Accelerated bulk memory operations on heterogeneous multi-core systems

Abstract

A traditional fixed-function graphics accelerator has evolved into a programmable general-purpose graphics processing unit over the past few years, the general-purpose computing on GPU (GPGPU). Recently, revolutionary measures have been taken along this direction: an integrated GPU, i.e., CPUs and GPUs are integrated into the same package or even into the same die. However, considering a system-on-chip, the GPU takes up considerable silicon resources, but when running non-graphical workloads or non-GPGPU applications it is likely that overall system performance will not be affected. This paper presents a novel approach to accelerate conventional operations that are normally performed on CPUs, which are bulk memory operations such as memcpy or memcmp, using an integrated GPU. Offloading bulk memory operations to the GPU has many benefits: (i) The throughput GPU outperforms the CPU in bulk memory operations; (ii) for on-die GPUs with unified cache between the GPU and the CPU, the CPU can utilize the GPU private cache to store the moved data and reduce the CPU cache bottleneck; (iii) additional lightweight hardware can also support asynchronous offloads; and (iv) unlike the prior art using a dedicated hardware copy engine (e.g., DMA), our approach utilizes as much GPU hardware resources as possible. The performance results based on our solution showed that offloaded bulk memory operations outperform CPU up to 4.3 times faster on micro-benchmarks while using fewer resources. Using eight real-world applications and a cycle-based full-system simulation environment, five of eight applications showed about 30% speedup and two applications showed about 20% speedup.