52,810 research outputs found
Gunrock: A High-Performance Graph Processing Library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs have been two
significant challenges for developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We evaluate Gunrock on five key graph
primitives and show that Gunrock has on average at least an order of magnitude
speedup over Boost and PowerGraph, comparable performance to the fastest GPU
hardwired primitives, and better performance than any other GPU high-level
graph library.Comment: 14 pages, accepted by PPoPP'16 (removed the text repetition in the
previous version v5
GraphH: High Performance Big Graph Analytics in Small Clusters
It is common for real-world applications to analyze big graphs using
distributed graph processing systems. Popular in-memory systems require an
enormous amount of resources to handle big graphs. While several out-of-core
approaches have been proposed for processing big graphs on disk, the high disk
I/O overhead could significantly reduce performance. In this paper, we propose
GraphH to enable high-performance big graph analytics in small clusters.
Specifically, we design a two-stage graph partition scheme to evenly divide the
input graph into partitions, and propose a GAB (Gather-Apply-Broadcast)
computation model to make each worker process a partition in memory at a time.
We use an edge cache mechanism to reduce the disk I/O overhead, and design a
hybrid strategy to improve the communication performance. GraphH can
efficiently process big graphs in small clusters or even a single commodity
server. Extensive evaluations have shown that GraphH could be up to 7.8x faster
compared to popular in-memory systems, such as Pregel+ and PowerGraph when
processing generic graphs, and more than 100x faster than recently proposed
out-of-core systems, such as GraphD and Chaos when processing big graphs
Gunrock: GPU Graph Analytics
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs, have presented two
significant challenges to developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We characterize the performance of
various optimization strategies and evaluate Gunrock's overall performance on
different GPU architectures on a wide range of graph primitives that span from
traversal-based algorithms and ranking algorithms, to triangle counting and
bipartite-graph-based algorithms. The results show that on a single GPU,
Gunrock has on average at least an order of magnitude speedup over Boost and
PowerGraph, comparable performance to the fastest GPU hardwired primitives and
CPU shared-memory graph libraries such as Ligra and Galois, and better
performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing
(TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance
Graph Processing Library on the GPU
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
From social networks to language modeling, the growing scale and importance
of graph data has driven the development of numerous new graph-parallel systems
(e.g., Pregel, GraphLab). By restricting the computation that can be expressed
and introducing new techniques to partition and distribute the graph, these
systems can efficiently execute iterative graph algorithms orders of magnitude
faster than more general data-parallel systems. However, the same restrictions
that enable the performance gains also make it difficult to express many of the
important stages in a typical graph-analytics pipeline: constructing the graph,
modifying its structure, or expressing computation that spans multiple graphs.
As a consequence, existing graph analytics pipelines compose graph-parallel and
data-parallel systems using external storage systems, leading to extensive data
movement and complicated programming model.
To address these challenges we introduce GraphX, a distributed graph
computation framework that unifies graph-parallel and data-parallel
computation. GraphX provides a small, core set of graph-parallel operators
expressive enough to implement the Pregel and PowerGraph abstractions, yet
simple enough to be cast in relational algebra. GraphX uses a collection of
query optimization techniques such as automatic join rewrites to efficiently
implement these graph-parallel operators. We evaluate GraphX on real-world
graphs and workloads and demonstrate that GraphX achieves comparable
performance as specialized graph computation systems, while outperforming them
in end-to-end graph pipelines. Moreover, GraphX achieves a balance between
expressiveness, performance, and ease of use
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