Emerging heterogeneous and homogeneous processing architectures demonstrate significant increases in throughput for scientific applications over traditional single core processors. Each of these processing architectures vary widely in their processing capabilities, memory hierarchies, and programming models. Determining the system architecture best suited to an application or deploying an application that is portable across a number of different platforms is increasingly complex and error prone within this rapidly increasing and evolving design space. Quickly and easily designing portable, high-performance applications that can function and maintain their correctness properly across these widely varied systems has become paramount. To deal with these programming challenges, there is a great need for new models and tools to be developed. One example is MIT Lincoln Laboratory’s Parallel Vector Tile Optimizing Library (PVTOL) which simplifies the task of developing software in C++ for these complex systems. This work extends the Tasks and Conduits framework in PVTOL to support GPU architectures and other heterogeneous platforms supported by the NVIDIA CUDA and OpenCL programming models. This allows the rapid portability of applications to a very wide range of architectures and clusters. Using this framework, porting applications from a single CPU core to a GPU requires a change of only 5 source lines of code (SLOC) in addition to the CUDA or OpenCL kernel. Using GPU-PVTOL we have achieved 22x speedup in an application of Monte Carlo simulations of photon propagation through a biological medium, and a 60x speedup of a 3D cone beam computed tomography (CT) image reconstruction algorithm.