P86: HyGraph: High Performance Graph Processing on Hybrid
CPU+GPUs platforms
SessionPoster Reception
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionGraph analytics is becoming increasingly important in
many domains, such as in biology, social sciences, and
data mining. Many large-scale graph-processing systems
have been proposed, either targeting distributed
clusters or GPU-based accelerated platforms. However,
little research exists on designing systems for hybrid
CPU-GPU platforms, i.e., exploiting both the CPU and the
GPU efficiently.
In this work, we present HyGraph, a novel graph-processing system for hybrid platforms which delivers performance by using both the CPU and GPUs concurrently. Its core feature is dynamic scheduling of tasks onto both the CPU and the GPUs, thus providing load balancing, contrary to the state-of-the-art approach based on static partitioning. Additionally, communication overhead is minimized by overlapping computation and communication.
Our results demonstrate that HyGraph outperforms CPU-only and GPU-only solutions, delivering close-to-optimal performance. Moreover, it supports large-scale graphs which do not fit into GPU memory and is competitive against state-of-the-art systems.
In this work, we present HyGraph, a novel graph-processing system for hybrid platforms which delivers performance by using both the CPU and GPUs concurrently. Its core feature is dynamic scheduling of tasks onto both the CPU and the GPUs, thus providing load balancing, contrary to the state-of-the-art approach based on static partitioning. Additionally, communication overhead is minimized by overlapping computation and communication.
Our results demonstrate that HyGraph outperforms CPU-only and GPU-only solutions, delivering close-to-optimal performance. Moreover, it supports large-scale graphs which do not fit into GPU memory and is competitive against state-of-the-art systems.
