On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing

The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers between the CPU and GPU over PCIe. Emerging heterogeneous computing architectures that "fuse" the functionality of the CPU and GPU, e.g., AMD Fusion and Intel Knights Ferry, hold the promise of addressing the PCIe bottleneck. In this paper, we empirically characterize and analyze the efficacy of AMD Fusion, an architecture that combines general-purposex86 cores and programmable accelerator cores on the same silicon die. We characterize its performance via a set of micro-benchmarks (e.g., PCIe data transfer), kernel benchmarks(e.g., reduction), and actual applications (e.g., molecular dynamics). Depending on the benchmark, our results show that Fusion produces a 1.7 to 6.0-fold improvement in the data-transfer time, when compared to a discrete GPU. In turn, this improvement in data-transfer performance can significantly enhance application performance. For example, running a reduction benchmark on AMD Fusion with its mere 80 GPU cores improves performance by 3.5-fold over the discrete AMD Radeon HD 5870 GPU with its 1600 more powerful GPU cores.