The graphics processing unit (GPU) of modern computers has evolved into a powerful, general-purpose, massively parallel numerical (co-)processor. The numerical computation in a number of machine learning algorithms fits well on the GPU. To help identify such algorithms, we present uniformly fine-grained data-parallel computing and illustrate it on two machine learning algorithms, clustering and regression clustering, on a GPU and central processing unit (CPU) mixed computing architecture. We discuss the key issues involved in a successful design of the algorithms, data structures, and computation partitioning between a CPU and a GPU. Performance gains on a CPU and GPU mixed architecture are compared with the performance of the regression clustering algorithm implemented completely on a CPU. Significant speedups are reported. A GPU and CPU mixed architecture also achieves better cost-performance and energy-performance ratios.

The computing power of the CPU has increased dramatically in the past few decades, supported by both miniaturization and increasing clock frequencies. More and more electronic gates were packed onto the same area of a silicon die as miniaturization continued. Hardware-supported parallel computing, pipelining for example, further increased the computing power of CPUs. Frequency increases speeded up CPUs even more directly. However, the long-predicted physical limit of the miniaturization process was finally hit a few years ago such that increasing the frequency was no longer feasible due to the accompanied nonlinear increase in power consumption, even though miniaturization still continues.