Efficient Mapping of Irregular C++ Applications to Integrated GPUs

There is growing interest in using GPUs to accelerate general-purpose computation since they offer the potential of massive parallelism with reduced energy consumption. This interest has been encouraged by the ubiquity of integrated processors that combine a GPU and CPU on the same die, lowering the cost of offloading work to the GPU. However, while the majority of effort has focused on GPU acceleration of regular applications, relatively little is known about the behavior of irregular applications on GPUs. These applications are expected to perform poorly on GPUs without major software engineering effort. We present a compiler framework with support for C++ features that enables GPU acceleration of a wide range of C++ applications with minimal changes. This framework, Concord, includes a low-cost, software SVM implementation that permits seamless sharing of pointer-containing data structures between the CPU and GPU. It also includes compiler optimizations to improve irregular application performance on GPUs. Using Concord, we ran nine irregular C++ programs on two computer systems containing Intel 4th Generation Core processors. One system is an Ultrabook with an integrated HD Graphics 5000 GPU, and the other system is a desktop with an integrated HD Graphics 4600 GPU. The nine applications are pointer-intensive and operate on irregular data structures such as trees and graphs; they include face detection, BTree, single-source shortest path, soft-body physics simulation, and breadth-first search. Our results show that Concord acceleration using the GPU improves energy efficiency by up to 6.04× on the Ultrabook and 3.52× on the desktop over multicore-CPU execution.