Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accelerating Direction-Optimized Breadth First Search on Hybrid
  Architectures   Large scale-free graphs are famously difficult to process efficiently: the
skewed vertex degree distribution makes it difficult to obtain balanced
partitioning. Our research instead aims to turn this into an advantage by
partitioning the workload to match the strength of the individual computing
elements in a Hybrid, GPU-accelerated architecture. As a proof of concept we
focus on the direction-optimized breadth first search algorithm. We present the
key graph partitioning, workload allocation, and communication strategies
required for massive concurrency and good overall performance. We show that
exploiting specialization enables gains as high as 2.4x in terms of
time-to-solution and 2.0x in terms of energy efficiency by adding 2 GPUs to a 2
CPU-only baseline, for synthetic graphs with up to 16 Billion undirected edges
as well as for large real-world graphs. We also show that, for a capped energy
envelope, it is more efficient to add a GPU than an additional CPU. Finally,
our performance would place us at the top of today's [Green]Graph500 challenges
for Scale29 graphs.

23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn