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CUDACasts Episode 15: Introduction to Thrust

Whenever I hear about a developer interested in accelerating his or her C++ application on a GPU, I make sure to tell them about Thrust. Thrust is a parallel algorithms library loosely based on the C++ Standard Template Library. Thrust provides a number of building blocks, such as sort, scans, transforms, and reductions, to enable developers to quickly embrace the power of parallel computing.  In addition to targeting the massive parallelism of NVIDIA GPUs, Thrust supports multiple system back-ends such as OpenMP and Intel’s Threading Building Blocks. This means that it’s possible to compile your code for different parallel processors with a simple flick of a compiler switch.

For this first in a mini-series of screencasts about Thrust, we’ll write a simple sorting program and execute it on both a GPU and a multi-core CPU.  In upcoming episodes, we’ll explore more capabilities of Thrust which really show its flexibility and power. For more examples of using Thrust, read the post Expressive Algorithmic Programming with Thrust, and check out the Thrust Quick Start Guide.

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About Mark Ebersole

As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a low-level systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked for IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University. Follow @cudahamster on Twitter