel programming on an NVIDIA GPU may have a steeper learning curve compared to traditional serial programming, there are plenty of resources available, including tutorials and documentation provided by NVIDIA. Additionally, if you are already familiar with programming languages like C or C++, learning a language like CUDA can be relatively straightforward.