CHAPTER Thrust: A Productivity-Oriented Library for CUDA 26

This chapter demonstrates how to leverage the Thrust parallel template library to implement high-performance applications with minimal programming effort. Based on the C++ Standard Template Library (STL), Thrust brings a familiar high-level interface to the realm of GPU Computing while remaining fully interoperable with the rest of the CUDA software ecosystem. Applications written with Thrust are concise, readable, and efficient. 26.1 MOTIVATION With the introduction of CUDA C/C++, developers can harness the massive parallelism of the GPU through a standard programming language. CUDA allows developers to make fine-grained decisions about how computations are decomposed into parallel threads and executed on the device. The level of control offered by CUDA C/C++ (henceforth CUDA C) is an important feature: it facilitates the development of high-performance algorithms for a variety of computationally demanding tasks which (1) merit significant optimization and (2) profit from low-level control of the mapping onto hardware. For this class of computational tasks CUDA C is an excellent solution. Thrust [1] solves a complementary set of problems, namely those that are (1) implemented effi-ciently without a detailed mapping of work onto the target architecture or those that (2) do not meri