Name
Title | ||
|---|---|---|
Design and Experimental Evaluation of Distributed Heterogeneous Graph-Processing Systems |
Abstract | ||
|---|---|---|
Graph processing is increasingly used in a variety of domains, from engineering to logistics and from scientific computing to online gaming. To process graphs efficiently, GPU-enabled graph-processing systems such as TOTEM and Medusa exploit the GPU or the combined CPU+GPU capabilities of a single machine. Unlike scalable distributed CPU-based systems such as Pregel and GraphX, existing GPU-enabled systems are restricted to the resources of a single machine, including the limited amount of GPU memory, and thus cannot analyze the increasingly large-scale graphs we see in practice. To address this problem, we design and implement three families of distributed heterogeneous graph-processing systems that can use both the CPUs and GPUs of multiple machines. We further focus on graph partitioning, for which we compare existing graph-partitioning policies and a new policy specifically targeted at heterogeneity. We implement all our distributed heterogeneous systems based on the programming model of the single-machine TOTEM, to which we add (1) a new communication layer for CPUs and GPUs across multiple machines to support distributed graphs, and (2) a workload partitioning method that uses offline profiling to distribute the work on the CPUs and the GPUs. We conduct a comprehensive real-world performance evaluation for all three families. To ensure representative results, we select 3 typical algorithms and 5 datasets with different characteristics. Our results include algorithm run time, performance breakdown, scalability, graph partitioning time, and comparison with other graph-processing systems. They demonstrate the feasibility of distributed heterogeneous graph processing and show evidence of the high performance that can be achieved by combining CPUs and GPUs in a distributed environment. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CCGrid.2016.53 | 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) |
Keywords | Field | DocType |
Graph Processing,Distributed Heterogeneous Systems | Graph,Distributed Computing Environment,Programming paradigm,Profiling (computer programming),Computer science,Workload,Parallel computing,Exploit,Graph partition,Distributed computing,Scalability | Conference |
ISSN | ISBN | Citations |
2376-4414 | 978-1-5090-2454-4 | 1 |
PageRank | References | Authors |
0.35 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Guo | 1 | 95 | 4.60 |
| Ana Lucia Varbanescu | 2 | 520 | 44.83 |
| Dick H. J. Epema | 3 | 3134 | 180.80 |
| Alexandru Iosup | 4 | 2042 | 125.89 |